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docs: modernize Keras save_and_load notebook, fix dead links and legacy syntax #2440

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docs: modernize Keras save_and_load notebook, fix dead links and legacy syntax #2440

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18 changes: 9 additions & 9 deletions site/en/tutorials/keras/save_and_load.ipynb
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Original file line number Diff line number Diff line change
Expand Up @@ -109,7 +109,7 @@
"\n",
"Caution: TensorFlow models are code and it is important to be careful with untrusted code. See [Using TensorFlow Securely](https://github.com/tensorflow/tensorflow/blob/master/SECURITY.md) for details.\n",
"\n",
"### Options\n",
"## Options\n",
"\n",
"There are different ways to save TensorFlow models depending on the API you're using. This guide uses [tf.keras](https://www.tensorflow.org/guide/keras)—a high-level API to build and train models in TensorFlow. The new, high-level `.keras` format used in this tutorial is recommended for saving Keras objects, as it provides robust, efficient name-based saving that is often easier to debug than low-level or legacy formats. For more advanced saving or serialization workflows, especially those involving custom objects, please refer to the [Save and load Keras models guide](https://www.tensorflow.org/guide/keras/save_and_serialize). For other approaches, refer to the [Using the SavedModel format guide](../../guide/saved_model.ipynb)."
]
Expand Down Expand Up @@ -169,7 +169,7 @@
"source": [
"### Get an example dataset\n",
"\n",
"To demonstrate how to save and load weights, you'll use the [MNIST dataset](http://yann.lecun.com/exdb/mnist/). To speed up these runs, use the first 1000 examples:"
"To demonstrate how to save and load weights, you'll use the [MNIST dataset](https://www.tensorflow.org/datasets/catalog/mnist). To speed up these runs, use the first 1000 examples:"
]
},
{
Expand Down Expand Up @@ -218,9 +218,9 @@
"# Define a simple sequential model\n",
"def create_model():\n",
" model = tf.keras.Sequential([\n",
" keras.layers.Dense(512, activation='relu', input_shape=(784,)),\n",
" keras.layers.Dropout(0.2),\n",
" keras.layers.Dense(10)\n",
" tf.keras.layers.Dense(512, activation='relu', input_shape=(784,)),\n",
" tf.keras.layers.Dropout(0.2),\n",
" tf.keras.layers.Dense(10)\n",
" ])\n",
"\n",
" model.compile(optimizer='adam',\n",
Expand Down Expand Up @@ -559,9 +559,9 @@
"- Passing `save_format='h5'` to `save()`\n",
"- Passing a filename that ends in `.h5`\n",
"\n",
"Saving a fully-functional model is very useful—you can load them in TensorFlow.js ([Saved Model](https://www.tensorflow.org/js/tutorials/conversion/import_saved_model), [HDF5](https://www.tensorflow.org/js/tutorials/conversion/import_keras)) and then train and run them in web browsers, or convert them to run on mobile devices using TensorFlow Lite ([Saved Model](https://www.tensorflow.org/lite/models/convert/#convert_a_savedmodel_recommended_), [HDF5](https://www.tensorflow.org/lite/models/convert/#convert_a_keras_model_))\n",
"Saving a fully-functional model is very useful—you can load them in TensorFlow.js ([Saved Model](https://www.tensorflow.org/js/guide/conversion), [HDF5](https://www.tensorflow.org/js/guide/conversion)) and then train and run them in web browsers, or convert them to run on mobile devices using TensorFlow Lite ([Saved Model](https://www.tensorflow.org/lite/models/convert/), [HDF5](https://www.tensorflow.org/lite/models/convert/))\n",
"\n",
"\\*Custom objects (for example, subclassed models or layers) require special attention when saving and loading. Refer to the **Saving custom objects** section below."
"**Note:** Custom objects (for example, subclassed models or layers) require special attention when saving and loading. Refer to the **Saving custom objects** section below."
]
},
{
Expand Down Expand Up @@ -813,8 +813,8 @@
"* The model's architecture\n",
"* The model's training configuration (what you pass to the `.compile()` method)\n",
"* The optimizer and its state, if any (this enables you to restart training where you left off)\n",
"\n",
"Keras is not able to save the `v1.x` optimizers (from `tf.compat.v1.train`) since they aren't compatible with checkpoints. For v1.x optimizers, you need to re-compile the model after loading—losing the state of the optimizer.\n"
"\n"

]
},
{
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