diff --git a/site/en/guide/intro_to_modules.ipynb b/site/en/guide/intro_to_modules.ipynb index 79bbe89ca56..5e49acb645b 100644 --- a/site/en/guide/intro_to_modules.ipynb +++ b/site/en/guide/intro_to_modules.ipynb @@ -716,7 +716,7 @@ "* Saving and restoring python objects instead of just black-box functions\n", "* `get_config` and `from_config` methods that allow you to accurately store configurations to allow model cloning in Python\n", "\n", - "These features allow for far more complex models through subclassing, such as a custom GAN or a Variational AutoEncoder (VAE) model. Read about them in the [full guide](./keras/custom_layers_and_models.ipynb) to custom layers and models.\n", + "These features allow for far more complex models through subclassing, such as a custom GAN or a Variational AutoEncoder (VAE) model. Read about them in the [full guide](https://keras.io/guides/making_new_layers_and_models_via_subclassing/) to custom layers and models.\n", "\n", "Keras models also come with extra functionality that makes them easy to train, evaluate, load, save, and even train on multiple machines." ] @@ -978,7 +978,7 @@ "source": [ "Overriding `tf.keras.Model` is a very Pythonic approach to building TensorFlow models. If you are migrating models from other frameworks, this can be very straightforward.\n", "\n", - "If you are constructing models that are simple assemblages of existing layers and inputs, you can save time and space by using the [functional API](./keras/functional.ipynb), which comes with additional features around model reconstruction and architecture.\n", + "If you are constructing models that are simple assemblages of existing layers and inputs, you can save time and space by using the [functional API](https://keras.io/guides/functional_api/), which comes with additional features around model reconstruction and architecture.\n", "\n", "Here is the same model with the functional API:" ]