NeuGuard_ALEXNET_cifar100.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {
    "lines_to_next_cell": 2
   },
   "outputs": [],
   "source": [
    "\n",
    "\n",
    "# coding: utf-8\n",
    "\n",
    "from __future__ import print_function\n",
    "\n",
    "import argparse\n",
    "import os\n",
    "os.environ[\"CUDA_DEVICE_ORDER\"]=\"PCI_BUS_ID\"   \n",
    "os.environ[\"CUDA_VISIBLE_DEVICES\"] = \"1\"\n",
    "import shutil\n",
    "import time\n",
    "import random\n",
    "\n",
    "import torch\n",
    "import torch.nn as nn\n",
    "import torch.nn.parallel\n",
    "import torch.backends.cudnn as cudnn\n",
    "import torch.optim as optim\n",
    "import torch.utils.data as data\n",
    "import torchvision.transforms as transforms\n",
    "import torchvision.datasets as datasets\n",
    "from utils import Bar, Logger, AverageMeter, accuracy, mkdir_p, savefig\n",
    "import numpy as np\n",
    "\n",
    "from matplotlib import pyplot\n",
    "from matplotlib.pylab import plt\n",
    "\n",
    "# from membership_inference_attacks import black_box_benchmarks\n",
    "from privacy_risk_score_utils import *"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {
    "lines_to_next_cell": 2
   },
   "outputs": [],
   "source": [
    "\n",
    "\n",
    "from sklearn.metrics import accuracy_score\n",
    "from sklearn.metrics import precision_score\n",
    "from sklearn.metrics import roc_curve\n",
    "\n",
    "from hop_skip_jump_attack import hop_skip_jump_attack"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {
    "lines_to_next_cell": 2
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "True"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "\n",
    "\n",
    "use_cuda = torch.cuda.is_available()\n",
    "# torch.cuda.set_device(2)\n",
    "use_cuda"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {
    "lines_to_next_cell": 2
   },
   "outputs": [],
   "source": [
    "\n",
    "\n",
    "manualSeed = random.randint(1, 10000)\n",
    "random.seed(manualSeed)\n",
    "torch.manual_seed(manualSeed)\n",
    "if use_cuda:\n",
    "    torch.cuda.manual_seed_all(manualSeed)\n",
    "\n",
    "global best_acc\n",
    "best_acc = 0  # best test accuracy"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {
    "lines_to_next_cell": 2
   },
   "outputs": [],
   "source": [
    "\n",
    "\n",
    "# class AlexNet(nn.Module):\n",
    "#     def __init__(self, num_classes=10):\n",
    "#         super(AlexNet, self).__init__()\n",
    "#         self.features = nn.Sequential(\n",
    "#             nn.Conv2d(3, 64, kernel_size=11, stride=4, padding=5),\n",
    "#             nn.ReLU(inplace=True),\n",
    "#             nn.MaxPool2d(kernel_size=2, stride=2),\n",
    "#             nn.Conv2d(64, 192, kernel_size=5, padding=2),\n",
    "#             nn.ReLU(inplace=True),\n",
    "#             nn.MaxPool2d(kernel_size=2, stride=2),\n",
    "#             nn.Conv2d(192, 384, kernel_size=3, padding=1),\n",
    "#             nn.ReLU(inplace=True),\n",
    "#             nn.Conv2d(384, 256, kernel_size=3, padding=1),\n",
    "#             nn.ReLU(inplace=True),\n",
    "#             nn.Conv2d(256, 256, kernel_size=3, padding=1),\n",
    "#             nn.ReLU(inplace=True),\n",
    "#             nn.MaxPool2d(kernel_size=2, stride=2),\n",
    "#         )\n",
    "#         self.classifier = nn.Linear(256, num_classes)\n",
    "#     def forward(self, x):\n",
    "#         x = self.features(x)\n",
    "#         x = x.view(x.size(0), -1)\n",
    "#         x = self.classifier(x)\n",
    "#         return x\n",
    "    \n",
    "# def alexnet(**kwargs):\n",
    "#     r\"\"\"AlexNet model architecture from the\n",
    "#     `\"One weird trick...\" <https://arxiv.org/abs/1404.5997>`_ paper.\n",
    "#     \"\"\"\n",
    "#     model = AlexNet(**kwargs)\n",
