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Updates to the codelab.

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PiperOrigin-RevId: 318051333
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A. Unique TensorFlower 2020-06-24 06:12:53 -07:00
parent c01e7b71ab
commit 74bd89d754
1 changed files with 20 additions and 10 deletions
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tensorflow_privacy/privacy/membership_inference_attack/codelab.ipynb
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@ -94,12 +94,6 @@
"outputs": [], "outputs": [],
"source": [ "source": [
"#@title Import statements.\n", "#@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", "import numpy as np\n",
"from typing import Tuple, Text\n", "from typing import Tuple, Text\n",
"from scipy import special\n", "from scipy import special\n",
@ -107,7 +101,12 @@
"import tensorflow as tf\n", "import tensorflow as tf\n",
"import tensorflow_datasets as tfds\n", "import tensorflow_datasets as tfds\n",
"\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 doesn't have privacy issues according to this test. Higher values,\n",
"#@markdown on the contrary, indicate potential privacy issues.\n", "#@markdown on the contrary, indicate potential privacy issues.\n",
"\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_train = np.argmax(y_train, axis=1)\n",
"labels_test = np.argmax(y_test, axis=1)\n", "labels_test = np.argmax(y_test, axis=1)\n",
"\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", "attack_result_summary = mia.run_all_attacks_and_create_summary(\n",
" loss_train,\n", " loss_train,\n",
" loss_test,\n", " loss_test,\n",