extrasensorypytorch.ipynb

{
  "cells": [
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "V5bBi2M44U5I"
      },
      "source": [
        "# Applying Differential Privacy to the <i>ExtraSensory Dataset</i>"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "1sgk6DKq4U5J"
      },
      "outputs": [],
      "source": [
        "%matplotlib inline\n",
        "import matplotlib as mpl\n",
        "import matplotlib.pyplot as plt"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "i4yEnQtI4U5J"
      },
      "outputs": [],
      "source": [
        "!wget http://extrasensory.ucsd.edu/data/primary_data_files/ExtraSensory.per_uuid_features_labels.zip\n",
        "!unzip ExtraSensory.per_uuid_features_labels.zip"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "Na6Yz0Be4U5K"
      },
      "source": [
        "<a name=\"read_user_data\"></a>\n",
        "<b>Reading user data</b>.<br/>\n",
        "Each user has a separate data file that contains several pieces of the data, mainly features and labels.<br/>\n",
        "To read the different data pieces for a user, you can use the following functions:"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "8dCI-o9D4U5K"
      },
      "outputs": [],
      "source": [
        "import numpy as np\n",
        "import gzip\n",
        "from io import StringIO\n",
        "\n",
        "\n",
        "def parse_header_of_csv(csv_str):\n",
        "    # Isolate the headline columns:\n",
        "    headline = csv_str[:csv_str.index('\\n')]\n",
        "    columns = headline.split(',')\n",
        "\n",
        "    # The first column should be timestamp:\n",
        "    assert columns[0] == 'timestamp'\n",
        "    # The last column should be label_source:\n",
        "    assert columns[-1] == 'label_source'\n",
        "\n",
        "    # Search for the column of the first label:\n",
        "    for (ci, col) in enumerate(columns):\n",
        "        if col.startswith('label:'):\n",
        "            first_label_ind = ci\n",
        "            break\n",
        "        pass\n",
        "\n",
        "    # Feature columns come after timestamp and before the labels:\n",
        "    feature_names = columns[1:first_label_ind];\n",
        "    # Then come the labels, till the one-before-last column:\n",
        "    label_names = columns[first_label_ind:-1];\n",
        "    for (li, label) in enumerate(label_names):\n",
        "        # In the CSV the label names appear with prefix 'label:', but we don't need it after reading the data:\n",
        "        assert label.startswith('label:');\n",
        "        label_names[li] = label.replace('label:', '');\n",
        "        pass;\n",
        "\n",
        "    return (feature_names, label_names);\n",
        "\n",
        "\n",
        "def parse_body_of_csv(csv_str, n_features):\n",
        "    # Read the entire CSV body into a single numeric matrix:\n",
        "    full_table = np.loadtxt(StringIO(csv_str), delimiter=',', skiprows=1)\n",
        "\n",
        "    # Timestamp is the primary key for the records (examples):\n",
        "    timestamps = full_table[:, 0].astype(int);\n",
        "\n",
        "    # Read the sensor features:\n",
        "    X = full_table[:, 1:(n_features + 1)];\n",
        "\n",
        "    # Read the binary label values, and the 'missing label' indicators:\n",
        "    trinary_labels_mat = full_table[:, (n_features + 1):-1];  # This should have values of either 0., 1. or NaN\n",
        "    M = np.isnan(trinary_labels_mat);  # M is the missing label matrix\n",
        "    Y = np.where(M, 0, trinary_labels_mat) > 0.;  # Y is the label matrix\n",
        "\n",
        "    return (X, Y, M, timestamps);\n",
        "\n",
        "\n",
        "'''\n",
        "Read the data (precomputed sensor-features and labels) for a user.\n",
        "This function assumes the user's data file is present.\n",
        "'''\n",
        "\n",
        "\n",
        "def read_user_data(uuid):\n",
        "    user_data_file = '%s.features_labels.csv.gz' % uuid;\n",
        "\n",
        "    # Read the entire csv file of the user:\n",
        "    with gzip.open(user_data_file, 'rb') as fid:\n",
        "        csv_str = fid.read()\n",
        "        pass\n",
        "\n",
        "    csv_str = csv_str.decode('UTF-8')\n",
        "\n",
        "    (feature_names, label_names) = parse_header_of_csv(csv_str)\n",
        "    n_features = len(feature_names);\n",
        "    (X, Y, M, timestamps) = parse_body_of_csv(csv_str, n_features)\n",
        "\n",
        "    return (X, Y, M, timestamps, feature_names, label_names)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "3P9lT_uq4U5N"
      },
      "outputs": [],
      "source": [
        "def get_sensor_names_from_features(feature_names):\n",
        "    feat_sensor_names = np.array([None for feat in feature_names]);\n",
        "    for (fi, feat) in enumerate(feature_names):\n",
        "        if feat.startswith('raw_acc'):\n",
        "            feat_sensor_names[fi] = 'Acc';\n",
        "            pass;\n",
        "        elif feat.startswith('proc_gyro'):\n",
        "            feat_sensor_names[fi] = 'Gyro';\n",
        "            pass;\n",
        "        elif feat.startswith('raw_magnet'):\n",
        "            feat_sensor_names[fi] = 'Magnet';\n",
        "            pass;\n",
        "        elif feat.startswith('watch_acceleration'):\n",
        "            feat_sensor_names[fi] = 'WAcc';\n",
        "            pass;\n",
        "        elif feat.startswith('watch_heading'):\n",
        "            feat_sensor_names[fi] = 'Compass';\n",
        "            pass;\n",
        "        elif feat.startswith('location'):\n",
        "            feat_sensor_names[fi] = 'Loc';\n",
        "            pass;\n",
        "        elif feat.startswith('location_quick_features'):\n",
        "            feat_sensor_names[fi] = 'Loc';\n",
        "            pass;\n",
        "        elif feat.startswith('audio_naive'):\n",
        "            feat_sensor_names[fi] = 'Aud';\n",
        "            pass;\n",
        "        elif feat.startswith('audio_properties'):\n",
        "            feat_sensor_names[fi] = 'AP';\n",
        "            pass;\n",
        "        elif feat.startswith('discrete'):\n",
        "            feat_sensor_names[fi] = 'PS';\n",
        "            pass;\n",
        "        elif feat.startswith('lf_measurements'):\n",
        "            feat_sensor_names[fi] = 'LF';\n",
        "            pass;\n",
        "        else:\n",
        "            raise ValueError(\"!!! Unsupported feature name: %s\" % feat);\n",
        "\n",
        "        pass;\n",
        "\n",
        "    return feat_sensor_names;"
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "def project_features_to_selected_sensors(X, feat_sensor_names, sensors_to_use):\n",
        "    use_feature = np.zeros(len(feat_sensor_names), dtype=bool)\n",
        "    for sensor in sensors_to_use:\n",
        "        is_from_sensor = (feat_sensor_names == sensor)\n",
        "        use_feature = np.logical_or(use_feature, is_from_sensor)\n",
        "        pass;\n",
        "    X = X[:, use_feature]\n",
        "    return X\n",
        "\n",
        "\n",
        "def estimate_standardization_params(X_train):\n",
        "    mean_vec = np.nanmean(X_train, axis=0)\n",
        "    std_vec = np.nanstd(X_train, axis=0)\n",
        "    return (mean_vec, std_vec)\n",
        "\n",
        "\n",
        "def standardize_features(X, mean_vec, std_vec):\n",
        "    # Subtract the mean, to centralize all features around zero:\n",
        "    X_centralized = X - mean_vec.reshape((1, -1))\n",
        "    # Divide by the standard deviation, to get unit-variance for all features:\n",
        "    # * Avoid dividing by zero, in case some feature had estimate of zero variance\n",
        "    normalizers = np.where(std_vec > 0., std_vec, 1.).reshape((1, -1))\n",
        "    X_standard = X_centralized / normalizers\n",
        "    return X_standard\n",
        "\n",
        "\n",
        "def preprocess_data(X, Y, M, feat_sensor_names, label_names, sensors_to_use, target_labels):\n",
        "    # Project the feature matrix to the features from the desired sensors:\n",
        "    X = project_features_to_selected_sensors(X, feat_sensor_names, sensors_to_use)\n",
        "\n",
        "    # Select target labels\n",
        "    label_inds = [label_names.index(t) for t in target_labels]\n",
        "    y = Y[:, label_inds]\n",
        "\n",
        "    # Select only the examples that are not missing the target labels\n",
        "    for label_ind in label_inds:\n",
        "        missing_label = M[:, label_ind]\n",
        "        existing_label = np.logical_not(missing_label)\n",
        "        X = X[existing_label, :]\n",
        "        y = y[existing_label]\n",
        "        M = M[existing_label]\n",
        "\n",
        "    # Also, there may be missing sensor-features (represented in the data as NaN).\n",
        "    # You can handle those by imputing a value of zero (since we standardized,\n",
        "    # this is equivalent to assuming average value).\n",
        "    X[np.isnan(X)] = 0.\n",
        "\n",
        "    return X, y"
      ],
      "metadata": {
        "id": "ItDWvZXgw5wX"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "EWOQZ-lJ4U5O"
      },
      "source": [
        "## Loading Data From Multiple Users\n",
        "\n",
        "Using cross-validation splits provided by the authors from [here](http://extrasensory.ucsd.edu/data/cv5Folds.zip).\n",
        "\n",
        "Unzip the file above and store the splits in `cv_5_folds`.\n",
        "\n",
        "Train data will come from the following splits: \n",
        "1. `fold_0_train_android_uuids.txt`\n",
        "2. `fold_0_train_iphone_uuids.txt`\n",
        "\n",
        "Test data will come from the following splits:\n",
        "1. `fold_0_test_android_uuids.txt`\n",
        "2. `fold_0_test_iphone_uuids.txt`\n",
        "\n",
        "This information will be useful for performing the membership inference attack later (users in the train and test splits will be members and non-members respectively). "
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "bX6OK5cH4U5P"
      },
      "outputs": [],
      "source": [
        "train_split_uuids_filepaths = [\n",
        "    './cv_5_folds/fold_0_train_android_uuids.txt',\n",
        "    './cv_5_folds/fold_0_train_iphone_uuids.txt',\n",
        "]\n",
        "\n",
        "test_split_uuids_filepaths = [\n",
        "    './cv_5_folds/fold_0_test_android_uuids.txt',\n",
        "    './cv_5_folds/fold_0_test_iphone_uuids.txt'\n",
        "]"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "myHo9h_04U5P"
      },
      "outputs": [],
      "source": [
        "def get_uuids_from_filepaths(uuid_filepaths):\n",
        "    uuids = []\n",
        "    for filepath in uuid_filepaths:\n",
        "        with open(filepath, 'r', newline='\\n') as f:\n",
        "            raw_uuids = f.readlines()\n",
        "            print(f\"Adding {len(raw_uuids)} UUIDS from {filepath}\")\n",
        "            clean_uuids = [uuid.strip('\\n') for uuid in raw_uuids]\n",
        "            uuids.extend(clean_uuids)\n",
        "    return uuids"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "sGAxu2ZZ5zft",
        "outputId": "d58eafb3-2254-47fb-ec9d-d59ac955229c"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "--2023-04-21 23:03:29--  http://extrasensory.ucsd.edu/data/cv5Folds.zip\n",
            "Resolving extrasensory.ucsd.edu (extrasensory.ucsd.edu)... 132.239.17.138\n",
            "Connecting to extrasensory.ucsd.edu (extrasensory.ucsd.edu)|132.239.17.138|:80... connected.\n",
            "HTTP request sent, awaiting response... 200 OK\n",
            "Length: 10803 (11K) [application/zip]\n",
            "Saving to: ‘cv5Folds.zip’\n",
            "\n",
            "cv5Folds.zip        100%[===================>]  10.55K  --.-KB/s    in 0s      \n",
            "\n",
            "2023-04-21 23:03:29 (61.2 MB/s) - ‘cv5Folds.zip’ saved [10803/10803]\n",
            "\n",
            "Archive:  cv5Folds.zip\n",
            "   creating: cv_5_folds/\n",
            "  inflating: cv_5_folds/fold_1_train_android_uuids.txt  \n",
            "  inflating: cv_5_folds/fold_0_train_iphone_uuids.txt  \n",
            "  inflating: cv_5_folds/fold_3_test_android_uuids.txt  \n",
            "  inflating: cv_5_folds/fold_3_train_android_uuids.txt  \n",
            "  inflating: cv_5_folds/fold_2_train_iphone_uuids.txt  \n",