    "#     return model"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {
    "lines_to_next_cell": 2
   },
   "outputs": [],
   "source": [
    "\n",
    "\n",
    "class AlexNet(nn.Module):\n",
    "    def __init__(self, num_classes=10):\n",
    "        super(AlexNet, self).__init__()\n",
    "#         self.features = nn.Sequential(\n",
    "        self.conv1 = nn.Conv2d(3, 64, kernel_size=3, stride=2, padding=1)\n",
    "        self.re1 = nn.ReLU(inplace=True)\n",
    "        self.pool1 = nn.MaxPool2d(kernel_size=3, stride=2)\n",
    "        self.conv2 = nn.Conv2d(64, 192, kernel_size=3, stride=1, padding=1)\n",
    "        self.re2 = nn.ReLU(inplace=True)\n",
    "        self.pool2 = nn.MaxPool2d(kernel_size=3, stride=2)\n",
    "        self.conv3 = nn.Conv2d(192, 384, kernel_size=3,stride=1, padding=1)\n",
    "        self.re3 = nn.ReLU(inplace=True)\n",
    "        self.conv4 = nn.Conv2d(384, 256, kernel_size=3,stride=1, padding=1)\n",
    "        self.re4 = nn.ReLU(inplace=True)\n",
    "        self.conv5 = nn.Conv2d(256, 256, kernel_size=3,stride=1, padding=1)\n",
    "        self.re5 = nn.ReLU(inplace=True)\n",
    "        self.pool3 = nn.MaxPool2d(kernel_size=3, stride=2)\n",
    "#         )\n",
    "        \n",
    "#         self.classifier = nn.Sequential(\n",
    "        self.d1 = nn.Dropout()\n",
    "        self.lin1 = nn.Linear(256 * 1 * 1, 4096)\n",
    "        self.re6 = nn.ReLU(inplace=True)\n",
    "        self.d2 = nn.Dropout()\n",
    "        self.lin2 = nn.Linear(4096, 4096)\n",
    "        self.re7 = nn.ReLU(inplace=True)\n",
    "        self.lin3 = nn.Linear(4096, num_classes)\n",
    "#         )\n",
    "#         self.classifier = nn.Linear(256, num_classes)\n",
    "    def forward(self, x):\n",
    "        h1 = self.pool1(self.re1(self.conv1(x)))\n",
    "        h2 = self.pool2(self.re2(self.conv2(h1)))\n",
    "        h3 = self.re3(self.conv3(h2))\n",
    "        h4 = self.re4(self.conv4(h3))\n",
    "        h5 = self.pool3(self.re5(self.conv5(h4)))\n",
    "#         x = self.avgpool(x)\n",
    "#         x = x.view(x.size(0), -1)\n",
    "        x = torch.flatten(h5, 1)\n",
    "        h6 = self.re6(self.lin1(self.d1(x)))\n",
    "        h7 = self.re7(self.lin2(self.d2(h6)))\n",
    "        output = self.lin3(h7)\n",
    "                          \n",
    "        return output, h1, h2, h3, h4, h5, h6, h7\n",
    "\n",
    "def alexnet(**kwargs):\n",
    "    r\"\"\"AlexNet model architecture from the\n",
    "    `\"One weird trick...\" <https://arxiv.org/abs/1404.5997>`_ paper.\n",
    "    \"\"\"\n",
    "    model = AlexNet(**kwargs)\n",
    "    return model"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {
    "lines_to_next_cell": 2
   },
   "outputs": [],
   "source": [
    "\n",
    "\n",
    "class AlexNet_fc(nn.Module):\n",
    "    def __init__(self, num_classes=10, q = 100, alpha = 1):\n",
    "        super(AlexNet_fc, self).__init__()\n",
    "#         self.features = nn.Sequential(\n",
    "        self.conv1 = nn.Conv2d(3, 64, kernel_size=3, stride=2, padding=1)\n",
    "        self.re1 = nn.ReLU(inplace=True)\n",
    "        self.pool1 = nn.MaxPool2d(kernel_size=3, stride=2)\n",
    "        self.conv2 = nn.Conv2d(64, 192, kernel_size=3, stride=1, padding=1)\n",
    "        self.re2 = nn.ReLU(inplace=True)\n",
    "        self.pool2 = nn.MaxPool2d(kernel_size=3, stride=2)\n",
    "        self.conv3 = nn.Conv2d(192, 384, kernel_size=3,stride=1, padding=1)\n",
    "        self.re3 = nn.ReLU(inplace=True)\n",
    "        self.conv4 = nn.Conv2d(384, 256, kernel_size=3,stride=1, padding=1)\n",
    "        self.re4 = nn.ReLU(inplace=True)\n",
    "        self.conv5 = nn.Conv2d(256, 256, kernel_size=3,stride=1, padding=1)\n",
    "        self.re5 = nn.ReLU(inplace=True)\n",
    "        self.pool3 = nn.MaxPool2d(kernel_size=3, stride=2)\n",
