From ca25bde1f8f8296ea9d41cc4442224a40295b10f Mon Sep 17 00:00:00 2001
From: Mark Daoust <markdaoust@google.com>
Date: Fri, 3 Apr 2020 10:35:35 -0700
Subject: [PATCH] Remove `%tensorflow_version` and `from __future__`

PiperOrigin-RevId: 304645574
---
 tutorials/Classification_Privacy.ipynb | 21 +++++----------------
 1 file changed, 5 insertions(+), 16 deletions(-)

diff --git a/tutorials/Classification_Privacy.ipynb b/tutorials/Classification_Privacy.ipynb
index 4e6605d..060c3f7 100644
--- a/tutorials/Classification_Privacy.ipynb
+++ b/tutorials/Classification_Privacy.ipynb
@@ -17,16 +17,14 @@
         "id": "XAVN6c8prKOL"
       },
       "source": [
-        "##### Copyright 2019 The TensorFlow Authors.\n",
-        "\n",
-        "\n"
+        "##### Copyright 2019 The TensorFlow Authors.\n"
       ]
     },
     {
       "cell_type": "code",
       "execution_count": 0,
       "metadata": {
-        "cellView": "both",
+        "cellView": "form",
         "colab": {},
         "colab_type": "code",
         "id": "SassPC7WQAUO"
@@ -131,10 +129,6 @@
       },
       "outputs": [],
       "source": [
-        "from __future__ import absolute_import\n",
-        "from __future__ import division\n",
-        "from __future__ import print_function\n",
-        "\n",
         "try:\n",
         "  # %tensorflow_version only exists in Colab.\n",
         "  %tensorflow_version 1.x\n",
@@ -226,8 +220,7 @@
       },
       "source": [
         "## Define and tune learning model hyperparameters\n",
-        "Set learning model hyperparamter values. \n",
-        "\n"
+        "Set learning model hyperparamter values. \n"
       ]
     },
     {
@@ -391,10 +384,7 @@
         "\n",
         "This guarantee is sometimes referred to as the **privacy budget**. A lower privacy budget bounds more tightly an adversary's ability to improve their guess. This ensures a stronger privacy guarantee. Intuitively, this is because it is harder for a single training point to affect the outcome of learning: for instance, the information contained in the training point cannot be memorized by the ML algorithm and the privacy of the individual who contributed this training point to the dataset is preserved.\n",
         "\n",
-        "In this tutorial, the privacy analysis is performed in the framework of Rényi Differential Privacy (RDP), which is a relaxation of pure DP based on [this paper](https://arxiv.org/abs/1702.07476) that is particularly well suited for DP-SGD.\n",
-        "\n",
-        "\n",
-        "\n"
+        "In this tutorial, the privacy analysis is performed in the framework of Rényi Differential Privacy (RDP), which is a relaxation of pure DP based on [this paper](https://arxiv.org/abs/1702.07476) that is particularly well suited for DP-SGD.\n"
       ]
     },
     {
@@ -414,8 +404,7 @@
         "1.   The total number of points in the training data, `n`.\n",
         "2. The `batch_size`.\n",
         "3.   The `noise_multiplier`.\n",
-        "4. The number of `epochs` of training.\n",
-        "\n"
+        "4. The number of `epochs` of training.\n"
       ]
     },
     {