            "  inflating: cv_5_folds/fold_4_test_android_uuids.txt  \n",
            "  inflating: cv_5_folds/fold_1_test_android_uuids.txt  \n",
            "  inflating: cv_5_folds/fold_2_test_android_uuids.txt  \n",
            "  inflating: cv_5_folds/fold_4_train_iphone_uuids.txt  \n",
            "  inflating: cv_5_folds/fold_3_train_iphone_uuids.txt  \n",
            "  inflating: cv_5_folds/fold_4_train_android_uuids.txt  \n",
            "  inflating: cv_5_folds/fold_2_train_android_uuids.txt  \n",
            "  inflating: cv_5_folds/fold_0_train_android_uuids.txt  \n",
            "  inflating: cv_5_folds/fold_0_test_android_uuids.txt  \n",
            "  inflating: cv_5_folds/fold_2_test_iphone_uuids.txt  \n",
            "  inflating: cv_5_folds/fold_4_test_iphone_uuids.txt  \n",
            "  inflating: cv_5_folds/fold_1_train_iphone_uuids.txt  \n",
            "  inflating: cv_5_folds/fold_3_test_iphone_uuids.txt  \n",
            "  inflating: cv_5_folds/fold_1_test_iphone_uuids.txt  \n",
            "  inflating: cv_5_folds/fold_0_test_iphone_uuids.txt  \n"
          ]
        }
      ],
      "source": [
        "!wget http://extrasensory.ucsd.edu/data/cv5Folds.zip\n",
        "!unzip cv5Folds.zip"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "toxByZdn4U5P",
        "outputId": "b5a673b6-4334-486f-b32e-feb59762dd2a"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Adding 21 UUIDS from ./cv_5_folds/fold_0_train_android_uuids.txt\n",
            "Adding 27 UUIDS from ./cv_5_folds/fold_0_train_iphone_uuids.txt\n"
          ]
        }
      ],
      "source": [
        "train_uuids = get_uuids_from_filepaths(train_split_uuids_filepaths)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "Rx8P2nAQ4U5P",
        "outputId": "76f4679e-7e09-40da-bfc3-7c4fc2c83dbb"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Adding 5 UUIDS from ./cv_5_folds/fold_0_test_android_uuids.txt\n",
            "Adding 7 UUIDS from ./cv_5_folds/fold_0_test_iphone_uuids.txt\n"
          ]
        }
      ],
      "source": [
        "test_uuids = get_uuids_from_filepaths(test_split_uuids_filepaths)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "fSkQCu9e4U5P"
      },
      "outputs": [],
      "source": [
        "# Sanity check: intersection of train and test uuids needs to be empty\n",
        "assert len(list(set(train_uuids) & set(test_uuids))) == 0"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "oFEp_d5w4U5P"
      },
      "outputs": [],
      "source": [
        "def preprocess_data(X, Y, M, feat_sensor_names, label_names, sensors_to_use, target_labels):\n",
        "    # Project the feature matrix to the features from the desired sensors:\n",
        "    X = project_features_to_selected_sensors(X, feat_sensor_names, sensors_to_use);\n",
        "    \n",
        "    # Select target labels\n",
        "    label_inds = [label_names.index(t) for t in target_labels]\n",
        "    y = Y[:, label_inds]\n",
        "    \n",
        "    # Select only the examples that are not missing the target labels\n",
        "    for label_ind in label_inds:\n",
        "        missing_label = M[:, label_ind]\n",
        "        existing_label = np.logical_not(missing_label)\n",
        "        X = X[existing_label, :]\n",
        "        y = y[existing_label]\n",
        "        M = M[existing_label]\n",
        "\n",
        "    # Also, there may be missing sensor-features (represented in the data as NaN).\n",
        "    # You can handle those by imputing a value of zero (since we standardized, this is equivalent to assuming average value).\n",
        "    # You can also further select examples - only those that have values for all the features.\n",
        "    # For this tutorial, let's use the simple heuristic of zero-imputation:\n",
        "    X[np.isnan(X)] = 0.\n",
        "    \n",
        "    return X, y"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "D6L_4emD4U5P"
      },
      "outputs": [],
      "source": [
        "def get_train_and_test_data(train_uuids, test_uuids, sensors_to_use, target_labels):\n",
        "    X_train, y_train = [], []\n",
        "    for uuid in train_uuids: \n",
        "        X, Y, M, timestamps, feature_names, label_names = read_user_data(uuid)\n",
        "        feat_sensor_names = get_sensor_names_from_features(feature_names);\n",
        "        X, y = preprocess_data(X, Y, M, feat_sensor_names, label_names, sensors_to_use, target_labels)\n",
        "        X_train.extend(X)\n",
        "        y_train.extend(y)\n",
        "        print(f\"Added data from UUID {uuid} to train data.\")\n",
        "        \n",
        "    # Standardize the features (subtract mean and divide by standard deviation),\n",
        "    # so that all their values will be roughly in the same range:\n",
        "    mean_vec, std_vec = estimate_standardization_params(X_train);\n",
        "    X_train = standardize_features(X_train, mean_vec, std_vec)\n",
        "        \n",
        "    X_test, y_test = [], []\n",
        "    for uuid in test_uuids: \n",
        "        X, Y, M, timestamps, feature_names, label_names = read_user_data(uuid)\n",
        "        feat_sensor_names = get_sensor_names_from_features(feature_names);\n",
        "        X, y = preprocess_data(X, Y, M, feat_sensor_names, label_names, sensors_to_use, target_labels)\n",
        "        X_test.extend(X)\n",
        "        y_test.extend(y)\n",
        "        print(f\"Added data from UUID {uuid} to test data.\")\n",
        "        \n",
        "    # Apply same standardization for test data\n",
        "    X_test = standardize_features(X_test, mean_vec, std_vec)\n",
        "    \n",
        "    X_train = np.array(X_train, np.float32)\n",
        "    y_train = np.array(y_train, dtype=int)\n",
        "    X_test = np.array(X_test, np.float32)\n",
        "    y_test = np.array(y_test, dtype=int)\n",
        "        \n",
        "    return X_train, y_train, X_test, y_test"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "Pex3zg_o4U5P"
      },
      "outputs": [],
      "source": [
        "sensors_to_use = ['Acc', 'WAcc', 'Loc', 'Aud']\n",
        "target_labels = ['FIX_walking', 'LYING_DOWN', 'SITTING']\n",
        "X_train, y_train, X_test, y_test = get_train_and_test_data(train_uuids, test_uuids, sensors_to_use, target_labels)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "a6EVimU94U5P",
        "outputId": "bf368dc4-0b6b-4757-cde9-fba586799ed6"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "(250906, 115)\n",
            "(250906, 3)\n",
            "(52817, 115)\n",
            "(52817, 3)\n"
          ]
        }
      ],
      "source": [
        "print(X_train.shape)\n",
        "print(y_train.shape)\n",
        "print(X_test.shape)\n",
        "print(y_test.shape)"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "j1jniCPy4U5P"
      },
      "source": [
        "## Non-Private and Private HAR Models Using PyTorch"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "6TF70LXm4U5P"
      },
      "outputs": [],
      "source": [
        "import torch\n",
        "import torch.nn as nn\n",
        "import torch.nn.functional as F\n",
        "import torch.optim as optim\n",
        "from torch.utils.data import TensorDataset, DataLoader\n",
        "from sklearn.linear_model import LogisticRegression\n",
        "from sklearn.metrics import accuracy_score, confusion_matrix, auc, roc_curve, RocCurveDisplay\n",
        "!pip install opacus\n",
        "from opacus import PrivacyEngine\n",
        "from tqdm import tqdm"
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "import matplotlib.pyplot as plt\n",
        "import seaborn as sns"
      ],
      "metadata": {
        "id": "mlwxoK1yhRcV"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "J6TZRvWt4U5P"
      },
      "outputs": [],
      "source": [
        "!mkdir models"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "YdWMUQlC4U5P"
      },
      "source": [
        "## Dataloaders"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "3yWnqEXE4U5P"
      },
      "outputs": [],
      "source": [
        "# Transform data to PyTorch tensors\n",
        "X_train, y_train = torch.Tensor(X_train), torch.Tensor(y_train)\n",
        "X_test, y_test = torch.Tensor(X_test), torch.Tensor(y_test)\n",
        "\n",
        "# Dataset hyperparameters\n",
        "batch_size = 256\n",
        "shuffle_train = True\n",
        "num_classes = y_test.shape[1]\n",
        "\n",
        "# Create PyTorch dataloaders\n",
        "train_dataloader = DataLoader(TensorDataset(X_train, y_train), batch_size=batch_size, shuffle=shuffle_train)\n",
        "test_dataloader = DataLoader(TensorDataset(X_test, y_test), batch_size=batch_size, shuffle=False)"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "B2eQAiAJ4U5P"
      },
      "source": [
        "## Generic Training and Testing Code"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "61ZdkbOx4U5P"
      },
      "outputs": [],
      "source": [
        "def train(args, model, device, train_loader, \n",
        "          optimizer, privacy_engine, epoch,\n",
        "          channel_format=None):\n",
        "    model.train(True)\n",
        "    criterion = nn.BCELoss()  # can't use cross entropy for multi-label\n",
        "    losses = []\n",
        "    for _batch_idx, (data, target) in enumerate(tqdm(train_loader)):\n",
        "        data, target = data.to(device), target.to(device)\n",
        "\n",
        "        if channel_format == 'channels_last':\n",
        "            data = torch.unsqueeze(data, 2)\n",
        "            data = data.permute(0, 2, 1)\n",
        "\n",
        "        optimizer.zero_grad()\n",
        "        output = model(data)\n",
        "        loss = criterion(output, target)\n",
        "\n",
        "        loss.backward()\n",
        "        optimizer.step()\n",
        "        losses.append(loss.item())\n",
        "\n",
        "    if not args[\"disable_dp\"]:\n",
        "        epsilon = privacy_engine.accountant.get_epsilon(delta=args['delta'])\n",
        "        print(\n",
        "            f\"Train Epoch: {epoch} \\t\"\n",
        "            f\"Loss: {np.mean(losses):.6f} \"\n",
        "            f\"(ε = {epsilon:.2f}, δ = {args['delta']})\"\n",
        "        )\n",
        "    else:\n",
        "        print(f\"Train Epoch: {epoch} \\t Loss: {np.mean(losses):.6f}\")\n",
        "\n",
        "    return np.mean(losses)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "gnOJk3CF4U5P"
      },
      "outputs": [],
      "source": [
        "def test(model, device, test_loader, channel_format=None):\n",
        "    model.eval()\n",
        "    criterion = nn.BCELoss()\n",
        "    losses = []\n",
        "    with torch.no_grad():\n",
        "        for data, target in tqdm(test_loader):\n",
        "            data, target = data.to(device), target.to(device)\n",
        "\n",
        "            if channel_format == 'channels_last':\n",
        "                data = torch.unsqueeze(data, 2)\n",
        "                data = data.permute(0, 2, 1)\n",
        "\n",
        "            output = model(data)\n",
        "            loss = criterion(output, target).item()\n",
        "            losses.append(loss)\n",
        "\n",
        "    test_loss = np.mean(losses)\n",
        "    print(f\"Test Loss: {test_loss:.6f}\")\n",
        "    return test_loss"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "whQyn6KeXe3x"
      },
      "outputs": [],
      "source": [
        "def compute_accuracy(model, device, dataloader, channel_format=None):\n",
        "    model.eval()\n",
        "    \n",
        "    num_correct = 0\n",
        "    num_samples = 0\n",
        "    with torch.no_grad():\n",
        "        for data, target in dataloader:\n",
        "            data = data.to(device)\n",
        "            target = target.to(device)\n",
        "\n",
        "            if channel_format == 'channels_last':\n",
        "                data = torch.unsqueeze(data, 2)\n",
        "                data = data.permute(0, 2, 1)\n",
        "            \n",
        "            scores = model(data)\n",
        "            \n",
        "            preds = scores > 0.5\n",
        "            num_correct += accuracy_score(target.to('cpu'), \n",
        "                                          preds.to('cpu'), \n",
        "                                          normalize=False)\n",
        "            num_samples += len(data)\n",
        "                \n",
        "    model.train(True)\n",
        "        \n",
