B )`@sdZddlmZddlmZddlmZddlZddlZddlmZddl m Z ddl m Z dd l mZdd lmZdd lmZdd lmZdd lmZy ddlZWnek rdZYnXdddgZdZeddddZeddddZdS)zKeras model saving code.)absolute_import)division)print_functionN)tf2) hdf5_format)load)save) generic_utils)path_to_string) loader_impl) keras_exportz.h5z.hdf5z.kerasTzkeras.models.save_modelc Csddlm}trdnd}|p"|}t|}|dks\tdk rHt|tjs\tj |dt kr|j svt||j svtdt||||nt||||||dS)a# Saves a model as a TensorFlow SavedModel or HDF5 file. 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 saved model contains: - the model's configuration (topology) - the model's weights - the model's optimizer's state (if any) Thus the saved model 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_ The SavedModel serialization path uses `tf.saved_model.save` to save the model and all trackable objects attached to the model (e.g. layers and variables). `@tf.function`-decorated methods are also saved. Additional trackable objects and functions are added to the SavedModel to allow the model to be loaded back as a Keras Model object. Arguments: 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: Optional `tf.saved_model.SaveOptions` object that specifies options for saving to SavedModel. Raises: ImportError: If save format is hdf5, and h5py is not available. r) sequentialtfh5NaWSaving 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 rZenabledr h5py isinstanceFileospathsplitext_HDF5_EXTENSIONSZ_is_graph_networkZ SequentialNotImplementedErrorrZsave_model_to_hdf5saved_model_saver) modelfilepath overwriteZinclude_optimizerZ save_formatZ signaturesoptionsr default_formatrR/home/dcms/DCMS/lib/python3.7/site-packages/tensorflow/python/keras/saving/save.py save_model/s?   r!zkeras.models.load_modelc Cs~t|p i^tdk r>> 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`. Arguments: 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.)r ZCustomObjectScoperrrZis_hdf5rZload_model_from_hdf5r six string_typesr Zparse_saved_modelsaved_model_loadrIOError)rZcustom_objectscompilerrrr load_models*  r')TTNNN)NTN)__doc__ __future__rrrrr"Ztensorflow.pythonrZtensorflow.python.keras.savingrZ*tensorflow.python.keras.saving.saved_modelrr$rrZtensorflow.python.keras.utilsr Z&tensorflow.python.keras.utils.io_utilsr Ztensorflow.python.saved_modelr Z tensorflow.python.util.tf_exportr r ImportErrorrZ%_KERAS_SAVED_MODEL_STILL_EXPERIMENTALr!r'rrrr s6               S