o 2h-@sddlZddlZddlmZddlmZddlmZddlm Z ddl m Z ddl m Z zddl Z Wn ey=dZ Ynwedd gdd d Zed dgdddZed dddZeddddZdS)N)logging) keras_export)legacy_h5_format) saving_lib) file_utils)io_utilszkeras.saving.save_modelzkeras.models.save_modelTc KsN|dd}|dd}|r,t|dst|dr%td|ntd||r9td t|t|drEtd t|d }|d urS| }z | o\t j |}Wn t yid}Ynw|rw|swt |} | swd S|rt|drt||S|stj||dd St|drt||||Std|d)aSaves a model as a `.keras` file. Args: model: Keras model instance to be saved. filepath: `str` or `pathlib.Path` object. Path where to save the model. overwrite: Whether we should overwrite any existing model at the target location, or instead ask the user via an interactive prompt. zipped: Whether to save the model as a zipped `.keras` archive (default when saving locally), or as an unzipped directory (default when saving on the Hugging Face Hub). Example: ```python model = keras.Sequential( [ keras.layers.Dense(5, input_shape=(3,)), keras.layers.Softmax(), ], ) model.save("model.keras") loaded_model = keras.saving.load_model("model.keras") x = keras.random.uniform((10, 3)) assert np.allclose(model.predict(x), loaded_model.predict(x)) ``` Note that `model.save()` is an alias for `keras.saving.save_model()`. The saved `.keras` file is a `zip` archive that contains: - The model's configuration (architecture) - The model's weights - The model's optimizer's state (if any) Thus models can be reinstantiated in the exact same state. include_optimizerT save_formatF.h5.hdf5.keraszThe `save_format` argument is deprecated in Keras 3. We recommend removing this argument as it can be inferred from the file path. Received: save_format=zThe `save_format` argument is deprecated in Keras 3. Please remove this argument and pass a file path with either `.keras` or `.h5` extension.Received: save_format=z-The following argument(s) are not supported: aYou are saving your model as an HDF5 file via `model.save()` or `keras.saving.save_model(model)`. This file format is considered legacy. We recommend using instead the native Keras format, e.g. `model.save('my_model.keras')` or `keras.saving.save_model(model, 'my_model.keras')`. hf://N)zippedaInvalid filepath extension for saving. Please add either a `.keras` extension for the native Keras format (recommended) or a `.h5` extension. Use `model.export(filepath)` if you want to export a SavedModel for use with TFLite/TFServing/etc. Received: filepath=.)popstrendswithrwarning ValueErrorlistkeys startswithospathexists TypeErrorrask_to_proceed_with_overwriter save_modelrsave_model_to_hdf5) modelfilepath overwriterkwargsrr is_hfrproceedr&V/var/www/html/chatgem/venv/lib/python3.10/site-packages/keras/src/saving/saving_api.pyrsf &      rzkeras.saving.load_modelzkeras.models.load_modelcCst|do t|}t|ott|d}t|d}t |rJt|sJ|sJ|sJtt t j |}t||t|rJ|}d}|sP|sP|rYt j||||dSt|dritj||||dSt|drxtd|dtd |d |d ) aLoads a model saved via `model.save()`. Args: filepath: `str` or `pathlib.Path` object, path to the saved model file. 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. 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`. Returns: A Keras model instance. If the original model was compiled, and the argument `compile=True` is set, then the returned model will be compiled. Otherwise, the model will be left uncompiled. Example: ```python model = keras.Sequential([ keras.layers.Dense(5, input_shape=(3,)), keras.layers.Softmax()]) model.save("model.keras") loaded_model = keras.saving.load_model("model.keras") x = np.random.random((10, 3)) assert np.allclose(model.predict(x), loaded_model.predict(x)) ``` Note that the model variables may have different name values (`var.name` property, e.g. `"dense_1/kernel:0"`) after being reloaded. It is recommended that you use layer attributes to access specific variables, e.g. `model.get_layer("dense_1").kernel`. r z config.jsonrT)custom_objectscompile safe_moder zFile not found: filepath=z<. Please ensure the file is an accessible `.keras` zip file.$File format not supported: filepath=a. Keras 3 only supports V3 `.keras` files and legacy H5 format files (`.h5` extension). Note that the legacy SavedModel format is not supported by `load_model()` in Keras 3. In order to reload a TensorFlow SavedModel as an inference-only layer in Keras 3, use `keras.layers.TFSMLayer(za, call_endpoint='serving_default')` (note that your `call_endpoint` might have a different name).)rrzipfile is_zipfilerisdirrjoinris_remote_pathr get_temp_dirrrbasenamecopy load_modelrload_model_from_hdf5r)r!r(r)r* is_keras_zip is_keras_dirr$ local_pathr&r&r'r4|sZ%     r4zkeras.saving.save_weightscKst|}|dur|dstd||dur$|ds$td|ztj|}Wn ty7d}Ynw|rE|sEt|}|sEdSt j |||fi|dS)N .weights.h5z;The filename must end in `.weights.h5`. Received: filepath=)z weights.h5z weights.jsonz`The filename must end in `.weights.json` when `max_shard_size` is specified. Received: filepath=F) rrrrrrrrrrsave_weights_only)r r!r"max_shard_sizer# filepath_strrr%r&r&r' save_weightss2   r=zkeras.saving.load_weightsFcKst|}|dd}|dd}|rtd||dr>|dur)td||dur4td|tj|||ddS|dsH|d r^|durStd|tj||||d dS|d sh|d rtsntd |durytd|t|d/}d|j vrd|vr|d}|rt |||nt |||WddSWddS1swYdStd|d)Nobjects_to_skipby_namezInvalid keyword arguments: r zF`objects_to_skip` only supports loading '.weights.h5' files.Received: zI`by_name` only supports loading legacy '.h5' or '.hdf5' files. Received: ) skip_mismatchr9z .weights.json)r@r>r r z2Loading a H5 file requires `h5py` to be installed.r layer_names model_weightsr+zY. Keras 3 only supports V3 `.keras` and `.weights.h5` files, or legacy V1/V2 `.h5` files.) rrrrrload_weights_onlyh5py ImportErrorFileattrsr$load_weights_from_hdf5_group_by_nameload_weights_from_hdf5_group)r r!r@r#r<r>r?fr&r&r' load_weightss~     " rL)TN)NTT)F)rr,abslrkeras.src.api_exportrkeras.src.legacy.savingrkeras.src.savingrkeras.src.utilsrrrErFrr4r=rLr&r&r&r's,           i c