        "    total_acc = num_correct/num_samples\n",
        "    print(\"Computed accuracy:\", total_acc)\n",
        "    return total_acc"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "4dTZyiZC4U5Q"
      },
      "outputs": [],
      "source": [
        "def plot_train_and_test_loss(model_id, train_losses, test_losses):\n",
        "    fig, ax = plt.subplots()\n",
        "    \n",
        "    ax.plot(train_losses, label='Train Loss')\n",
        "    ax.plot(test_losses, label='Test Loss')\n",
        "    \n",
        "    ax.set_title(f\"Model: {model_id}\")\n",
        "    ax.set_xlabel(\"Epoch\")\n",
        "    ax.set_ylabel(\"Loss\")\n",
        "    \n",
        "    ax.legend()\n",
        "    plt.show()"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "k4PgWa9YXe3y"
      },
      "outputs": [],
      "source": [
        "def plot_train_and_test_acc(model_id, train_accs, test_accs):\n",
        "    fig, ax = plt.subplots()\n",
        "    \n",
        "    ax.plot(train_accs, label='Train Acc')\n",
        "    ax.plot(test_accs, label='Test Acc')\n",
        "        \n",
        "    ax.set_title(f\"Model: {model_id}\")\n",
        "    ax.set_xlabel(\"Epoch\")\n",
        "    ax.set_ylabel(\"Acc\")\n",
        "    \n",
        "    ax.legend()\n",
        "    plt.show()"
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "## Generic Membership Inference Code"
      ],
      "metadata": {
        "id": "6aTBmUTcg2ue"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "def get_loss_values(model, device, dataloader, channels_format=None):\n",
        "    model.eval()\n",
        "    criterion = nn.BCELoss()\n",
        "\n",
        "    loss_values = []\n",
        "    with torch.no_grad():\n",
        "        for data, target in tqdm(dataloader):\n",
        "            data, target = data.to(device), target.to(device)\n",
        "\n",
        "            if channels_format == 'channels_last':\n",
        "                data = torch.unsqueeze(data, 2)\n",
        "                data = data.permute(0, 2, 1)\n",
        "\n",
        "            output = model(data)\n",
        "\n",
        "            for one_output, one_target in zip(target, output):\n",
        "                loss = criterion(one_output, one_target).item()\n",
        "                loss_values.append(loss)\n",
        "    \n",
        "    model.train(True)\n",
        "    return loss_values"
      ],
      "metadata": {
        "id": "5_-QwlJfg5nM"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "def plot_train_and_test_losses(train_loss_values, test_loss_values):\n",
        "    train_loss_values = np.array(train_loss_values)\n",
        "    test_loss_values = np.array(test_loss_values)\n",
        "\n",
        "    train_loss_values = train_loss_values[train_loss_values != 0]\n",
        "    test_loss_values = test_loss_values[test_loss_values != 0]\n",
        "\n",
        "    loss_value_types = ['Train'] * len(train_loss_values)\n",
        "    loss_value_types.extend(['Test'] * len(test_loss_values))\n",
        "    data_dict = {\n",
        "        'loss_values': np.concatenate([train_loss_values, test_loss_values]),\n",
        "        'Data': loss_value_types\n",
        "    }\n",
        "\n",
        "    fig, ax = plt.subplots()\n",
        "    sns.histplot(data=data_dict, \n",
        "                x='loss_values',\n",
        "                hue='Data',\n",
        "                stat=\"probability\",\n",
        "                # binwidth=0.01,\n",
        "                log_scale=True,\n",
        "                kde=True,\n",
        "                hue_order=['Train', 'Test'],\n",
        "                palette=['g', 'b'],\n",
        "                common_norm=False)\n",
        "    ax.set_xlabel('Loss Value')\n",
        "    ax.set_ylabel('Fraction')\n",
        "    plt.tight_layout()\n",
        "\n",
        "    plt.show()"
      ],
      "metadata": {
        "id": "3yigj6fkhmU2"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "def get_mia_model_roc_curve(train_loss_values, test_loss_values):\n",
        "    loss_values = np.concatenate(\n",
        "        [train_loss_values, test_loss_values]\n",
        "    ).reshape(-1, 1)\n",
        "    true_membership_labels = [1] * len(train_loss_values)\n",
        "    true_membership_labels.extend([0] * len(test_loss_values))\n",
        "\n",
        "    mia_attack_model = LogisticRegression(\n",
        "        class_weight='balanced'\n",
        "    );\n",
        "    mia_attack_model.fit(loss_values, true_membership_labels);\n",
        "\n",
        "    RocCurveDisplay.from_estimator(mia_attack_model, \n",
        "                                   loss_values,\n",
        "                                   true_membership_labels)\n",
        "    plt.show()"
      ],
      "metadata": {
        "id": "WcjBj_4gkfp-"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "s7kJj3qx4U5Q"
      },
      "source": [
        "## Simple NN"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "35miSxY94U5Q"
      },
      "outputs": [],
      "source": [
        "# Define a HAR model\n",
        "class SimpleNN(nn.Module):\n",
        "    def __init__(self, input_size, num_classes):\n",
        "        super(SimpleNN, self).__init__()\n",
        "        self.fc1 = nn.Linear(input_size, 256)\n",
        "        self.fc2 = nn.Linear(256, 128)\n",
        "        self.fc3 = nn.Linear(128, num_classes)\n",
        "        self.sigm = nn.Sigmoid()\n",
        "        self.dropout = nn.Dropout(0.2)\n",
        "\n",
        "    def forward(self, x):\n",
        "        x = F.relu(self.fc1(x))\n",
        "        x = self.dropout(x)\n",
        "        x = F.relu(self.fc2(x))\n",
        "        x = self.dropout(x)\n",
        "        x = self.fc3(x)\n",
        "        x = self.sigm(x)\n",
        "        return x"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "oyV3-tgA4U5Q"
      },
      "source": [
        "### Train Non-Private Simple NN"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "vWa52uo74U5Q",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "9cffe210-f677-4083-cd16-5c02c56eb59a"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "cuda\n"
          ]
        }
      ],
      "source": [
        "device = \"cuda\" if torch.cuda.is_available() else \"mps\" if torch.backends.mps.is_available() else \"cpu\"\n",
        "print(device)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "lKIJzws74U5Q"
      },
      "outputs": [],
      "source": [
        "# Define hyperparameters\n",
        "non_private_simple_nn_args = {\n",
        "    'epochs': 10,  # number of epochs for training model\n",
        "    'lr': 0.0001,  # learning rate for training model\n",
        "    'disable_dp': True,  # non-private training\n",
        "}\n",
        "\n",
        "input_size = X_train.shape[1]  # 115\n",
        "num_classes = y_train.shape[1]  # 51\n",
        "non_private_simple_nn_model = SimpleNN(input_size, num_classes).to(device)\n",
        "\n",
        "model_id = 'non_private_simple_nn'\n",
        "non_private_simple_nn_optimizer = optim.Adam(\n",
        "    non_private_simple_nn_model.parameters(), \n",
        "    lr=non_private_simple_nn_args['lr'], \n",
        ")\n",
        "privacy_engine = None"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "obeZm_Wb4U5Q",
        "outputId": "3dc07d3a-2d1b-4012-aaab-e520fffd4ff3"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "100%|██████████| 981/981 [00:05<00:00, 164.33it/s]\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Train Epoch: 1 \t Loss: 0.381142\n",
            "Computed accuracy: 0.6830805162092577\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "100%|██████████| 207/207 [00:00<00:00, 483.92it/s]\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Test Loss: 0.385225\n",
            "Computed accuracy: 0.6331294848249617\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "100%|██████████| 981/981 [00:03<00:00, 314.50it/s]\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Train Epoch: 2 \t Loss: 0.321780\n",
            "Computed accuracy: 0.703865192542227\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "100%|██████████| 207/207 [00:00<00:00, 483.82it/s]\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Test Loss: 0.385558\n",
            "Computed accuracy: 0.639472139652006\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "100%|██████████| 981/981 [00:03<00:00, 312.62it/s]\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Train Epoch: 3 \t Loss: 0.306419\n",
            "Computed accuracy: 0.7233465919507704\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "100%|██████████| 207/207 [00:00<00:00, 482.27it/s]\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Test Loss: 0.379655\n",
            "Computed accuracy: 0.6474809247022739\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "100%|██████████| 981/981 [00:03<00:00, 311.45it/s]\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Train Epoch: 4 \t Loss: 0.295928\n",
            "Computed accuracy: 0.7317760436179286\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "100%|██████████| 207/207 [00:00<00:00, 482.11it/s]\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Test Loss: 0.376478\n",
            "Computed accuracy: 0.6484843894958062\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "100%|██████████| 981/981 [00:03<00:00, 311.22it/s]\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Train Epoch: 5 \t Loss: 0.288152\n",
            "Computed accuracy: 0.7387029405434704\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "100%|██████████| 207/207 [00:00<00:00, 385.20it/s]\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Test Loss: 0.379813\n",
            "Computed accuracy: 0.6450763958573944\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "100%|██████████| 981/981 [00:03<00:00, 325.00it/s]\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Train Epoch: 6 \t Loss: 0.281599\n",
            "Computed accuracy: 0.7475349334013535\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "100%|██████████| 207/207 [00:00<00:00, 484.41it/s]\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Test Loss: 0.381884\n",
            "Computed accuracy: 0.6496393206732681\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "100%|██████████| 981/981 [00:03<00:00, 326.19it/s]\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Train Epoch: 7 \t Loss: 0.276495\n",
            "Computed accuracy: 0.7535730512622257\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "100%|██████████| 207/207 [00:00<00:00, 482.50it/s]\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Test Loss: 0.383786\n",
            "Computed accuracy: 0.6480678569400004\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "100%|██████████| 981/981 [00:03<00:00, 310.94it/s]\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Train Epoch: 8 \t Loss: 0.272193\n",
            "Computed accuracy: 0.7570763552884348\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "100%|██████████| 207/207 [00:00<00:00, 484.07it/s]\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Test Loss: 0.386603\n",
            "Computed accuracy: 0.6492985213094269\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "100%|██████████| 981/981 [00:03<00:00, 314.31it/s]\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Train Epoch: 9 \t Loss: 0.267935\n",
            "Computed accuracy: 0.7617633695487553\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "100%|██████████| 207/207 [00:00<00:00, 486.93it/s]\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Test Loss: 0.386682\n",
            "Computed accuracy: 0.6438078648919855\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "100%|██████████| 981/981 [00:03<00:00, 315.12it/s]\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Train Epoch: 10 \t Loss: 0.264342\n",
            "Computed accuracy: 0.7650554390887424\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "100%|██████████| 207/207 [00:00<00:00, 464.25it/s]\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Test Loss: 0.392326\n",
            "Computed accuracy: 0.6432777325482326\n"
          ]
        }
      ],
      "source": [
        "# Train and test model\n",
        "train_losses, test_losses = [], []\n",
        "train_accs, test_accs = [], []\n",
        "for epoch in range(1, non_private_simple_nn_args['epochs'] + 1):\n",
        "    train_loss = train(\n",
        "        non_private_simple_nn_args, \n",
        "        non_private_simple_nn_model, \n",
        "        device, \n",