    "#         )\n",
    "        \n",
    "#         self.classifier = nn.Sequential(\n",
    "        self.d1 = nn.Dropout()\n",
    "        self.lin1 = nn.Linear(256 * 1 * 1, 4096)\n",
    "        self.re6 = nn.ReLU(inplace=True)\n",
    "        self.d2 = nn.Dropout()\n",
    "        self.lin2 = nn.Linear(4096, 4096)\n",
    "        self.re7 = nn.ReLU(inplace=True)\n",
    "        self.lin3 = nn.Linear(4096, num_classes)\n",
    "        \n",
    "        self.q = q\n",
    "        self.alpha = alpha\n",
    "#         )\n",
    "#         self.classifier = nn.Linear(256, num_classes)\n",
    "\n",
    "    \n",
    "    def forward(self, x):\n",
    "        h1 = self.pool1(self.re1(self.conv1(x)))\n",
    "        h2 = self.pool2(self.re2(self.conv2(h1)))\n",
    "        h3 = self.re3(self.conv3(h2))\n",
    "        h4 = self.re4(self.conv4(h3))\n",
    "        h5 = self.pool3(self.re5(self.conv5(h4)))\n",
    "#         x = self.avgpool(x)\n",
    "#         x = x.view(x.size(0), -1)\n",
    "        x = torch.flatten(h5, 1)\n",
    "#         print('self.q: ',self.q, ' self.alpha: ',self.alpha )\n",
    "#         x = scale_by_percentage(x, q=self.q, alpha = self.alpha)\n",
    "        h6 = self.re6(self.lin1(self.d1(x)))\n",
    "        h6 = scale_by_percentage(h6, q=self.q, alpha = self.alpha)\n",
    "        h7 = self.re7(self.lin2(self.d2(h6)))\n",
    "        h7 = scale_by_percentage(h7, q=self.q, alpha = self.alpha)\n",
    "        output = self.lin3(h7)\n",
    "                          \n",
    "        return output, h1, h2, h3, h4, h5, h6, h7\n",
    "    \n",
    "def scale_by_percentage(x, q=5.0, alpha = 1):\n",
    "    \"\"\"\n",
    "    scale paramters by threshold.\n",
    "\n",
    "    \"\"\"\n",
    "    temp_shape = x.shape\n",
    "    weight = x.data.cpu().numpy()\n",
    "    flattened_weights = np.abs(weight.flatten())\n",
    "#     flattened_weights = np.abs(weight)\n",
    "#     percentile_value = np.percentile(flattened_weights, q)\n",
    "    nonzero = flattened_weights[np.nonzero(flattened_weights)]\n",
    "    percentile_value  = np.percentile(nonzero, q)\n",
    "    amp = alpha - 1.\n",
    "    \n",
    "    tweight = x.data\n",
    "    new_mask = (tweight >= percentile_value)*amp  + 1.\n",
    "    \n",
    "#     mask = np.ones(flattened_weights.shape)\n",
    "#     new_mask = np.where(flattened_weights >= percentile_value, alpha, mask)\n",
    "\n",
    "##     new_mask = (flattened_weights >= percentile_value)*amp + 1.\n",
    "\n",
    "#     new_mask = new_mask.reshape(temp_shape)\n",
    "#     new_feature_map = np.asarray(weight * new_mask, dtype=np.float32)\n",
    "#     x.data = torch.from_numpy(new_feature_map).cuda()\n",
    "    x.data  = new_mask * tweight\n",
    "    return x\n",
    "\n",
    "        \n",
    "def alexnet_fc(**kwargs):\n",
    "    r\"\"\"AlexNet model architecture from the\n",
    "    `\"One weird trick...\" <https://arxiv.org/abs/1404.5997>`_ paper.\n",
    "    \"\"\"\n",
    "    model = AlexNet_fc(**kwargs)\n",
    "    return model"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {
    "lines_to_next_cell": 2
   },
   "outputs": [],
   "source": [
    "\n",
    "\n",
    "class AlexNet_fc_replace(nn.Module):\n",
    "    def __init__(self, num_classes=10, q = 100, alpha = 1):\n",
    "        super(AlexNet_fc_replace, self).__init__()\n",
    "#         self.features = nn.Sequential(\n",
    "        self.conv1 = nn.Conv2d(3, 64, kernel_size=3, stride=2, padding=1)\n",
    "        self.re1 = nn.ReLU(inplace=True)\n",
    "        self.pool1 = nn.MaxPool2d(kernel_size=3, stride=2)\n",
    "        self.conv2 = nn.Conv2d(64, 192, kernel_size=3, stride=1, padding=1)\n",
    "        self.re2 = nn.ReLU(inplace=True)\n",
    "        self.pool2 = nn.MaxPool2d(kernel_size=3, stride=2)\n",
