ó 6êbi`3ãóò•SrSSKrSSKrSSKrSSKJs Jr SSKJ r SSK J r SSK J r SSKJr SSKrSrSr\ "S S 5SS j5r\ "S S 5SSj5rSSjrSSjrSrg!\a SrN@f=f)z#Public API surface for saving APIs.éN)Ú keras_export)Ú saving_lib)Úsave)Úio_utilsTcó,•Sn[U5RS5(aqUS:wakUnU(dURSS5n[RR [ R"5[RRU55nX04$)z8Supports GCS URIs through bigstore via a temporary file.Nzgs://Útfz /bigstore/) ÚstrÚ startswithÚreplaceÚosÚpathÚjoinrÚ get_temp_dirÚbasename)ÚfilepathÚ save_formatÚis_ossÚ gs_filepaths ÚX/home/james-whalen/.local/lib/python3.13/site-packages/tf_keras/src/saving/saving_api.pyÚ_support_gcs_urir$sw€à€KÜ ˆ8ƒ}×Ñ ×(Ñ(¨[¸DÓ-@؈ ÞØ"×*Ñ*¨7°LÓAˆKÜ—7‘7—<‘<Ü × #Ò #Ó %¤r§w¡w×'7Ñ'7¸ Ó'Dó ˆð Ð Ð ózkeras.saving.save_modelzkeras.models.save_modelc óö•[X5n[X[5upQUS:Xa[R"SSS9 US:Xa[ R RU5nU(a%U(d[R"U5nU(dgU(a%[S[UR5535e[R"X5 g[ R"UU4UUS .UD6$![a SnN˜f=f) 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. Args: model: TF-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. save_format: Either `"keras"`, `"tf"`, `"h5"`, indicating whether to save the model in the native TF-Keras format (`.keras`), in the TensorFlow SavedModel format (referred to as "SavedModel" below), or in the legacy HDF5 format (`.h5`). Defaults to `"tf"` in TF 2.X, and `"h5"` in TF 1.X. SavedModel format arguments: include_optimizer: Only applied to SavedModel and legacy HDF5 formats. If False, do not save the optimizer state. Defaults to True. signatures: Only applies to SavedModel format. Signatures to save with the SavedModel. See the `signatures` argument in `tf.saved_model.save` for details. options: Only applies to SavedModel format. `tf.saved_model.SaveOptions` object that specifies SavedModel saving options. 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. Example: ```python model = tf.keras.Sequential([ tf.keras.layers.Dense(5, input_shape=(3,)), tf.keras.layers.Softmax()]) model.save("model.keras") loaded_model = tf.keras.saving.load_model("model.keras") x = tf.random.uniform((10, 3)) assert np.allclose(model.predict(x), loaded_model.predict(x)) ``` Note that `model.save()` is an alias for `tf.keras.saving.save_model()`. The SavedModel or HDF5 file 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, 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__ With `save_format="tf"`, the model and all trackable objects attached to the it (e.g. layers and variables) are saved as a TensorFlow SavedModel. The model config, weights, and optimizer are included in the SavedModel. Additionally, for every TF-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.saving.load_model`. Úh5zÀYou are saving your model as an HDF5 file via `model.save()`. This file format is considered legacy. We recommend using instead the native TF-Keras format, e.g. `model.save('my_model.keras')`.é)Ú stacklevelÚkerasFNúMThe following argument(s) are not supported with the native TF-Keras format: )Ú overwriter)Úget_save_formatrrÚwarningsÚwarnr r ÚexistsÚ TypeErrorrÚask_to_proceed_with_overwriteÚ ValueErrorÚlistÚkeysrÚ save_modelÚlegacy_sm_saving_lib)ÚmodelrrrÚkwargsrr"Úproceeds rr(r(1s€ôj" (Ó8€Kô-¨XÄFÓKÑ€KðdÓÜ Š ð 3ðò  ðgÓð Ü—W‘W—^‘^ HÓ-ˆFö ž)Ü×<Ò<¸XÓFˆGÞØÞ Üð4Ü48¸¿¹»Ó4GÐ3HðJóð ô ×Ò˜eÕ.ô$×.Ò.Ø Ø ð ð Ø#ñ  ð ñ  ð øôó ØŠFð úsÁC)Ã) C8Ã7C8zkeras.saving.load_modelzkeras.models.load_modelc ó’•[USS9n[X[5up`Ub([RR R X`SS9 [U5RS5=(a [R"U5n[R"U5(aÌ[RR RU5(džU(d—[RR![R""5[RR%U55n[RR R XSS9 [R"U5(aUnSnU(aCU(a%['S[)UR+5535e[R,"UUUUS9$[.R,"U4XS.UD6$) a.Loads 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 TF-Keras v3 model format. Defaults to True. SavedModel format arguments: options: Only applies to SavedModel format. Optional `tf.saved_model.LoadOptions` object that specifies SavedModel loading options. Returns: A TF-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 = tf.keras.Sequential([ tf.keras.layers.Dense(5, input_shape=(3,)), tf.keras.layers.Softmax()]) model.save("model.keras") loaded_model = tf.keras.saving.load_model("model.keras") x = tf.random.uniform((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`. 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