        "        train_dataloader, \n",
        "        non_private_simple_nn_optimizer, \n",
        "        privacy_engine, \n",
        "        epoch\n",
        "    )\n",
        "    train_losses.append(train_loss)\n",
        "    \n",
        "    train_acc = compute_accuracy(\n",
        "        non_private_simple_nn_model, \n",
        "        device,\n",
        "        train_dataloader\n",
        "    )\n",
        "    train_accs.append(train_acc)\n",
        "    \n",
        "    test_loss = test(non_private_simple_nn_model, device, test_dataloader)\n",
        "    test_losses.append(test_loss)\n",
        "    \n",
        "    test_acc = compute_accuracy(\n",
        "        non_private_simple_nn_model, \n",
        "        device,\n",
        "        test_dataloader\n",
        "    )\n",
        "    test_accs.append(test_acc)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "FxkkFrE14U5Q"
      },
      "outputs": [],
      "source": [
        "torch.save({\n",
        "    'epoch': non_private_simple_nn_args['epochs'],\n",
        "    'model_state_dict': non_private_simple_nn_model.state_dict(),\n",
        "    'optimizer_state_dict': non_private_simple_nn_optimizer.state_dict(),\n",
        "}, f\"models/{model_id}\")"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 472
        },
        "id": "Jcg3-sXZ4U5Q",
        "outputId": "3cf2b496-77ac-4a7c-a855-d3a985b53f51"
      },
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 640x480 with 1 Axes>"
            ],
            "image/png": "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\n"
          },
          "metadata": {}
        }
      ],
      "source": [
        "plot_train_and_test_loss(model_id, train_losses, test_losses)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 472
        },
        "id": "Hsd2jl7QXe3y",
        "outputId": "23d05833-c5cd-4113-b0d1-1c4d68095556"
      },
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 640x480 with 1 Axes>"
            ],
            "image/png": "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\n"
          },
          "metadata": {}
        }
      ],
      "source": [
        "plot_train_and_test_acc(model_id, train_accs, test_accs)"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "5NDBdEJ74U5Q"
      },
      "source": [
        "### Membership Inference Attack on Non-Private Simple NN"
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "train_loss_values = get_loss_values(non_private_simple_nn_model, \n",
        "                                    device, train_dataloader)\n",
        "test_loss_values = get_loss_values(non_private_simple_nn_model, \n",
        "                                   device, test_dataloader)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "rNwG7ejokdsF",
        "outputId": "95fe8a79-a772-44cf-cde3-7d62399c0398"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "100%|██████████| 981/981 [00:15<00:00, 62.39it/s]\n",
            "100%|██████████| 207/207 [00:03<00:00, 63.07it/s]\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "plot_train_and_test_losses(train_loss_values, test_loss_values)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 487
        },
        "id": "0VwTH1m1nPp0",
        "outputId": "be12eaa0-a277-4c98-91d9-8d6a6062b9fe"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 640x480 with 1 Axes>"
            ],
            "image/png": "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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "get_mia_model_roc_curve(train_loss_values, test_loss_values)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 449
        },
        "id": "6LVc544cnNne",
        "outputId": "6ee191cb-dc37-4169-a277-a15e24685d5a"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 640x480 with 1 Axes>"
            ],
            "image/png": "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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "oQfKDrXL4U5Q"
      },
      "source": [
        "### Train Private Simple NN"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "pSVNTy_r4U5Q",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "42d5db1a-ebe9-4b6e-e4e6-8e1379f498fa"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "/usr/local/lib/python3.9/dist-packages/opacus/privacy_engine.py:142: UserWarning: Secure RNG turned off. This is perfectly fine for experimentation as it allows for much faster training performance, but remember to turn it on and retrain one last time before production with ``secure_mode`` turned on.\n",
            "  warnings.warn(\n",
            "/usr/local/lib/python3.9/dist-packages/opacus/accountants/analysis/rdp.py:332: UserWarning: Optimal order is the largest alpha. Please consider expanding the range of alphas to get a tighter privacy bound.\n",
            "  warnings.warn(\n",
            "/usr/local/lib/python3.9/dist-packages/opacus/accountants/analysis/prv/prvs.py:50: RuntimeWarning: invalid value encountered in log\n",
            "  z = np.log((np.exp(t) + q - 1) / q)\n"
          ]
        }
      ],
      "source": [
        "# Define hyperparameters\n",
        "private_simple_nn_args = {\n",
        "    'epochs': 10,  # number of epochs for training model\n",
        "    'lr': 0.0001,  # learning rate for training model\n",
        "    'disable_dp': False,  # non-private training,\n",
        "     # flag to enable secure RNG to have trustworthy privacy guarantees (comes at a performance cost)\n",
        "    'secure_rng': False,  \n",
        "    'epsilon': 0.125,\n",
        "    'delta': 1e-5,  # target delta for (eps, delta)-DP\n",
        "    'max_per_sample_grad_norm': 1.0  # per-sample clipping norm for DP-SGD\n",
        "}\n",
        "\n",
        "input_size = X_train.shape[1]\n",
        "num_classes = y_train.shape[1]\n",
        "private_simple_nn_model = SimpleNN(input_size, num_classes).to(device)\n",
        "\n",
        "model_id = 'private_simple_nn'\n",
        "private_simple_nn_optimizer = optim.Adam(\n",
        "    private_simple_nn_model.parameters(), \n",
        "    lr=private_simple_nn_args['lr'], \n",
        ")\n",
        "privacy_engine = PrivacyEngine(secure_mode=private_simple_nn_args['secure_rng'])\n",
        "\n",
        "private_simple_nn_model, private_simple_nn_optimizer, train_dataloader = privacy_engine.make_private_with_epsilon(\n",
        "    module=private_simple_nn_model,\n",
        "    optimizer=private_simple_nn_optimizer,\n",
        "    data_loader=train_dataloader,\n",
        "    target_epsilon=private_simple_nn_args['epsilon'],\n",
        "    target_delta=private_simple_nn_args['delta'],\n",
        "    epochs=private_simple_nn_args['epochs'],\n",
        "    max_grad_norm=private_simple_nn_args['max_per_sample_grad_norm'],\n",
        ")"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "4LZkSDBS4U5Q",
        "outputId": "adb121ae-01fd-4ef4-ec2a-5787b6475bc5"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "  0%|          | 0/980 [00:00<?, ?it/s]/usr/local/lib/python3.9/dist-packages/torch/nn/modules/module.py:1344: UserWarning: Using a non-full backward hook when the forward contains multiple autograd Nodes is deprecated and will be removed in future versions. This hook will be missing some grad_input. Please use register_full_backward_hook to get the documented behavior.\n",
            "  warnings.warn(\"Using a non-full backward hook when the forward contains multiple autograd Nodes \"\n",
            "100%|██████████| 980/980 [00:08<00:00, 113.08it/s]\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Train Epoch: 1 \tLoss: 0.548032 (ε = 0.04, δ = 1e-05)\n",
            "Computed accuracy: 0.3257381642280706\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "100%|██████████| 207/207 [00:00<00:00, 457.04it/s]\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Test Loss: 0.510165\n",
            "Computed accuracy: 0.2762557509892648\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "  0%|          | 0/980 [00:00<?, ?it/s]/usr/local/lib/python3.9/dist-packages/torch/nn/modules/module.py:1344: UserWarning: Using a non-full backward hook when the forward contains multiple autograd Nodes is deprecated and will be removed in future versions. This hook will be missing some grad_input. Please use register_full_backward_hook to get the documented behavior.\n",
            "  warnings.warn(\"Using a non-full backward hook when the forward contains multiple autograd Nodes \"\n",
            "100%|██████████| 980/980 [00:08<00:00, 110.32it/s]\n",
            "/usr/local/lib/python3.9/dist-packages/opacus/accountants/analysis/rdp.py:332: UserWarning: Optimal order is the largest alpha. Please consider expanding the range of alphas to get a tighter privacy bound.\n",
            "  warnings.warn(\n",
            "/usr/local/lib/python3.9/dist-packages/opacus/accountants/analysis/prv/prvs.py:50: RuntimeWarning: invalid value encountered in log\n",
            "  z = np.log((np.exp(t) + q - 1) / q)\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Train Epoch: 2 \tLoss: 0.472574 (ε = 0.06, δ = 1e-05)\n",
            "Computed accuracy: 0.5520259073961952\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "100%|██████████| 207/207 [00:00<00:00, 451.57it/s]\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Test Loss: 0.485796\n",
            "Computed accuracy: 0.5386712611469792\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "  0%|          | 0/980 [00:00<?, ?it/s]/usr/local/lib/python3.9/dist-packages/torch/nn/modules/module.py:1344: UserWarning: Using a non-full backward hook when the forward contains multiple autograd Nodes is deprecated and will be removed in future versions. This hook will be missing some grad_input. Please use register_full_backward_hook to get the documented behavior.\n",
            "  warnings.warn(\"Using a non-full backward hook when the forward contains multiple autograd Nodes \"\n",
            "100%|██████████| 980/980 [00:08<00:00, 110.15it/s]\n",
            "/usr/local/lib/python3.9/dist-packages/opacus/accountants/analysis/rdp.py:332: UserWarning: Optimal order is the largest alpha. Please consider expanding the range of alphas to get a tighter privacy bound.\n",
            "  warnings.warn(\n",
            "/usr/local/lib/python3.9/dist-packages/opacus/accountants/analysis/prv/prvs.py:50: RuntimeWarning: invalid value encountered in log\n",
            "  z = np.log((np.exp(t) + q - 1) / q)\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Train Epoch: 3 \tLoss: 0.466130 (ε = 0.07, δ = 1e-05)\n",
            "Computed accuracy: 0.5816832078121011\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "100%|██████████| 207/207 [00:00<00:00, 452.76it/s]\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Test Loss: 0.487602\n",
            "Computed accuracy: 0.5653672113145389\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "  0%|          | 0/980 [00:00<?, ?it/s]/usr/local/lib/python3.9/dist-packages/torch/nn/modules/module.py:1344: UserWarning: Using a non-full backward hook when the forward contains multiple autograd Nodes is deprecated and will be removed in future versions. This hook will be missing some grad_input. Please use register_full_backward_hook to get the documented behavior.\n",
            "  warnings.warn(\"Using a non-full backward hook when the forward contains multiple autograd Nodes \"\n",
            "100%|██████████| 980/980 [00:08<00:00, 109.81it/s]\n",
            "/usr/local/lib/python3.9/dist-packages/opacus/accountants/analysis/rdp.py:332: UserWarning: Optimal order is the largest alpha. Please consider expanding the range of alphas to get a tighter privacy bound.\n",
            "  warnings.warn(\n",
            "/usr/local/lib/python3.9/dist-packages/opacus/accountants/analysis/prv/prvs.py:50: RuntimeWarning: invalid value encountered in log\n",
            "  z = np.log((np.exp(t) + q - 1) / q)\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Train Epoch: 4 \tLoss: 0.467458 (ε = 0.08, δ = 1e-05)\n",
            "Computed accuracy: 0.6067097318574948\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "100%|██████████| 207/207 [00:00<00:00, 455.88it/s]\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Test Loss: 0.485802\n",
            "Computed accuracy: 0.5844330423916542\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "  0%|          | 0/980 [00:00<?, ?it/s]/usr/local/lib/python3.9/dist-packages/torch/nn/modules/module.py:1344: UserWarning: Using a non-full backward hook when the forward contains multiple autograd Nodes is deprecated and will be removed in future versions. This hook will be missing some grad_input. Please use register_full_backward_hook to get the documented behavior.\n",