    "        self.conv3 = nn.Conv2d(192, 384, kernel_size=3,stride=1, padding=1)\n",
    "        self.re3 = nn.ReLU(inplace=True)\n",
    "        self.conv4 = nn.Conv2d(384, 256, kernel_size=3,stride=1, padding=1)\n",
    "        self.re4 = nn.ReLU(inplace=True)\n",
    "        self.conv5 = nn.Conv2d(256, 256, kernel_size=3,stride=1, padding=1)\n",
    "        self.re5 = nn.ReLU(inplace=True)\n",
    "        self.pool3 = nn.MaxPool2d(kernel_size=3, stride=2)\n",
    "#         )\n",
    "        \n",
    "#         self.classifier = nn.Sequential(\n",
    "        self.d1 = nn.Dropout()\n",
    "        self.lin1 = nn.Linear(256 * 1 * 1, 4096)\n",
    "        self.re6 = nn.ReLU(inplace=True)\n",
    "        self.d2 = nn.Dropout()\n",
    "        self.lin2 = nn.Linear(4096, 4096)\n",
    "        self.re7 = nn.ReLU(inplace=True)\n",
    "        self.lin3 = nn.Linear(4096, num_classes)\n",
    "        \n",
    "        self.q = q\n",
    "        self.alpha = alpha\n",
    "#         )\n",
    "#         self.classifier = nn.Linear(256, num_classes)\n",
    "\n",
    "    \n",
    "    def forward(self, x):\n",
    "        h1 = self.pool1(self.re1(self.conv1(x)))\n",
    "        h2 = self.pool2(self.re2(self.conv2(h1)))\n",
    "        h3 = self.re3(self.conv3(h2))\n",
    "        h4 = self.re4(self.conv4(h3))\n",
    "        h5 = self.pool3(self.re5(self.conv5(h4)))\n",
    "#         x = self.avgpool(x)\n",
    "#         x = x.view(x.size(0), -1)\n",
    "        x = torch.flatten(h5, 1)\n",
    "#         print('self.q: ',self.q, ' self.alpha: ',self.alpha )\n",
    "#         x = scale_by_percentage(x, q=self.q, alpha = self.alpha)\n",
    "        h6 = self.re6(self.lin1(self.d1(x)))\n",
    "        h6 = scale_by_percentage(h6, q=self.q, alpha = self.alpha)\n",
    "        h7 = self.re7(self.lin2(self.d2(h6)))\n",
    "        h7 = scale_by_percentage(h7, q=self.q, alpha = self.alpha)\n",
    "        output = self.lin3(h7)\n",
    "                          \n",
    "        return output, h1, h2, h3, h4, h5, h6, h7\n",
    "\n",
    "def scale_by_percentage(x, q=5.0, alpha = 1):\n",
    "    \"\"\"\n",
    "    scale paramters by threshold.\n",
    "\n",
    "    \"\"\"\n",
    "    temp_shape = x.shape\n",
    "    weight = x.data.cpu().numpy()\n",
    "    flattened_weights = np.abs(weight.flatten())\n",
    "    percentile_value = np.percentile(flattened_weights, q)\n",
    "    mask = np.ones(flattened_weights.shape)\n",
    "#     new_mask = np.where(flattened_weights >= percentile_value, alpha, mask)\n",
    "    new_mask = np.where(flattened_weights >= percentile_value, alpha, percentile_value)\n",
    "    new_feature_map = new_mask.reshape(temp_shape)\n",
    "    new_feature_map = np.asarray(new_feature_map, dtype=np.float32)\n",
    "    x.data = torch.from_numpy(new_feature_map).cuda()\n",
    "    return x\n",
    "\n",
    "        \n",
    "def alexNet_fc_replace(**kwargs):\n",
    "    r\"\"\"AlexNet model architecture from the\n",
    "    `\"One weird trick...\" <https://arxiv.org/abs/1404.5997>`_ paper.\n",
    "    \"\"\"\n",
    "    model = AlexNet_fc_replace(**kwargs)\n",
    "    return model"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {
    "lines_to_next_cell": 2
   },
   "outputs": [],
   "source": [
    "\n",
    "\n",
    "class AlexNet_conv(nn.Module):\n",
    "    def __init__(self, num_classes=10, q = 100, alpha = 1):\n",
    "        super(AlexNet_conv, self).__init__()\n",
    "#         self.features = nn.Sequential(\n",
    "        self.conv1 = nn.Conv2d(3, 64, kernel_size=3, stride=2, padding=1)\n",
    "        self.re1 = nn.ReLU(inplace=True)\n",
    "        self.pool1 = nn.MaxPool2d(kernel_size=3, stride=2)\n",
    "        self.conv2 = nn.Conv2d(64, 192, kernel_size=3, stride=1, padding=1)\n",
    "        self.re2 = nn.ReLU(inplace=True)\n",
