From 74bd89d754fca076f9a3046bf6b9e5c4b87bd710 Mon Sep 17 00:00:00 2001
From: "A. Unique TensorFlower" <gardener@tensorflow.org>
Date: Wed, 24 Jun 2020 06:12:53 -0700
Subject: [PATCH]   Updates to the codelab.

PiperOrigin-RevId: 318051333
---
 .../membership_inference_attack/codelab.ipynb | 30 ++++++++++++-------
 1 file changed, 20 insertions(+), 10 deletions(-)

diff --git a/tensorflow_privacy/privacy/membership_inference_attack/codelab.ipynb b/tensorflow_privacy/privacy/membership_inference_attack/codelab.ipynb
index e717432..ad13f10 100644
--- a/tensorflow_privacy/privacy/membership_inference_attack/codelab.ipynb
+++ b/tensorflow_privacy/privacy/membership_inference_attack/codelab.ipynb
@@ -94,12 +94,6 @@
       "outputs": [],
       "source": [
         "#@title Import statements.\n",
-        "try:\n",
-        "  # %tensorflow_version only exists in Colab.\n",
-        "  %tensorflow_version 1.x\n",
-        "except Exception:\n",
-        "  pass\n",
-        "\n",
         "import numpy as np\n",
         "from typing import Tuple, Text\n",
         "from scipy import special\n",
@@ -107,7 +101,12 @@
         "import tensorflow as tf\n",
         "import tensorflow_datasets as tfds\n",
         "\n",
-        "tf.compat.v1.logging.set_verbosity(tf.logging.ERROR)"
+        "# Set verbosity.\n",
+        "tf.compat.v1.logging.set_verbosity(tf.compat.v1.logging.ERROR)\n",
+        "from warnings import simplefilter\n",
+        "from sklearn.exceptions import ConvergenceWarning\n",
+        "simplefilter(action=\"ignore\", category=ConvergenceWarning)\n",
+        "simplefilter(action=\"ignore\", category=FutureWarning)"
       ]
     },
     {
@@ -274,12 +273,23 @@
         "#@markdown doesn't have privacy issues according to this test. Higher values,\n",
         "#@markdown on the contrary, indicate potential privacy issues.\n",
         "\n",
-        "#@markdown Note: This will take a while, since it also trains ML models to\n",
-        "#@markdown separate train/test examples.\n",
-        "\n",
         "labels_train = np.argmax(y_train, axis=1)\n",
         "labels_test = np.argmax(y_test, axis=1)\n",
         "\n",
+        "results_without_classifiers = mia.run_all_attacks(\n",
+        "    loss_train,\n",
+        "    loss_test,\n",
+        "    logits_train,\n",
+        "    logits_test,\n",
+        "    labels_train,\n",
+        "    labels_test,\n",
+        "    attack_classifiers=[],\n",
+        ")\n",
+        "print(results_without_classifiers)\n",
+        "\n",
+        "# Note: This will take a while, since it also trains ML models to\n",
+        "# separate train/test examples. If it's taking too looking, use\n",
+        "# the `run_all_attacks` function instead.\n",
         "attack_result_summary = mia.run_all_attacks_and_create_summary(\n",
         "    loss_train,\n",
         "    loss_test,\n",