            "  warnings.warn(\"Using a non-full backward hook when the forward contains multiple autograd Nodes \"\n",
            "100%|██████████| 980/980 [00:08<00:00, 111.99it/s]\n",
            "/usr/local/lib/python3.9/dist-packages/opacus/accountants/analysis/rdp.py:332: UserWarning: Optimal order is the largest alpha. Please consider expanding the range of alphas to get a tighter privacy bound.\n",
            "  warnings.warn(\n",
            "/usr/local/lib/python3.9/dist-packages/opacus/accountants/analysis/prv/prvs.py:50: RuntimeWarning: invalid value encountered in log\n",
            "  z = np.log((np.exp(t) + q - 1) / q)\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Train Epoch: 5 \tLoss: 0.461673 (ε = 0.08, δ = 1e-05)\n",
            "Computed accuracy: 0.6281911516298684\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "100%|██████████| 207/207 [00:00<00:00, 374.43it/s]\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Test Loss: 0.488786\n",
            "Computed accuracy: 0.5935210254274192\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "  0%|          | 0/980 [00:00<?, ?it/s]/usr/local/lib/python3.9/dist-packages/torch/nn/modules/module.py:1344: UserWarning: Using a non-full backward hook when the forward contains multiple autograd Nodes is deprecated and will be removed in future versions. This hook will be missing some grad_input. Please use register_full_backward_hook to get the documented behavior.\n",
            "  warnings.warn(\"Using a non-full backward hook when the forward contains multiple autograd Nodes \"\n",
            "100%|██████████| 980/980 [00:08<00:00, 110.72it/s]\n",
            "/usr/local/lib/python3.9/dist-packages/opacus/accountants/analysis/rdp.py:332: UserWarning: Optimal order is the largest alpha. Please consider expanding the range of alphas to get a tighter privacy bound.\n",
            "  warnings.warn(\n",
            "/usr/local/lib/python3.9/dist-packages/opacus/accountants/analysis/prv/prvs.py:50: RuntimeWarning: invalid value encountered in log\n",
            "  z = np.log((np.exp(t) + q - 1) / q)\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Train Epoch: 6 \tLoss: 0.456507 (ε = 0.09, δ = 1e-05)\n",
            "Computed accuracy: 0.6367117090112666\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "100%|██████████| 207/207 [00:00<00:00, 460.97it/s]\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Test Loss: 0.495009\n",
            "Computed accuracy: 0.5983679497131605\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "  0%|          | 0/980 [00:00<?, ?it/s]/usr/local/lib/python3.9/dist-packages/torch/nn/modules/module.py:1344: UserWarning: Using a non-full backward hook when the forward contains multiple autograd Nodes is deprecated and will be removed in future versions. This hook will be missing some grad_input. Please use register_full_backward_hook to get the documented behavior.\n",
            "  warnings.warn(\"Using a non-full backward hook when the forward contains multiple autograd Nodes \"\n",
            "100%|██████████| 980/980 [00:08<00:00, 111.09it/s]\n",
            "/usr/local/lib/python3.9/dist-packages/opacus/accountants/analysis/rdp.py:332: UserWarning: Optimal order is the largest alpha. Please consider expanding the range of alphas to get a tighter privacy bound.\n",
            "  warnings.warn(\n",
            "/usr/local/lib/python3.9/dist-packages/opacus/accountants/analysis/prv/prvs.py:50: RuntimeWarning: invalid value encountered in log\n",
            "  z = np.log((np.exp(t) + q - 1) / q)\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Train Epoch: 7 \tLoss: 0.459571 (ε = 0.10, δ = 1e-05)\n",
            "Computed accuracy: 0.6428921344108076\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "100%|██████████| 207/207 [00:00<00:00, 447.39it/s]\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Test Loss: 0.500856\n",
            "Computed accuracy: 0.6035556733627431\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "  0%|          | 0/980 [00:00<?, ?it/s]/usr/local/lib/python3.9/dist-packages/torch/nn/modules/module.py:1344: UserWarning: Using a non-full backward hook when the forward contains multiple autograd Nodes is deprecated and will be removed in future versions. This hook will be missing some grad_input. Please use register_full_backward_hook to get the documented behavior.\n",
            "  warnings.warn(\"Using a non-full backward hook when the forward contains multiple autograd Nodes \"\n",
            "100%|██████████| 980/980 [00:09<00:00, 108.59it/s]\n",
            "/usr/local/lib/python3.9/dist-packages/opacus/accountants/analysis/rdp.py:332: UserWarning: Optimal order is the largest alpha. Please consider expanding the range of alphas to get a tighter privacy bound.\n",
            "  warnings.warn(\n",
            "/usr/local/lib/python3.9/dist-packages/opacus/accountants/analysis/prv/prvs.py:50: RuntimeWarning: invalid value encountered in log\n",
            "  z = np.log((np.exp(t) + q - 1) / q)\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Train Epoch: 8 \tLoss: 0.460766 (ε = 0.11, δ = 1e-05)\n",
            "Computed accuracy: 0.6475852080564685\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "100%|██████████| 207/207 [00:00<00:00, 443.85it/s]\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Test Loss: 0.505260\n",
            "Computed accuracy: 0.6104095272355492\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "  0%|          | 0/980 [00:00<?, ?it/s]/usr/local/lib/python3.9/dist-packages/torch/nn/modules/module.py:1344: UserWarning: Using a non-full backward hook when the forward contains multiple autograd Nodes is deprecated and will be removed in future versions. This hook will be missing some grad_input. Please use register_full_backward_hook to get the documented behavior.\n",
            "  warnings.warn(\"Using a non-full backward hook when the forward contains multiple autograd Nodes \"\n",
            "100%|██████████| 980/980 [00:08<00:00, 111.57it/s]\n",
            "/usr/local/lib/python3.9/dist-packages/opacus/accountants/analysis/rdp.py:332: UserWarning: Optimal order is the largest alpha. Please consider expanding the range of alphas to get a tighter privacy bound.\n",
            "  warnings.warn(\n",
            "/usr/local/lib/python3.9/dist-packages/opacus/accountants/analysis/prv/prvs.py:50: RuntimeWarning: invalid value encountered in log\n",
            "  z = np.log((np.exp(t) + q - 1) / q)\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Train Epoch: 9 \tLoss: 0.461421 (ε = 0.11, δ = 1e-05)\n",
            "Computed accuracy: 0.652975831960527\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "100%|██████████| 207/207 [00:00<00:00, 461.03it/s]\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Test Loss: 0.510659\n",
            "Computed accuracy: 0.6176041804721965\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "  0%|          | 0/980 [00:00<?, ?it/s]/usr/local/lib/python3.9/dist-packages/torch/nn/modules/module.py:1344: UserWarning: Using a non-full backward hook when the forward contains multiple autograd Nodes is deprecated and will be removed in future versions. This hook will be missing some grad_input. Please use register_full_backward_hook to get the documented behavior.\n",
            "  warnings.warn(\"Using a non-full backward hook when the forward contains multiple autograd Nodes \"\n",
            "100%|██████████| 980/980 [00:08<00:00, 111.12it/s]\n",
            "/usr/local/lib/python3.9/dist-packages/opacus/accountants/analysis/rdp.py:332: UserWarning: Optimal order is the largest alpha. Please consider expanding the range of alphas to get a tighter privacy bound.\n",
            "  warnings.warn(\n",
            "/usr/local/lib/python3.9/dist-packages/opacus/accountants/analysis/prv/prvs.py:50: RuntimeWarning: invalid value encountered in log\n",
            "  z = np.log((np.exp(t) + q - 1) / q)\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Train Epoch: 10 \tLoss: 0.461098 (ε = 0.12, δ = 1e-05)\n",
            "Computed accuracy: 0.6568145023008765\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "100%|██████████| 207/207 [00:00<00:00, 460.23it/s]\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Test Loss: 0.511876\n",
            "Computed accuracy: 0.6237953689153114\n"
          ]
        }
      ],
      "source": [
        "# Train and test model\n",
        "train_losses, test_losses = [], []\n",
        "train_accs, test_accs = [], []\n",
        "for epoch in range(1, private_simple_nn_args['epochs'] + 1):\n",
        "    train_loss = train(\n",
        "        private_simple_nn_args, \n",
        "        private_simple_nn_model, \n",
        "        device, \n",
        "        train_dataloader, \n",
        "        private_simple_nn_optimizer, \n",
        "        privacy_engine, \n",
        "        epoch\n",
        "    )\n",
        "    train_losses.append(train_loss)\n",
        "\n",
        "    train_acc = compute_accuracy(\n",
        "        private_simple_nn_model, \n",
        "        device,\n",
        "        train_dataloader\n",
        "    )\n",
        "    train_accs.append(train_acc)\n",
        "    \n",
        "    test_loss = test(private_simple_nn_model, device, test_dataloader)\n",
        "    test_losses.append(test_loss)\n",
        "\n",
        "    test_acc = compute_accuracy(\n",
        "        private_simple_nn_model, \n",
        "        device,\n",
        "        test_dataloader\n",
        "    )\n",
        "    test_accs.append(test_acc)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "EPEd7S8b4U5Q"
      },
      "outputs": [],
      "source": [
        "torch.save({\n",
        "    'epoch': private_simple_nn_args['epochs'],\n",
        "    'model_state_dict': private_simple_nn_model.state_dict(),\n",
        "    'optimizer_state_dict': private_simple_nn_optimizer.state_dict(),\n",
        "}, f\"models/{model_id}\")"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 472
        },
        "id": "vAmfe1IG4U5Q",
        "outputId": "a026c26a-6ebc-497e-d2be-34ff99cdfefa"
      },
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 640x480 with 1 Axes>"
            ],
            "image/png": "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\n"
          },
          "metadata": {}
        }
      ],
      "source": [
        "plot_train_and_test_loss(model_id, train_losses, test_losses)"
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "plot_train_and_test_acc(model_id, train_accs, test_accs)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 472
        },
        "id": "IANewU91CvD2",
        "outputId": "55c8b636-4cac-4ee4-84ec-eb4e9ecef7ea"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 640x480 with 1 Axes>"
            ],
            "image/png": "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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "NERVTx4I4U5Q"
      },
      "source": [
        "### Membership Inference Attack on Private Simple NN"
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "train_loss_values = get_loss_values(private_simple_nn_model, \n",
        "                                    device, train_dataloader)\n",
        "test_loss_values = get_loss_values(private_simple_nn_model, \n",
        "                                   device, test_dataloader)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "1HYOL_8ap6dP",
        "outputId": "d31e363e-0fda-4d6b-ba4e-f7492a6c85f0"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "100%|██████████| 980/980 [00:18<00:00, 54.40it/s]\n",
            "100%|██████████| 207/207 [00:03<00:00, 62.53it/s]\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "plot_train_and_test_losses(train_loss_values, test_loss_values)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 487
        },
        "id": "-rbGi0uvnWZl",
        "outputId": "90bf6a36-f498-4759-f500-39d28261273b"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 640x480 with 1 Axes>"
            ],
            "image/png": "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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "get_mia_model_roc_curve(train_loss_values, test_loss_values)"
      ],
      "metadata": {
        "id": "mI3n_m7TnXue",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 449
        },
        "outputId": "f3e68832-4d3d-4a45-9f03-838ba4e207bb"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 640x480 with 1 Axes>"
            ],