    "        self.pool2 = nn.MaxPool2d(kernel_size=3, stride=2)\n",
    "        self.conv3 = nn.Conv2d(192, 384, kernel_size=3,stride=1, padding=1)\n",
    "        self.re3 = nn.ReLU(inplace=True)\n",
    "        self.conv4 = nn.Conv2d(384, 256, kernel_size=3,stride=1, padding=1)\n",
    "        self.re4 = nn.ReLU(inplace=True)\n",
    "        self.conv5 = nn.Conv2d(256, 256, kernel_size=3,stride=1, padding=1)\n",
    "        self.re5 = nn.ReLU(inplace=True)\n",
    "        self.pool3 = nn.MaxPool2d(kernel_size=3, stride=2)\n",
    "#         )\n",
    "        \n",
    "#         self.classifier = nn.Sequential(\n",
    "        self.d1 = nn.Dropout()\n",
    "        self.lin1 = nn.Linear(256 * 1 * 1, 4096)\n",
    "        self.re6 = nn.ReLU(inplace=True)\n",
    "        self.d2 = nn.Dropout()\n",
    "        self.lin2 = nn.Linear(4096, 4096)\n",
    "        self.re7 = nn.ReLU(inplace=True)\n",
    "        self.lin3 = nn.Linear(4096, num_classes)\n",
    "        \n",
    "        self.q = q\n",
    "        self.alpha = alpha\n",
    "#         )\n",
    "#         self.classifier = nn.Linear(256, num_classes)\n",
    "\n",
    "    \n",
    "    def forward(self, x):\n",
    "        h1 = self.pool1(self.re1(self.conv1(x)))\n",
    "        h1 = scale_by_percentage(h1, q=self.q, alpha = self.alpha)\n",
    "        h2 = self.pool2(self.re2(self.conv2(h1)))\n",
    "        h2 = scale_by_percentage(h2, q=self.q, alpha = self.alpha)\n",
    "        h3 = self.re3(self.conv3(h2))\n",
    "        h3 = scale_by_percentage(h3, q=self.q, alpha = self.alpha)\n",
    "        h4 = self.re4(self.conv4(h3))\n",
    "        h4 = scale_by_percentage(h4, q=self.q, alpha = self.alpha)\n",
    "        h5 = self.pool3(self.re5(self.conv5(h4)))\n",
    "#         h5 = scale_by_percentage(h5, q=self.q, alpha = self.alpha)\n",
    "#         x = self.avgpool(x)\n",
    "#         x = x.view(x.size(0), -1)\n",
    "        x = torch.flatten(h5, 1)\n",
    "#         print('self.q: ',self.q, ' self.alpha: ',self.alpha )\n",
    "        x = scale_by_percentage(x, q=self.q, alpha = self.alpha)\n",
    "        h6 = self.re6(self.lin1(self.d1(x)))\n",
    "#         h6 = scale_by_percentage(h6, q=self.q, alpha = self.alpha)\n",
    "        h7 = self.re7(self.lin2(self.d2(h6)))\n",
    "#         h7 = scale_by_percentage(h7, q=self.q, alpha = self.alpha)\n",
    "        output = self.lin3(h7)\n",
    "                          \n",
    "        return output, h1, h2, h3, h4, h5, h6, h7\n",
    "\n",
    "def scale_by_percentage(x, q=5.0, alpha = 1):\n",
    "    \"\"\"\n",
    "    scale paramters by threshold.\n",
    "\n",
    "    \"\"\"\n",
    "    temp_shape = x.shape\n",
    "    weight = x.data.cpu().numpy()\n",
    "    flattened_weights = np.abs(weight.flatten())\n",
    "    nonzero = flattened_weights[np.nonzero(flattened_weights)]\n",
    "    percentile_value = np.percentile(nonzero, q)\n",
    "#     percentile_value = np.percentile(flattened_weights, q)\n",
    "    mask = np.ones(flattened_weights.shape)\n",
    "    new_mask = np.where(flattened_weights >= percentile_value, alpha, mask)\n",
    "    new_mask = new_mask.reshape(temp_shape)\n",
    "    new_feature_map = np.asarray(weight * new_mask, dtype=np.float32)\n",
    "    x.data = torch.from_numpy(new_feature_map).cuda()\n",
    "    return x\n",
    "\n",
    "        \n",
    "def alexnet_conv(**kwargs):\n",
    "    r\"\"\"AlexNet model architecture from the\n",
    "    `\"One weird trick...\" <https://arxiv.org/abs/1404.5997>`_ paper.\n",
    "    \"\"\"\n",
    "    model = AlexNet_conv(**kwargs)\n",
    "    return model"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {
    "lines_to_next_cell": 2
   },
   "outputs": [],
   "source": [
    "\n",
    "\n",
    "class AlexNet_comb(nn.Module):\n",
