Removed experimental flag.

PiperOrigin-RevId: 421709721

site/en/guide/data.ipynb

@@ -2572,7 +2572,7 @@
         "id": "GxLAr-7p0ATX"
       },
       "source": [
-        "To use `tf.data.experimental.sample_from_datasets` pass the datasets, and the weight for each:"
+        "To use `tf.data.Dataset.sample_from_datasets` pass the datasets, and the weight for each:"
       ]
     },
     {
@@ -2583,7 +2583,7 @@
       },
       "outputs": [],
       "source": [
-        "balanced_ds = tf.data.experimental.sample_from_datasets(\n",
+        "balanced_ds = tf.data.Dataset.sample_from_datasets(\n",
         "    [negative_ds, positive_ds], [0.5, 0.5]).batch(10)"
       ]
     },
@@ -2623,15 +2623,15 @@
         "id": "kZ9ezkK6irMD"
       },
       "source": [
-        "One problem with the above `experimental.sample_from_datasets` approach is that\n",
-        "it needs a separate `tf.data.Dataset` per class. Using `Dataset.filter`\n",
-        "works, but results in all the data being loaded twice.\n",
+        "One problem with the above `Dataset.sample_from_datasets` approach is that\n",
+        "it needs a separate `tf.data.Dataset` per class. You could use `Dataset.filter`\n",
+        "to create those two datasets, but that results in all the data being loaded twice.\n",
         "\n",
-        "The `data.experimental.rejection_resample` function can be applied to a dataset to rebalance it, while only loading it once. Elements will be dropped from the dataset to achieve balance.\n",
+        "The `data.Dataset.rejection_resample` method can be applied to a dataset to rebalance it, while only loading it once. Elements will be dropped from the dataset to achieve balance.\n",
         "\n",
-        "`data.experimental.rejection_resample` takes a `class_func` argument. This `class_func` is applied to each dataset element, and is used to determine which class an example belongs to for the purposes of balancing.\n",
+        "`data.Dataset.rejection_resample` takes a `class_func` argument. This `class_func` is applied to each dataset element, and is used to determine which class an example belongs to for the purposes of balancing.\n",
         "\n",
-        "The elements of `creditcard_ds` are already `(features, label)` pairs. So the `class_func` just needs to return those labels:"
+        "The goal here is to balance the lable distribution, and the elements of `creditcard_ds` are already `(features, label)` pairs. So the `class_func` just needs to return those labels:"
       ]
     },
     {
@@ -2646,34 +2646,15 @@
         "  return label"
       ]
     },
-    {
-      "cell_type": "markdown",
-      "metadata": {
-        "id": "DdKmE8Jumlp0"
-      },
-      "source": [
-        "The resampler also needs a target distribution, and optionally an initial distribution estimate:"
-      ]
-    },
-    {
-      "cell_type": "code",
-      "execution_count": null,
-      "metadata": {
-        "id": "9tv0tWNxmkzM"
-      },
-      "outputs": [],
-      "source": [
-        "resampler = tf.data.experimental.rejection_resample(\n",
-        "    class_func, target_dist=[0.5, 0.5], initial_dist=fractions)"
-      ]
-    },
     {
       "cell_type": "markdown",
       "metadata": {
         "id": "YxJrOZVToGuE"
       },
       "source": [
-        "The resampler deals with individual examples, so you must `unbatch` the dataset before applying the resampler:"
+        "The resampling method deals with individual examples, so in this case you must `unbatch` the dataset before applying that method.\n",
+        "\n",
+        "The method needs a target distribution, and optionally an initial distribution estimate as inputs."
       ]
     },
     {
@@ -2684,7 +2665,12 @@
       },
       "outputs": [],
       "source": [
-        "resample_ds = creditcard_ds.unbatch().apply(resampler).batch(10)"
+        "resample_ds = (\n",
+        "    creditcard_ds\n",
+        "    .unbatch()\n",
+        "    .rejection_resample(class_func, target_dist=[0.5,0.5],\n",
+        "                        initial_dist=fractions)\n",
+        "    .batch(10))"
       ]
     },
     {
@@ -2693,7 +2679,7 @@
         "id": "L-HnC1s8idqV"
       },
       "source": [
-        "The resampler returns creates `(class, example)` pairs from the output of the `class_func`. In this case, the `example` was already a `(feature, label)` pair, so use `map` to drop the extra copy of the labels:"
+        "The `rejection_resample` method returns `(class, example)` pairs where the `class` is the output of the `class_func`. In this case, the `example` was already a `(feature, label)` pair, so use `map` to drop the extra copy of the labels:"
       ]
     },
     {