            "image/png": "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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "gu4FDTt84U5Q"
      },
      "source": [
        "## CNN"
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# Define a HAR CNN model\n",
        "class CNN(nn.Module):\n",
        "    def __init__(self, input_size, num_classes):\n",
        "        super(CNN, self).__init__()\n",
        "        self.conv1 = nn.Conv1d(input_size, 256, 5)\n",
        "        self.conv2 = nn.Conv1d(256, 128, 5)\n",
        "        self.conv3 = nn.Conv1d(128, 64, 5)\n",
        "        self.flatten = nn.Flatten()\n",
        "        self.fc1 = nn.Linear(6592, 512)\n",
        "        self.fc2 = nn.Linear(512, 128)\n",
        "        self.fc3 = nn.Linear(128, num_classes)\n",
        "        self.sigm = nn.Sigmoid()\n",
        "        self.dropout = nn.Dropout(0.2)\n",
        "\n",
        "    def forward(self, x):\n",
        "        x = F.relu(self.conv1(x))\n",
        "        x = self.dropout(x)\n",
        "        x = F.relu(self.conv2(x))\n",
        "        x = self.dropout(x)\n",
        "        x = F.relu(self.conv3(x))\n",
        "        \n",
        "        x = self.flatten(x)\n",
        "\n",
        "        x = F.relu(self.fc1(x))\n",
        "        x = self.dropout(x)\n",
        "        x = F.relu(self.fc2(x))\n",
        "        x = self.dropout(x)\n",
        "        x = F.relu(self.fc3(x))\n",
        "        x = self.sigm(x)\n",
        "        return x"
      ],
      "metadata": {
        "id": "_mo_I_dRDOKx"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "### Train Non-Private CNN"
      ],
      "metadata": {
        "id": "avvhihpQFwl9"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "device = \"cuda\" if torch.cuda.is_available() else \"mps\" if torch.backends.mps.is_available() else \"cpu\"\n",
        "print(device)"
      ],
      "metadata": {
        "id": "xsO8icHYEpiT",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "9eb33a5d-f2e9-4aa8-f3da-d2e564afa87f"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "cuda\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# Define hyperparameters\n",
        "non_private_cnn_args = {\n",
        "    'epochs': 10,  # number of epochs for training model\n",
        "    'lr': 0.001,  # learning rate for training model\n",
        "    'disable_dp': True,  # non-private training\n",
        "}\n",
        "\n",
        "input_size = 1  # input_dim = 1 (1d feature)\n",
        "num_classes = y_train.shape[1]  # 51\n",
        "non_private_cnn_model = CNN(input_size, num_classes).to(device)\n",
        "\n",
        "model_id = 'non_private_cnn'\n",
        "non_private_cnn_optimizer = optim.Adam(\n",
        "    non_private_cnn_model.parameters(), \n",
        "    lr=non_private_cnn_args['lr'], \n",
        ")\n",
        "privacy_engine = None"
      ],
      "metadata": {
        "id": "5_VkvoikEqIb"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "# Train and test model\n",
        "train_losses, test_losses = [], []\n",
        "train_accs, test_accs = [], []\n",
        "for epoch in range(1, non_private_cnn_args['epochs'] + 1):\n",
        "    train_loss = train(\n",
        "        non_private_cnn_args, \n",
        "        non_private_cnn_model, \n",
        "        device, \n",
        "        train_dataloader, \n",
        "        non_private_cnn_optimizer, \n",
        "        privacy_engine, \n",
        "        epoch,\n",
        "        channel_format='channels_last'\n",
        "    )\n",
        "    train_losses.append(train_loss)\n",
        "    \n",
        "    train_acc = compute_accuracy(\n",
        "        non_private_cnn_model, \n",
        "        device,\n",
        "        train_dataloader,\n",
        "        channel_format='channels_last'\n",
        "    )\n",
        "    train_accs.append(train_acc)\n",
        "    \n",
        "    test_loss = test(non_private_cnn_model, \n",
        "                     device, \n",
        "                     test_dataloader,\n",
        "                     channel_format='channels_last'\n",
        "                     )\n",
        "    test_losses.append(test_loss)\n",
        "    \n",
        "    test_acc = compute_accuracy(\n",
        "        non_private_cnn_model, \n",
        "        device,\n",
        "        test_dataloader,\n",
        "        channel_format='channels_last'\n",
        "    )\n",
        "    test_accs.append(test_acc)"
      ],
      "metadata": {
        "id": "oiABICFUEtsJ",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 503
        },
        "outputId": "754882b4-719c-409a-d1ea-ce22e6704336"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "100%|██████████| 979/979 [00:07<00:00, 132.12it/s]\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Train Epoch: 1 \t Loss: 0.693147\n",
            "Computed accuracy: 0.14839870113332485\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "100%|██████████| 207/207 [00:00<00:00, 366.90it/s]\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Test Loss: 0.693147\n",
            "Computed accuracy: 0.11621258306984493\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            " 23%|██▎       | 224/979 [00:01<00:05, 125.96it/s]\n"
          ]
        },
        {
          "output_type": "error",
          "ename": "KeyboardInterrupt",
          "evalue": "ignored",
          "traceback": [
            "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
            "\u001b[0;31mKeyboardInterrupt\u001b[0m                         Traceback (most recent call last)",
            "\u001b[0;32m<ipython-input-78-9b084f7a1384>\u001b[0m in \u001b[0;36m<cell line: 4>\u001b[0;34m()\u001b[0m\n\u001b[1;32m      3\u001b[0m \u001b[0mtrain_accs\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mtest_accs\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;34m[\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m[\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m      4\u001b[0m \u001b[0;32mfor\u001b[0m \u001b[0mepoch\u001b[0m \u001b[0;32min\u001b[0m \u001b[0mrange\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;36m1\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mnon_private_cnn_args\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;34m'epochs'\u001b[0m\u001b[0;34m]\u001b[0m \u001b[0;34m+\u001b[0m \u001b[0;36m1\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m----> 5\u001b[0;31m     train_loss = train(\n\u001b[0m\u001b[1;32m      6\u001b[0m         \u001b[0mnon_private_cnn_args\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m      7\u001b[0m         \u001b[0mnon_private_cnn_model\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;32m<ipython-input-20-e181884a38ae>\u001b[0m in \u001b[0;36mtrain\u001b[0;34m(args, model, device, train_loader, optimizer, privacy_engine, epoch, channel_format)\u001b[0m\n\u001b[1;32m      5\u001b[0m     \u001b[0mcriterion\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mnn\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mBCELoss\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m  \u001b[0;31m# can't use cross entropy for multi-label\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m      6\u001b[0m     \u001b[0mlosses\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;34m[\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m----> 7\u001b[0;31m     \u001b[0;32mfor\u001b[0m \u001b[0m_batch_idx\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m(\u001b[0m\u001b[0mdata\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mtarget\u001b[0m\u001b[0;34m)\u001b[0m \u001b[0;32min\u001b[0m \u001b[0menumerate\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mtqdm\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mtrain_loader\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m      8\u001b[0m         \u001b[0mdata\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mtarget\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mdata\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mto\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mdevice\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mtarget\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mto\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mdevice\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m      9\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;32m/usr/local/lib/python3.9/dist-packages/tqdm/std.py\u001b[0m in \u001b[0;36m__iter__\u001b[0;34m(self)\u001b[0m\n\u001b[1;32m   1176\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1177\u001b[0m         \u001b[0;32mtry\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 1178\u001b[0;31m             \u001b[0;32mfor\u001b[0m \u001b[0mobj\u001b[0m \u001b[0;32min\u001b[0m \u001b[0miterable\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m   1179\u001b[0m                 \u001b[0;32myield\u001b[0m \u001b[0mobj\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1180\u001b[0m                 \u001b[0;31m# Update and possibly print the progressbar.\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;32m/usr/local/lib/python3.9/dist-packages/torch/utils/data/dataloader.py\u001b[0m in \u001b[0;36m__next__\u001b[0;34m(self)\u001b[0m\n\u001b[1;32m    632\u001b[0m                 \u001b[0;31m# TODO(https://github.com/pytorch/pytorch/issues/76750)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    633\u001b[0m                 \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_reset\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m  \u001b[0;31m# type: ignore[call-arg]\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 634\u001b[0;31m             \u001b[0mdata\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_next_data\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    635\u001b[0m             \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_num_yielded\u001b[0m \u001b[0;34m+=\u001b[0m \u001b[0;36m1\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    636\u001b[0m             \u001b[0;32mif\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_dataset_kind\u001b[0m \u001b[0;34m==\u001b[0m \u001b[0m_DatasetKind\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mIterable\u001b[0m \u001b[0;32mand\u001b[0m\u001b[0;31m \u001b[0m\u001b[0;31m\\\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;32m/usr/local/lib/python3.9/dist-packages/torch/utils/data/dataloader.py\u001b[0m in \u001b[0;36m_next_data\u001b[0;34m(self)\u001b[0m\n\u001b[1;32m    675\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    676\u001b[0m     \u001b[0;32mdef\u001b[0m \u001b[0m_next_data\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 677\u001b[0;31m         \u001b[0mindex\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_next_index\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m  \u001b[0;31m# may raise StopIteration\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    678\u001b[0m         \u001b[0mdata\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_dataset_fetcher\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mfetch\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mindex\u001b[0m\u001b[0;34m)\u001b[0m  \u001b[0;31m# may raise StopIteration\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    679\u001b[0m         \u001b[0;32mif\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_pin_memory\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;32m/usr/local/lib/python3.9/dist-packages/torch/utils/data/dataloader.py\u001b[0m in \u001b[0;36m_next_index\u001b[0;34m(self)\u001b[0m\n\u001b[1;32m    622\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    623\u001b[0m     \u001b[0;32mdef\u001b[0m \u001b[0m_next_index\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 624\u001b[0;31m         \u001b[0;32mreturn\u001b[0m \u001b[0mnext\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_sampler_iter\u001b[0m\u001b[0;34m)\u001b[0m  \u001b[0;31m# may raise StopIteration\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    625\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    626\u001b[0m     \u001b[0;32mdef\u001b[0m \u001b[0m_next_data\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;32m/usr/local/lib/python3.9/dist-packages/opacus/utils/uniform_sampler.py\u001b[0m in \u001b[0;36m__iter__\u001b[0;34m(self)\u001b[0m\n\u001b[1;32m     59\u001b[0m         \u001b[0;32mwhile\u001b[0m \u001b[0mnum_batches\u001b[0m \u001b[0;34m>\u001b[0m \u001b[0;36m0\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     60\u001b[0m             mask = (\n\u001b[0;32m---> 61\u001b[0;31m                 \u001b[0mtorch\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mrand\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mnum_samples\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mgenerator\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mgenerator\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m     62\u001b[0m                 \u001b[0;34m<\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0msample_rate\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     63\u001b[0m             )\n",
