Merge pull request #1964 from RenuPatelGoogle:patch-5

PiperOrigin-RevId: 407797233

Author: copybara-github

site/en/guide/extension_type.ipynb

@@ -350,8 +350,7 @@
       "source": [
         "### Printable representation\n",
         "\n",
-        "`ExtensionType` adds a default printable representation method (`__repr__`) that includes the class name and the value for each field:\n",
-        "\n"
+        "`ExtensionType` adds a default printable representation method (`__repr__`) that includes the class name and the value for each field:\n"
       ]
     },
     {
@@ -2115,15 +2114,11 @@
     "colab": {
       "collapsed_sections": [],
       "name": "extension_type.ipynb",
-      "provenance": [],
       "toc_visible": true
     },
     "kernelspec": {
       "display_name": "Python 3",
       "name": "python3"
-    },
-    "language_info": {
-      "name": "python"
     }
   },
   "nbformat": 4,

site/en/guide/function.ipynb

@@ -272,7 +272,7 @@
         "id": "K7scSzLx662f"
       },
       "source": [
-        "When we pass arguments of different types into a `Function`, both stages are run:\n"
+        "When you pass arguments of different types into a `Function`, both stages are run:\n"
       ]
     },
     {
@@ -1301,7 +1301,7 @@
         "\n",
         "`Function` creates a new `ConcreteFunction` when called with a new value of a Python argument. However, it does not do that for the Python closure, globals, or nonlocals of that `Function`. If their value changes in between calls to the `Function`, the `Function` will still use the values they had when it was traced. This is different from how regular Python functions work.\n",
         "\n",
-        "For that reason, we recommend a functional programming style that uses arguments instead of closing over outer names."
+        "For that reason, you should follow a functional programming style that uses arguments instead of closing over outer names."
       ]
     },
     {
@@ -1439,7 +1439,7 @@
       "source": [
         "Using the same `Function` to evaluate the updated instance of the model will be buggy since the updated model has the [same cache key](#rules_of_tracing) as the original model.\n",
         "\n",
-        "For that reason, we recommend that you write your `Function` to avoid depending on mutable object attributes or create new objects.\n",
+        "For that reason, you're recommended to write your `Function` to avoid depending on mutable object attributes or create new objects.\n",
         "\n",
         "If that is not possible, one workaround is to make new `Function`s each time you modify your object to force retracing:"
       ]
@@ -1690,7 +1690,6 @@
     "colab": {
       "collapsed_sections": [],
       "name": "function.ipynb",
-      "provenance": [],
       "toc_visible": true
     },
     "kernelspec": {

site/en/guide/migrate/saved_model.ipynb

@@ -748,7 +748,6 @@
     "colab": {
       "collapsed_sections": [],
       "name": "saved_model.ipynb",
-      "provenance": [],
       "toc_visible": true
     },
     "kernelspec": {

site/en/guide/tensor.ipynb

@@ -1491,7 +1491,6 @@
         "Tce3stUlHN0L"
       ],
       "name": "tensor.ipynb",
-      "provenance": [],
       "toc_visible": true
     },
     "kernelspec": {

site/en/tutorials/keras/text_classification.ipynb

@@ -119,8 +119,7 @@
         "import tensorflow as tf\n",
         "\n",
         "from tensorflow.keras import layers\n",
-        "from tensorflow.keras import losses\n",
-        "from tensorflow.keras import preprocessing"
+        "from tensorflow.keras import losses\n"
       ]
     },
     {
@@ -286,7 +285,7 @@
         "batch_size = 32\n",
         "seed = 42\n",
         "\n",
-        "raw_train_ds = tf.keras.preprocessing.text_dataset_from_directory(\n",
+        "raw_train_ds = tf.keras.utils.text_dataset_from_directory(\n",
         "    'aclImdb/train', \n",
         "    batch_size=batch_size, \n",
         "    validation_split=0.2, \n",
@@ -366,7 +365,7 @@
       },
       "outputs": [],
       "source": [
-        "raw_val_ds = tf.keras.preprocessing.text_dataset_from_directory(\n",
+        "raw_val_ds = tf.keras.utils.text_dataset_from_directory(\n",
         "    'aclImdb/train', \n",
         "    batch_size=batch_size, \n",
         "    validation_split=0.2, \n",
@@ -382,7 +381,7 @@
       },
       "outputs": [],
       "source": [
-        "raw_test_ds = tf.keras.preprocessing.text_dataset_from_directory(\n",
+        "raw_test_ds = tf.keras.utils.text_dataset_from_directory(\n",
         "    'aclImdb/test', \n",
         "    batch_size=batch_size)"
       ]
@@ -395,7 +394,7 @@
       "source": [
         "### Prepare the dataset for training\n",
         "\n",
-        "Next, you will standardize, tokenize, and vectorize the data using the helpful `preprocessing.TextVectorization` layer. \n",
+        "Next, you will standardize, tokenize, and vectorize the data using the helpful `tf.keras.layers.TextVectorization` layer. \n",
         "\n",
         "Standardization refers to preprocessing the text, typically to remove punctuation or HTML elements to simplify the dataset. Tokenization refers to splitting strings into tokens (for example, splitting a sentence into individual words, by splitting on whitespace). Vectorization refers to converting tokens into numbers so they can be fed into a neural network. All of these tasks can be accomplished with this layer.\n",
         "\n",