add warning to feature columns

PiperOrigin-RevId: 477822908

Author: tensorflower-gardener

site/en/tutorials/estimator/linear.ipynb

@@ -293,14 +293,31 @@
         "pd.concat([dftrain, y_train], axis=1).groupby('sex').survived.mean().plot(kind='barh').set_xlabel('% survive')"
       ]
     },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "qCHvgeorEsHa"
+      },
+      "source": [
+        "## Feature Engineering for the Model"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "Dhcq8Ds4mCtm"
+      },
+      "source": [
+        "> Warning: The tf.feature_columns module described in this tutorial is not recommended for new code. Keras preprocessing layers cover this functionality, for migration instructions see the [Migrating feature columns guide](https://www.tensorflow.org/guide/migrate/migrating_feature_columns). The tf.feature_columns module was designed for use with TF1 Estimators. It does fall under our [compatibility guarantees](https://tensorflow.org/guide/versions), but will receive no fixes other than security vulnerabilities."
+      ]
+    },
     {
       "cell_type": "markdown",
       "metadata": {
         "id": "VqDKQLZn8L-B"
       },
       "source": [
-        "## Feature Engineering for the Model\n",
-        "Estimators use a system called [feature columns](https://www.tensorflow.org/guide/feature_columns) to describe how the model should interpret each of the raw input features. An Estimator expects a vector of numeric inputs, and *feature columns* describe how the model should convert each feature.\n",
+        "Estimators use a system called [feature columns](https://www.tensorflow.org/tutorials/structured_data/feature_columns) to describe how the model should interpret each of the raw input features. An Estimator expects a vector of numeric inputs, and *feature columns* describe how the model should convert each feature.\n",
         "\n",
         "Selecting and crafting the right set of feature columns is key to learning an effective model. A feature column can be either one of the raw inputs in the original features `dict` (a *base feature column*), or any new columns created using transformations defined over one or multiple base columns (a *derived feature columns*).\n",
         "\n",