            "\u001b[0;31mKeyboardInterrupt\u001b[0m: "
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "torch.save({\n",
        "    'epoch': non_private_cnn_args['epochs'],\n",
        "    'model_state_dict': non_private_cnn_model.state_dict(),\n",
        "    'optimizer_state_dict': non_private_cnn_optimizer.state_dict(),\n",
        "}, f\"models/{model_id}\")"
      ],
      "metadata": {
        "id": "5SAU7_ZLFEy8"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "plot_train_and_test_loss(model_id, train_losses, test_losses)"
      ],
      "metadata": {
        "id": "zepYwTuaFNbJ",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 472
        },
        "outputId": "4ca4afa7-6d8b-41e9-bd47-1aa77d5e7032"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 640x480 with 1 Axes>"
            ],
            "image/png": "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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "plot_train_and_test_acc(model_id, train_accs, test_accs)"
      ],
      "metadata": {
        "id": "rWCf7thCFNzX",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 472
        },
        "outputId": "21348fd3-2303-4779-8f37-b5ec11efc505"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 640x480 with 1 Axes>"
            ],
            "image/png": "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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "### Membership Inference Attack on Non-Private CNN"
      ],
      "metadata": {
        "id": "uwwwknKwFaDP"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "train_loss_values = get_loss_values(non_private_cnn_model, \n",
        "                                    device, train_dataloader,\n",
        "                                    channels_format='channels_last')\n",
        "test_loss_values = get_loss_values(non_private_cnn_model, \n",
        "                                   device, test_dataloader,\n",
        "                                   channels_format='channels_last')"
      ],
      "metadata": {
        "id": "jywPHa3qFVSr",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "835d8813-a69f-47a1-f499-f9582053de1d"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "100%|██████████| 980/980 [00:18<00:00, 52.65it/s]\n",
            "100%|██████████| 207/207 [00:03<00:00, 59.28it/s]\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "plot_train_and_test_losses(train_loss_values, test_loss_values)"
      ],
      "metadata": {
        "id": "AUapcV8xFaaS",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 487
        },
        "outputId": "8ff9d540-d888-45ec-bf09-b1ae66e81a5a"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 640x480 with 1 Axes>"
            ],
            "image/png": "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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "get_mia_model_roc_curve(train_loss_values, test_loss_values)"
      ],
      "metadata": {
        "id": "gNAaFQO5HL0Z",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 449
        },
        "outputId": "605c1ce2-acda-484c-e3a9-6204f26fd47a"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 640x480 with 1 Axes>"
            ],
            "image/png": "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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "### Train Private CNN"
      ],
      "metadata": {
        "id": "I2Nak_EoF77_"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "# Define hyperparameters\n",
        "private_cnn_args = {\n",
        "    'epochs': 10,  # number of epochs for training model\n",
        "    'lr': 0.001,  # learning rate for training model\n",
        "    'disable_dp': False,  # non-private training,\n",
        "     # flag to enable secure RNG to have trustworthy privacy guarantees (comes at a performance cost)\n",
        "    'secure_rng': False,  \n",
        "    'epsilon': 0.125,\n",
        "    'delta': 1e-5,  # target delta for (eps, delta)-DP\n",
        "    'max_per_sample_grad_norm': 1.0  # per-sample clipping norm for DP-SGD\n",
        "}\n",
        "\n",
        "input_size = 1\n",
        "num_classes = y_train.shape[1]\n",
        "private_cnn_model = CNN(input_size, num_classes).to(device)\n",
        "\n",
        "model_id = 'private_simple_nn'\n",
        "private_cnn_optimizer = optim.Adam(\n",
        "    private_cnn_model.parameters(), \n",
        "    lr=private_cnn_args['lr'], \n",
        ")\n",
        "privacy_engine = PrivacyEngine(secure_mode=private_cnn_args['secure_rng'])\n",
        "\n",
        "private_cnn_model, private_cnn_optimizer, train_dataloader = privacy_engine.make_private_with_epsilon(\n",
        "    module=private_cnn_model,\n",
        "    optimizer=private_cnn_optimizer,\n",
        "    data_loader=train_dataloader,\n",
        "    target_epsilon=private_simple_nn_args['epsilon'],\n",
        "    target_delta=private_simple_nn_args['delta'],\n",
        "    epochs=private_simple_nn_args['epochs'],\n",
        "    max_grad_norm=private_cnn_args['max_per_sample_grad_norm'],\n",
        ")"
      ],
      "metadata": {
        "id": "AczbzYW7F8Dq"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "# Train and test model\n",
        "train_losses, test_losses = [], []\n",
        "train_accs, test_accs = [], []\n",
        "for epoch in range(1, private_cnn_args['epochs'] + 1):\n",
        "    train_loss = train(\n",
        "        private_cnn_args, \n",
        "        private_cnn_model, \n",
        "        device, \n",
        "        train_dataloader, \n",
        "        private_cnn_optimizer, \n",
        "        privacy_engine, \n",
        "        epoch,\n",
        "        channel_format='channels_last'\n",
        "    )\n",
        "    train_losses.append(train_loss)\n",
        "\n",
        "    train_acc = compute_accuracy(\n",
        "        private_cnn_model, \n",
        "        device,\n",
        "        train_dataloader,\n",
        "        channel_format='channels_last'\n",
        "    )\n",
        "    train_accs.append(train_acc)\n",
        "    \n",
        "    test_loss = test(private_cnn_model, \n",
        "                     device, \n",
        "                     test_dataloader,\n",
        "                     channel_format='channels_last')\n",
        "    test_losses.append(test_loss)\n",
        "\n",
        "    test_acc = compute_accuracy(\n",
        "        private_cnn_model, \n",
        "        device,\n",
        "        test_dataloader,\n",
        "        channel_format='channels_last'\n",
        "    )\n",
        "    test_accs.append(test_acc)"
      ],
      "metadata": {
        "id": "GuzUqp65Grz7",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "4c324d7a-d2c7-47aa-9144-836a44515f98"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "  0%|          | 0/979 [00:00<?, ?it/s]/usr/local/lib/python3.9/dist-packages/torch/nn/modules/module.py:1344: UserWarning: Using a non-full backward hook when the forward contains multiple autograd Nodes is deprecated and will be removed in future versions. This hook will be missing some grad_input. Please use register_full_backward_hook to get the documented behavior.\n",
            "  warnings.warn(\"Using a non-full backward hook when the forward contains multiple autograd Nodes \"\n",
            "100%|██████████| 979/979 [00:31<00:00, 30.64it/s]\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Train Epoch: 1 \tLoss: 0.693752 (ε = 0.04, δ = 1e-05)\n",
            "Computed accuracy: 0.40750028905536706\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "100%|██████████| 207/207 [00:00<00:00, 361.79it/s]\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Test Loss: 0.701422\n",
            "Computed accuracy: 0.4090349698013897\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "  0%|          | 0/979 [00:00<?, ?it/s]/usr/local/lib/python3.9/dist-packages/torch/nn/modules/module.py:1344: UserWarning: Using a non-full backward hook when the forward contains multiple autograd Nodes is deprecated and will be removed in future versions. This hook will be missing some grad_input. Please use register_full_backward_hook to get the documented behavior.\n",
            "  warnings.warn(\"Using a non-full backward hook when the forward contains multiple autograd Nodes \"\n",
            "100%|██████████| 979/979 [00:31<00:00, 31.11it/s]\n",
            "/usr/local/lib/python3.9/dist-packages/opacus/accountants/analysis/rdp.py:332: UserWarning: Optimal order is the largest alpha. Please consider expanding the range of alphas to get a tighter privacy bound.\n",
            "  warnings.warn(\n",
            "/usr/local/lib/python3.9/dist-packages/opacus/accountants/analysis/prv/prvs.py:50: RuntimeWarning: invalid value encountered in log\n",
            "  z = np.log((np.exp(t) + q - 1) / q)\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Train Epoch: 2 \tLoss: 0.757304 (ε = 0.06, δ = 1e-05)\n",
            "Computed accuracy: 0.4051109579160872\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "100%|██████████| 207/207 [00:00<00:00, 357.30it/s]\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Test Loss: 0.763567\n",
            "Computed accuracy: 0.3956491281216275\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "  0%|          | 0/979 [00:00<?, ?it/s]/usr/local/lib/python3.9/dist-packages/torch/nn/modules/module.py:1344: UserWarning: Using a non-full backward hook when the forward contains multiple autograd Nodes is deprecated and will be removed in future versions. This hook will be missing some grad_input. Please use register_full_backward_hook to get the documented behavior.\n",
            "  warnings.warn(\"Using a non-full backward hook when the forward contains multiple autograd Nodes \"\n",
            "100%|██████████| 979/979 [00:31<00:00, 31.21it/s]\n",
            "/usr/local/lib/python3.9/dist-packages/opacus/accountants/analysis/rdp.py:332: UserWarning: Optimal order is the largest alpha. Please consider expanding the range of alphas to get a tighter privacy bound.\n",
            "  warnings.warn(\n",
            "/usr/local/lib/python3.9/dist-packages/opacus/accountants/analysis/prv/prvs.py:50: RuntimeWarning: invalid value encountered in log\n",
            "  z = np.log((np.exp(t) + q - 1) / q)\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Train Epoch: 3 \tLoss: 0.779729 (ε = 0.07, δ = 1e-05)\n",
            "Computed accuracy: 0.3997471062352628\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "100%|██████████| 207/207 [00:00<00:00, 364.11it/s]\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Test Loss: 0.772598\n",
            "Computed accuracy: 0.3937557983225098\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "  0%|          | 0/979 [00:00<?, ?it/s]/usr/local/lib/python3.9/dist-packages/torch/nn/modules/module.py:1344: UserWarning: Using a non-full backward hook when the forward contains multiple autograd Nodes is deprecated and will be removed in future versions. This hook will be missing some grad_input. Please use register_full_backward_hook to get the documented behavior.\n",
            "  warnings.warn(\"Using a non-full backward hook when the forward contains multiple autograd Nodes \"\n",
            "100%|██████████| 979/979 [00:31<00:00, 31.05it/s]\n",
            "/usr/local/lib/python3.9/dist-packages/opacus/accountants/analysis/rdp.py:332: UserWarning: Optimal order is the largest alpha. Please consider expanding the range of alphas to get a tighter privacy bound.\n",
            "  warnings.warn(\n",
            "/usr/local/lib/python3.9/dist-packages/opacus/accountants/analysis/prv/prvs.py:50: RuntimeWarning: invalid value encountered in log\n",
            "  z = np.log((np.exp(t) + q - 1) / q)\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Train Epoch: 4 \tLoss: 0.823836 (ε = 0.08, δ = 1e-05)\n",
            "Computed accuracy: 0.6245726547050017\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "100%|██████████| 207/207 [00:00<00:00, 360.65it/s]\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Test Loss: 0.877735\n",
