B ӻd{$@sdZddlmZddlmZddlmZddlmZddlm Z ddlm Z ddl m Z dd lmZdd lmZy dd lZWnek rd ZYnXed dddZeddddZd S)zKeras model saving code.)tf2) hdf5_format) saving_utils)load) load_context)save) generic_utils)path_to_string) keras_exportNzkeras.models.save_modelTc Csddlm}trdnd} |p"| }t|}t||dks\tdk rRt|tj s\t |r|j svt||j svt dt||||n*tt|||||||WdQRXdS)a Saves a model as a TensorFlow SavedModel or HDF5 file. See the [Serialization and Saving guide](https://keras.io/guides/serialization_and_saving/) for details. Usage: >>> model = tf.keras.Sequential([ ... tf.keras.layers.Dense(5, input_shape=(3,)), ... tf.keras.layers.Softmax()]) >>> model.save('/tmp/model') >>> loaded_model = tf.keras.models.load_model('/tmp/model') >>> x = tf.random.uniform((10, 3)) >>> assert np.allclose(model.predict(x), loaded_model.predict(x)) The SavedModel and HDF5 file contains: - the model's configuration (topology) - the model's weights - the model's optimizer's state (if any) Thus models can be reinstantiated in the exact same state, without any of the code used for model definition or training. Note that the model weights may have different scoped names after being loaded. Scoped names include the model/layer names, such as `"dense_1/kernel:0"`. It is recommended that you use the layer properties to access specific variables, e.g. `model.get_layer("dense_1").kernel`. __SavedModel serialization format__ Keras SavedModel uses `tf.saved_model.save` to save the model and all trackable objects attached to the model (e.g. layers and variables). The model config, weights, and optimizer are saved in the SavedModel. Additionally, for every Keras layer attached to the model, the SavedModel stores: * the config and metadata -- e.g. name, dtype, trainable status * traced call and loss functions, which are stored as TensorFlow subgraphs. The traced functions allow the SavedModel format to save and load custom layers without the original class definition. You can choose to not save the traced functions by disabling the `save_traces` option. This will decrease the time it takes to save the model and the amount of disk space occupied by the output SavedModel. If you enable this option, then you _must_ provide all custom class definitions when loading the model. See the `custom_objects` argument in `tf.keras.models.load_model`. Args: model: Keras model instance to be saved. filepath: One of the following: - String or `pathlib.Path` object, path where to save the model - `h5py.File` object where to save the model overwrite: Whether we should overwrite any existing model at the target location, or instead ask the user with a manual prompt. include_optimizer: If True, save optimizer's state together. save_format: Either 'tf' or 'h5', indicating whether to save the model to Tensorflow SavedModel or HDF5. Defaults to 'tf' in TF 2.X, and 'h5' in TF 1.X. signatures: Signatures to save with the SavedModel. Applicable to the 'tf' format only. Please see the `signatures` argument in `tf.saved_model.save` for details. options: (only applies to SavedModel format) `tf.saved_model.SaveOptions` object that specifies options for saving to SavedModel. save_traces: (only applies to SavedModel format) When enabled, the SavedModel will store the function traces for each layer. This can be disabled, so that only the configs of each layer are stored. Defaults to `True`. Disabling this will decrease serialization time and reduce file size, but it requires that all custom layers/models implement a `get_config()` method. Raises: ImportError: If save format is hdf5, and h5py is not available. r) sequentialtfZh5NaWSaving the model to HDF5 format requires the model to be a Functional model or a Sequential model. It does not work for subclassed models, because such models are defined via the body of a Python method, which isn't safely serializable. Consider saving to the Tensorflow SavedModel format (by setting save_format="tf") or using `save_weights`.)Ztensorflow.python.keras.enginer renabledr rZtry_build_compiled_argumentsh5py isinstanceFileZis_hdf5_filepathZ_is_graph_networkZ SequentialNotImplementedErrorrZsave_model_to_hdf5rZSharedObjectSavingScopesaved_model_saver) modelfilepath overwriteZinclude_optimizerZ save_formatZ signaturesoptionsZ save_tracesr default_formatrU/var/www/html/venv/lib/python3.7/site-packages/tensorflow/python/keras/saving/save.py save_model#s"U      rzkeras.models.load_modelc Cstt|piht|Rtdk rRt|tjsDt|rRt |||St |}t|t rrt |||SWdQRXWdQRXWdQRXtddS)aLoads a model saved via `model.save()`. Usage: >>> model = tf.keras.Sequential([ ... tf.keras.layers.Dense(5, input_shape=(3,)), ... tf.keras.layers.Softmax()]) >>> model.save('/tmp/model') >>> loaded_model = tf.keras.models.load_model('/tmp/model') >>> x = tf.random.uniform((10, 3)) >>> assert np.allclose(model.predict(x), loaded_model.predict(x)) Note that the model weights may have different scoped names after being loaded. Scoped names include the model/layer names, such as `"dense_1/kernel:0"`. It is recommended that you use the layer properties to access specific variables, e.g. `model.get_layer("dense_1").kernel`. Args: filepath: One of the following: - String or `pathlib.Path` object, path to the saved model - `h5py.File` object from which to load the model custom_objects: Optional dictionary mapping names (strings) to custom classes or functions to be considered during deserialization. compile: Boolean, whether to compile the model after loading. options: Optional `tf.saved_model.LoadOptions` object that specifies options for loading from SavedModel. Returns: A Keras model instance. If the original model was compiled, and saved with the optimizer, then the returned model will be compiled. Otherwise, the model will be left uncompiled. In the case that an uncompiled model is returned, a warning is displayed if the `compile` argument is set to `True`. Raises: ImportError: if loading from an hdf5 file and h5py is not available. IOError: In case of an invalid savefile. Nz\Unable to load model. Filepath is not an hdf5 file (or h5py is not available) or SavedModel.)rZSharedObjectLoadingScopeZCustomObjectScoperrrrZis_hdf5rZload_model_from_hdf5r strsaved_model_loadrIOError)rZcustom_objectscompilerrrr load_models*   ,r)TTNNNT)NTN)__doc__Ztensorflow.pythonrZtensorflow.python.keras.savingrrZ*tensorflow.python.keras.saving.saved_modelrrrrrZtensorflow.python.keras.utilsrZ&tensorflow.python.keras.utils.io_utilsr Z tensorflow.python.util.tf_exportr r ImportErrorrrrrrrs,            n