    "    def __init__(self, num_classes=10, qconv = 100,qfc = 100, aconv = 1, afc = 1):\n",
    "        super(AlexNet_comb, self).__init__()\n",
    "#         self.features = nn.Sequential(\n",
    "        self.conv1 = nn.Conv2d(3, 64, kernel_size=3, stride=2, padding=1)\n",
    "        self.re1 = nn.ReLU(inplace=True)\n",
    "        self.pool1 = nn.MaxPool2d(kernel_size=3, stride=2)\n",
    "        self.conv2 = nn.Conv2d(64, 192, kernel_size=3, stride=1, padding=1)\n",
    "        self.re2 = nn.ReLU(inplace=True)\n",
    "        self.pool2 = nn.MaxPool2d(kernel_size=3, stride=2)\n",
    "        self.conv3 = nn.Conv2d(192, 384, kernel_size=3,stride=1, padding=1)\n",
    "        self.re3 = nn.ReLU(inplace=True)\n",
    "        self.conv4 = nn.Conv2d(384, 256, kernel_size=3,stride=1, padding=1)\n",
    "        self.re4 = nn.ReLU(inplace=True)\n",
    "        self.conv5 = nn.Conv2d(256, 256, kernel_size=3,stride=1, padding=1)\n",
    "        self.re5 = nn.ReLU(inplace=True)\n",
    "        self.pool3 = nn.MaxPool2d(kernel_size=3, stride=2)\n",
    "#         )\n",
    "        \n",
    "#         self.classifier = nn.Sequential(\n",
    "        self.d1 = nn.Dropout()\n",
    "        self.lin1 = nn.Linear(256 * 1 * 1, 4096)\n",
    "        self.re6 = nn.ReLU(inplace=True)\n",
    "        self.d2 = nn.Dropout()\n",
    "        self.lin2 = nn.Linear(4096, 4096)\n",
    "        self.re7 = nn.ReLU(inplace=True)\n",
    "        self.lin3 = nn.Linear(4096, num_classes)\n",
    "        \n",
    "        self.qconv = qconv\n",
    "        self.qfc = qfc\n",
    "        self.aconv = aconv\n",
    "        self.afc = afc\n",
    "#         )\n",
    "#         self.classifier = nn.Linear(256, num_classes)\n",
    "\n",
    "    \n",
    "    def forward(self, x):\n",
    "        h1 = self.pool1(self.re1(self.conv1(x)))\n",
    "        h1 = scale_by_percentage(h1, q=self.qconv, alpha = self.aconv)\n",
    "        h2 = self.pool2(self.re2(self.conv2(h1)))\n",
    "        h2 = scale_by_percentage(h2, q=self.qconv, alpha = self.aconv)\n",
    "        h3 = self.re3(self.conv3(h2))\n",
    "        h3 = scale_by_percentage(h3, q=self.qconv, alpha = self.aconv)\n",
    "        h4 = self.re4(self.conv4(h3))\n",
    "        h4 = scale_by_percentage(h4, q=self.qconv, alpha = self.aconv)\n",
    "        h5 = self.pool3(self.re5(self.conv5(h4)))\n",
    "#         h5 = scale_by_percentage(h5, q=self.q, alpha = self.alpha)\n",
    "#         x = self.avgpool(x)\n",
    "#         x = x.view(x.size(0), -1)\n",
    "        x = torch.flatten(h5, 1)\n",
    "#         print('self.q: ',self.q, ' self.alpha: ',self.alpha )\n",
    "        x = scale_by_percentage(x, q=self.qconv, alpha = self.aconv)\n",
    "#    ========================conv feature map output=================================\n",
    "        h6 = self.re6(self.lin1(self.d1(x)))\n",
    "        h6 = scale_by_percentage(h6, q=self.qfc, alpha = self.afc)\n",
    "        h7 = self.re7(self.lin2(self.d2(h6)))\n",
    "        h7 = scale_by_percentage(h7, q=self.qfc, alpha = self.afc)\n",
    "        output = self.lin3(h7)\n",
    "#    ========================fc feature map output=================================                          \n",
    "        return output, h1, h2, h3, h4, h5, h6, h7\n",
    "\n",
    "def scale_by_percentage(x, q=5.0, alpha = 1):\n",
    "    \"\"\"\n",
    "    scale paramters by threshold.\n",
    "\n",
    "    \"\"\"\n",
    "    temp_shape = x.shape\n",
    "    weight = x.data.cpu().numpy()\n",
    "    flattened_weights = np.abs(weight.flatten())\n",
    "    percentile_value = np.percentile(flattened_weights, q)\n",
    "    mask = np.ones(flattened_weights.shape)\n",
    "    new_mask = np.where(flattened_weights >= percentile_value, alpha, mask)\n",
    "    new_mask = new_mask.reshape(temp_shape)\n",