            "Computed accuracy: 0.5836946437699982\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "  0%|          | 0/979 [00:00<?, ?it/s]/usr/local/lib/python3.9/dist-packages/torch/nn/modules/module.py:1344: UserWarning: Using a non-full backward hook when the forward contains multiple autograd Nodes is deprecated and will be removed in future versions. This hook will be missing some grad_input. Please use register_full_backward_hook to get the documented behavior.\n",
            "  warnings.warn(\"Using a non-full backward hook when the forward contains multiple autograd Nodes \"\n",
            "100%|██████████| 979/979 [00:31<00:00, 30.84it/s]\n",
            "/usr/local/lib/python3.9/dist-packages/opacus/accountants/analysis/rdp.py:332: UserWarning: Optimal order is the largest alpha. Please consider expanding the range of alphas to get a tighter privacy bound.\n",
            "  warnings.warn(\n",
            "/usr/local/lib/python3.9/dist-packages/opacus/accountants/analysis/prv/prvs.py:50: RuntimeWarning: invalid value encountered in log\n",
            "  z = np.log((np.exp(t) + q - 1) / q)\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Train Epoch: 5 \tLoss: 0.871886 (ε = 0.08, δ = 1e-05)\n",
            "Computed accuracy: 0.6275637828888313\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "100%|██████████| 207/207 [00:00<00:00, 359.72it/s]\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Test Loss: 0.902305\n",
            "Computed accuracy: 0.5878410360300661\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "  0%|          | 0/979 [00:00<?, ?it/s]/usr/local/lib/python3.9/dist-packages/torch/nn/modules/module.py:1344: UserWarning: Using a non-full backward hook when the forward contains multiple autograd Nodes is deprecated and will be removed in future versions. This hook will be missing some grad_input. Please use register_full_backward_hook to get the documented behavior.\n",
            "  warnings.warn(\"Using a non-full backward hook when the forward contains multiple autograd Nodes \"\n",
            "100%|██████████| 979/979 [00:31<00:00, 31.19it/s]\n",
            "/usr/local/lib/python3.9/dist-packages/opacus/accountants/analysis/rdp.py:332: UserWarning: Optimal order is the largest alpha. Please consider expanding the range of alphas to get a tighter privacy bound.\n",
            "  warnings.warn(\n",
            "/usr/local/lib/python3.9/dist-packages/opacus/accountants/analysis/prv/prvs.py:50: RuntimeWarning: invalid value encountered in log\n",
            "  z = np.log((np.exp(t) + q - 1) / q)\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Train Epoch: 6 \tLoss: 0.908890 (ε = 0.09, δ = 1e-05)\n",
            "Computed accuracy: 0.6109871100943027\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "100%|██████████| 207/207 [00:00<00:00, 303.24it/s]\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Test Loss: 0.886042\n",
            "Computed accuracy: 0.5821989132286953\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "  0%|          | 0/979 [00:00<?, ?it/s]/usr/local/lib/python3.9/dist-packages/torch/nn/modules/module.py:1344: UserWarning: Using a non-full backward hook when the forward contains multiple autograd Nodes is deprecated and will be removed in future versions. This hook will be missing some grad_input. Please use register_full_backward_hook to get the documented behavior.\n",
            "  warnings.warn(\"Using a non-full backward hook when the forward contains multiple autograd Nodes \"\n",
            "100%|██████████| 979/979 [00:31<00:00, 31.07it/s]\n",
            "/usr/local/lib/python3.9/dist-packages/opacus/accountants/analysis/rdp.py:332: UserWarning: Optimal order is the largest alpha. Please consider expanding the range of alphas to get a tighter privacy bound.\n",
            "  warnings.warn(\n",
            "/usr/local/lib/python3.9/dist-packages/opacus/accountants/analysis/prv/prvs.py:50: RuntimeWarning: invalid value encountered in log\n",
            "  z = np.log((np.exp(t) + q - 1) / q)\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Train Epoch: 7 \tLoss: 0.959345 (ε = 0.10, δ = 1e-05)\n",
            "Computed accuracy: 0.6285825743583211\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "100%|██████████| 207/207 [00:00<00:00, 357.12it/s]\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Test Loss: 0.941695\n",
            "Computed accuracy: 0.5831645114262454\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "  0%|          | 0/979 [00:00<?, ?it/s]/usr/local/lib/python3.9/dist-packages/torch/nn/modules/module.py:1344: UserWarning: Using a non-full backward hook when the forward contains multiple autograd Nodes is deprecated and will be removed in future versions. This hook will be missing some grad_input. Please use register_full_backward_hook to get the documented behavior.\n",
            "  warnings.warn(\"Using a non-full backward hook when the forward contains multiple autograd Nodes \"\n",
            "100%|██████████| 979/979 [00:31<00:00, 31.24it/s]\n",
            "/usr/local/lib/python3.9/dist-packages/opacus/accountants/analysis/rdp.py:332: UserWarning: Optimal order is the largest alpha. Please consider expanding the range of alphas to get a tighter privacy bound.\n",
            "  warnings.warn(\n",
            "/usr/local/lib/python3.9/dist-packages/opacus/accountants/analysis/prv/prvs.py:50: RuntimeWarning: invalid value encountered in log\n",
            "  z = np.log((np.exp(t) + q - 1) / q)\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Train Epoch: 8 \tLoss: 0.994057 (ε = 0.11, δ = 1e-05)\n",
            "Computed accuracy: 0.6292602691599829\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "100%|██████████| 207/207 [00:00<00:00, 358.19it/s]\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Test Loss: 0.949354\n",
            "Computed accuracy: 0.5819338470568188\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "  0%|          | 0/979 [00:00<?, ?it/s]/usr/local/lib/python3.9/dist-packages/torch/nn/modules/module.py:1344: UserWarning: Using a non-full backward hook when the forward contains multiple autograd Nodes is deprecated and will be removed in future versions. This hook will be missing some grad_input. Please use register_full_backward_hook to get the documented behavior.\n",
            "  warnings.warn(\"Using a non-full backward hook when the forward contains multiple autograd Nodes \"\n",
            "100%|██████████| 979/979 [00:31<00:00, 31.31it/s]\n",
            "/usr/local/lib/python3.9/dist-packages/opacus/accountants/analysis/rdp.py:332: UserWarning: Optimal order is the largest alpha. Please consider expanding the range of alphas to get a tighter privacy bound.\n",
            "  warnings.warn(\n",
            "/usr/local/lib/python3.9/dist-packages/opacus/accountants/analysis/prv/prvs.py:50: RuntimeWarning: invalid value encountered in log\n",
            "  z = np.log((np.exp(t) + q - 1) / q)\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Train Epoch: 9 \tLoss: 1.049808 (ε = 0.11, δ = 1e-05)\n",
            "Computed accuracy: 0.6257238160899385\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "100%|██████████| 207/207 [00:00<00:00, 360.10it/s]\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Test Loss: 1.001575\n",
            "Computed accuracy: 0.5812333150311453\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "  0%|          | 0/979 [00:00<?, ?it/s]/usr/local/lib/python3.9/dist-packages/torch/nn/modules/module.py:1344: UserWarning: Using a non-full backward hook when the forward contains multiple autograd Nodes is deprecated and will be removed in future versions. This hook will be missing some grad_input. Please use register_full_backward_hook to get the documented behavior.\n",
            "  warnings.warn(\"Using a non-full backward hook when the forward contains multiple autograd Nodes \"\n",
            "100%|██████████| 979/979 [00:31<00:00, 30.98it/s]\n",
            "/usr/local/lib/python3.9/dist-packages/opacus/accountants/analysis/rdp.py:332: UserWarning: Optimal order is the largest alpha. Please consider expanding the range of alphas to get a tighter privacy bound.\n",
            "  warnings.warn(\n",
            "/usr/local/lib/python3.9/dist-packages/opacus/accountants/analysis/prv/prvs.py:50: RuntimeWarning: invalid value encountered in log\n",
            "  z = np.log((np.exp(t) + q - 1) / q)\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Train Epoch: 10 \tLoss: 1.149641 (ε = 0.12, δ = 1e-05)\n",
            "Computed accuracy: 0.5949330250358794\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "100%|██████████| 207/207 [00:00<00:00, 351.99it/s]\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Test Loss: 1.257234\n",
            "Computed accuracy: 0.5473427116269383\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "torch.save({\n",
        "    'epoch': private_cnn_args['epochs'],\n",
        "    'model_state_dict': private_cnn_model.state_dict(),\n",
        "    'optimizer_state_dict': private_cnn_optimizer.state_dict(),\n",
        "}, f\"models/{model_id}\")"
      ],
      "metadata": {
        "id": "bjB7iROwG8ja"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "plot_train_and_test_loss(model_id, train_losses, test_losses)"
      ],
      "metadata": {
        "id": "bgOEa55DG_UJ",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 472
        },
        "outputId": "4b28b01a-e204-471f-90b5-a6d9f9b3cf18"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 640x480 with 1 Axes>"
            ],
            "image/png": "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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "plot_train_and_test_acc(model_id, train_accs, test_accs)"
      ],
      "metadata": {
        "id": "vHAIUHLQHEfv",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 472
        },
        "outputId": "ec980fed-8401-44e4-ff04-c5d90b71cd9f"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 640x480 with 1 Axes>"
            ],
            "image/png": "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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "### Membership Inference Attack on Private CNN"
      ],
      "metadata": {
        "id": "JiEeZDzmHNOF"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "train_loss_values = get_loss_values(private_cnn_model, \n",
        "                                    device, train_dataloader,\n",
        "                                    channels_format='channels_last')\n",
        "test_loss_values = get_loss_values(private_cnn_model, \n",
        "                                   device, test_dataloader,\n",
        "                                   channels_format='channels_last')"
      ],
      "metadata": {
        "id": "vS94Z80aHQUR",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "818b88fc-449f-40ee-ccac-5e4e4c3baee2"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "100%|██████████| 979/979 [00:19<00:00, 50.49it/s]\n",
            "100%|██████████| 207/207 [00:03<00:00, 57.93it/s]\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "plot_train_and_test_losses(train_loss_values, test_loss_values)"
      ],
      "metadata": {
        "id": "uc38msIpHQ4F",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 486
        },
        "outputId": "86628cde-675f-45a9-bcb3-3f2ac0f9ba4e"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 640x480 with 1 Axes>"
            ],
            "image/png": "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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "get_mia_model_roc_curve(train_loss_values, test_loss_values)"
      ],
      "metadata": {
        "id": "IWzKZtBjHUr9",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 449
        },
        "outputId": "0b0c2e4c-d215-4734-a749-ba2b81b3ecc7"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 640x480 with 1 Axes>"
            ],
            "image/png": "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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "XqBs2R8d4U5R"
      },
      "source": [
        "## LSTM"
      ]
    }
  ],
  "metadata": {
    "accelerator": "GPU",
    "colab": {
      "provenance": [],
      "toc_visible": true,
      "machine_shape": "hm"
    },
    "gpuClass": "premium",
    "kernelspec": {
      "display_name": "Python 3 (ipykernel)",
      "language": "python",
      "name": "python3"
    },
    "language_info": {
      "codemirror_mode": {
        "name": "ipython",
        "version": 3
      },
      "file_extension": ".py",
      "mimetype": "text/x-python",
      "name": "python",
      "nbconvert_exporter": "python",
      "pygments_lexer": "ipython3",
      "version": "3.10.9"
    }
  },
  "nbformat": 4,
  "nbformat_minor": 0
}