|---| | [ View source on GitHub](https://github.com/keras-team/keras/tree/v3.15.1/keras/src/saving/saving_api.py#L19-L122) | Saves a model as a `.keras` file. tf.keras.models.save_model( model, filepath, overwrite=True, zipped=None, **kwargs ) ### Used in the notebooks | Used in the tutorials | |---| | - [Train and serve a TensorFlow model with TensorFlow Serving](https://www.tensorflow.org/tfx/tutorials/serving/rest_simple) |


| ## 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: 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.