    "    new_feature_map = np.asarray(weight * new_mask, dtype=np.float32)\n",
    "    x.data = torch.from_numpy(new_feature_map).cuda()\n",
    "    return x\n",
    "\n",
    "        \n",
    "def alexnet_comb(**kwargs):\n",
    "    r\"\"\"AlexNet model architecture from the\n",
    "    `\"One weird trick...\" <https://arxiv.org/abs/1404.5997>`_ paper.\n",
    "    \"\"\"\n",
    "    model = AlexNet_comb(**kwargs)\n",
    "    return model"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {
    "lines_to_next_cell": 2
   },
   "outputs": [],
   "source": [
    "\n",
    "\n",
    "class AlexNet_comb_mid(nn.Module):\n",
    "    def __init__(self, num_classes=10, qconv_l = 100,qconv_h = 100,qfc_l = 100,qfc_h = 100, aconv_l = 1,aconv_h = 1, afc_l = 1, afc_h = 1):\n",
    "        super(AlexNet_comb_mid, self).__init__()\n",
    "#         self.features = nn.Sequential(\n",
    "        self.conv1 = nn.Conv2d(3, 64, kernel_size=3, stride=2, padding=1)\n",
    "        self.re1 = nn.ReLU(inplace=True)\n",
    "        self.pool1 = nn.MaxPool2d(kernel_size=3, stride=2)\n",
    "        self.conv2 = nn.Conv2d(64, 192, kernel_size=3, stride=1, padding=1)\n",
    "        self.re2 = nn.ReLU(inplace=True)\n",
    "        self.pool2 = nn.MaxPool2d(kernel_size=3, stride=2)\n",
    "        self.conv3 = nn.Conv2d(192, 384, kernel_size=3,stride=1, padding=1)\n",
    "        self.re3 = nn.ReLU(inplace=True)\n",
    "        self.conv4 = nn.Conv2d(384, 256, kernel_size=3,stride=1, padding=1)\n",
    "        self.re4 = nn.ReLU(inplace=True)\n",
    "        self.conv5 = nn.Conv2d(256, 256, kernel_size=3,stride=1, padding=1)\n",
    "        self.re5 = nn.ReLU(inplace=True)\n",
    "        self.pool3 = nn.MaxPool2d(kernel_size=3, stride=2)\n",
    "#         )\n",
    "        \n",
    "#         self.classifier = nn.Sequential(\n",
    "        self.d1 = nn.Dropout()\n",
    "        self.lin1 = nn.Linear(256 * 1 * 1, 4096)\n",
    "        self.re6 = nn.ReLU(inplace=True)\n",
    "        self.d2 = nn.Dropout()\n",
    "        self.lin2 = nn.Linear(4096, 4096)\n",
    "        self.re7 = nn.ReLU(inplace=True)\n",
    "        self.lin3 = nn.Linear(4096, num_classes)\n",
    "        \n",
    "        self.qconv_l = qconv_l\n",
    "        self.qconv_h = qconv_h\n",
    "\n",
    "        self.qfc_l = qfc_l\n",
    "        self.qfc_h = qfc_h\n",
    "        \n",
    "        self.aconv_l = aconv_l\n",
    "        self.aconv_h = aconv_h\n",
    "        self.afc_l = afc_l\n",
    "        self.afc_h= afc_h\n",
    "#         )\n",
    "#         self.classifier = nn.Linear(256, num_classes)\n",
    "\n",
    "    \n",
    "    def forward(self, x):\n",
    "        h1 = self.pool1(self.re1(self.conv1(x)))\n",
    "        h1 = scale_by_percentage_mid(h1, q_l=self.qconv_l, q_h=self.qconv_h, alpha_l = self.aconv_l, alpha_h = self.aconv_h)\n",
    "        h2 = self.pool2(self.re2(self.conv2(h1)))\n",
    "        h2 = scale_by_percentage_mid(h2, q_l=self.qconv_l, q_h=self.qconv_h, alpha_l = self.aconv_l, alpha_h = self.aconv_h)\n",
    "        h3 = self.re3(self.conv3(h2))\n",
    "        h3 = scale_by_percentage_mid(h3, q_l=self.qconv_l, q_h=self.qconv_h, alpha_l = self.aconv_l, alpha_h = self.aconv_h)\n",
    "        h4 = self.re4(self.conv4(h3))\n",
    "        h4 = scale_by_percentage_mid(h4, q_l=self.qconv_l, q_h=self.qconv_h, alpha_l = self.aconv_l, alpha_h = self.aconv_h)\n",
    "        h5 = self.pool3(self.re5(self.conv5(h4)))\n",
    "#         h5 = scale_by_percentage(h5, q=self.q, alpha = self.alpha)\n",
    "#         x = self.avgpool(x)\n",
    "#         x = x.view(x.size(0), -1)\n",
    "        x = torch.flatten(h5, 1)\n",
    "#         print('self.q: ',self.q, ' self.alpha: ',self.alpha )\n",
    "        x = scale_by_percentage_mid(x, q_l=self.qconv_l, q_h=self.qconv_h, alpha_l = self.aconv_l, alpha_h = self.aconv_h)\n",
