a Sic,@sdZddlZddlZddlZddlZddlmmZddl m Z ddl m Z ddl mZdZdZdd Zd d Zd d ZddZddZddZdS)z>Keras python-based idempotent saving functions (experimental).N) json_utils) generic_utils) tf_exportz config.kerasFcCsXtj|t}tjj|d}|}Wdn1s<0Yt |}t |S)zLoad a saved python model.rN) ospathjoin _CONFIG_FILEtfiogfileGFilereadrdecodedeserialize_keras_object)dirpath file_pathf config_json config_dictr`/var/www/html/django/DPS/env/lib/python3.9/site-packages/keras/saving/experimental/saving_lib.pyload#s & rcCstjj|stjj|tj|t}t |}t j |t j d}tjj|d}||Wdn1sr0YdS)zSave a saved python model.)clswN)r r r existsmkdirrrrr serialize_keras_objectjsondumpsrEncoderr write)modelrrZserialized_model_dictrrrrrsave,sr#c Cs\|d}|d}|d}|d}|dkr|dkrNt|tsJtd|d|S|d krt|}|d url|S|dd }zt|}Wn<ty}z$td |d |d|WYd }~n d }~00t ||d Std|t|}|d ur| |S|d urt |} nt|} t | |d } t | dsNtd| d| |Sd S)aRetrieve 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. For classes that have an exported Keras namespace, this is the full path that starts with "keras", such as "keras.optimizers.Adam". For classes that do not have an exported Keras namespace, 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`. For classes that have an exported Keras namespace, this is `None` since the class can be fully identified by the full Keras path. - `registered_name`: String. The key the class is registered under via `keras.utils.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 defaults to the class name. If `registered_name` successfully resolves to a class (that was registered), `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. Note that for built-in (exported symbols that have an exported Keras namespace) classes, the library tracks the class by the the import location of the built-in object in the Keras namespace, e.g. `"keras.optimizers.Adam"`, and this information is stored in `class_name`: ``` dict_structure = { "class_name": "keras.optimizers.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": null, "registered_name": "Adam" } # 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.utils.generic_utils.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" } # Gives `ModifiedMeanSquaredError` object deserialize_keras_object(dict_structure) ``` Args: config_dict: the python dict structure to deserialize the Keras object from. Returns: The Keras object that is deserialized from `config_dict`. class_nameconfigmoduleregistered_namebuiltinsstrz7Config of string is supposed to be a string. Received: .functionNzThe function module z5 is not available. The config dictionary provided is function_namezUnrecognized type: from_configz%Unable to reconstruct an instance of ) isinstancer) TypeErrorrget_custom_objects_by_nameget importlib import_module ImportErrorvarsr-rget_symbol_from_namehasattr) rr$r%r&r'Zcustom_functionfunction_moduleeZ custom_classrmodrrrr@sTg         rcCsBd}tj|jdd}|dur,|jj}|jj}||t|t|dS)aRetrieve the config dict by serializing the Keras object. `serialize_keras_object()` serializes a Keras object to a python dictionary that represents the object, and is a reciprocal function of `deserialize_keras_object()`. See `deserialize_keras_object()` for more information about the config format. Args: obj: the Keras object to serialize. Returns: A python dict that represents the object. The python dict can be deserialized via `deserialize_keras_object()`. Nkeras)api_name)r&r$r%r')rget_canonical_name_for_symbol __class__ __module____name___get_object_config_get_object_registered_name)objr&r$rrrrsrcCs&t|tjrt|St|jSdS)N)r.types FunctionTyperget_registered_namer>rCrrrrB s  rBcCsJt|tr|St|tjr(|j|jdSt|dsBtd|d|S)zDReturn the object's config depending on string, function, or others.)r&r, get_configz"Unable to recognize the config of r*) r.r)rDrEr?r@r7r/rHrGrrrrAs   rA)__doc__r2rrrDtensorflow.compat.v2compatv2r keras.saving.saved_modelr keras.utilsrtensorflow.python.utilrr _ENABLEDrr#rrrBrArrrrs"    '%