    "#    ========================conv feature map output=================================\n",
    "        h6 = self.re6(self.lin1(self.d1(x)))\n",
    "        h6 = scale_by_percentage_mid(h6, q_l=self.qfc_l, q_h=self.qfc_h, alpha_l=self.afc_l, alpha_h=self.afc_h)\n",
    "        h7 = self.re7(self.lin2(self.d2(h6)))\n",
    "        h7 = scale_by_percentage_mid(h7, q_l=self.qfc_l, q_h=self.qfc_h, alpha_l=self.afc_l, alpha_h=self.afc_h)\n",
    "        output = self.lin3(h7)\n",
    "#    ========================fc feature map output=================================                          \n",
    "        return output, h1, h2, h3, h4, h5, h6, h7\n",
    "\n",
    "def scale_by_percentage(x, q=5.0, alpha = 1):\n",
    "    \"\"\"\n",
    "    scale paramters by threshold.\n",
    "\n",
    "    \"\"\"\n",
    "    temp_shape = x.shape\n",
    "    weight = x.data.cpu().numpy()\n",
    "    flattened_weights = np.abs(weight.flatten())\n",
    "    percentile_value = np.percentile(flattened_weights, q)\n",
    "    mask = np.ones(flattened_weights.shape)\n",
    "    new_mask = np.where(flattened_weights >= percentile_value, alpha, mask)\n",
    "    new_mask = new_mask.reshape(temp_shape)\n",
    "    new_feature_map = np.asarray(weight * new_mask, dtype=np.float32)\n",
    "    x.data = torch.from_numpy(new_feature_map).cuda()\n",
    "    return x\n",
    "\n",
    "def scale_by_percentage_mid(x, q_l=100, q_h=100, alpha_l=1, alpha_h=1):\n",
    "    \"\"\"\n",
    "    scale paramters by threshold.\n",
    "\n",
    "    \"\"\"\n",
    "    # print('q_l: ', q_l, 'q_h: ', q_h)\n",
    "    temp_shape = x.shape\n",
    "    weight = x.data.cpu().numpy()\n",
    "    flattened_weights = np.abs(weight.flatten())\n",
    "    nonzero = flattened_weights[np.nonzero(flattened_weights)]\n",
    "    p_value_low = np.percentile(nonzero, q_l)\n",
    "    p_value_high = np.percentile(nonzero, q_h)\n",
    "    # print('p_value_low: ', p_value_low, 'p_value_high: ',p_value_high)\n",
    "    mask = np.ones(flattened_weights.shape)\n",
    "    new_mask = np.where(flattened_weights >= p_value_low, alpha_l, mask)\n",
    "    # print(new_mask)\n",
    "    new_mask = np.where(flattened_weights >= p_value_high, alpha_h, new_mask)\n",
    "    # print(new_mask)\n",
    "    new_mask = new_mask.reshape(temp_shape)\n",
    "\n",
    "    new_feature_map = np.asarray(weight * new_mask, dtype=np.float32)\n",
    "    x.data = torch.from_numpy(new_feature_map).cuda()\n",
    "    return x\n",
    "\n",
    "\n",
    "# def scale_by_percentage_mid(x, q_l = 50, q_h = 90, alpha_l = 1, alpha_h = 1):\n",
    "#     \"\"\"\n",
    "#     scale paramters by threshold.\n",
    "\n",
    "#     \"\"\"\n",
    "# #     print('q_l: ', q_l, 'q_h: ', q_h)\n",
    "#     temp_shape = x.shape\n",
    "#     weight = x.data.cpu().numpy()\n",
    "#     flattened_weights = np.abs(weight.flatten())\n",
    "#     p_value_low = np.percentile(flattened_weights, q_l)\n",
    "#     p_value_high = np.percentile(flattened_weights, q_h)\n",
    "# #     print('p_value_low: ', p_value_low, 'p_value_high: ',p_value_high)\n",
    "#     mask = np.ones(flattened_weights.shape)\n",
    "#     new_mask = np.where(flattened_weights >= p_value_low, alpha_l, mask)\n",
    "# #     print(new_mask)\n",
    "#     new_mask = np.where(flattened_weights >= p_value_high, alpha_h, new_mask)\n",
    "# #     print(new_mask)\n",
    "#     new_mask = new_mask.reshape(temp_shape)\n",
    "    \n",
    "#     new_feature_map = np.asarray(weight * new_mask, dtype=np.float32)\n",
    "#     x.data = torch.from_numpy(new_feature_map).cuda()\n",
    "#     return x\n",
    "        \n",
    "def alexnet_comb_mid(**kwargs):\n",
    "    r\"\"\"AlexNet model architecture from the\n",
    "    `\"One weird trick...\" <https://arxiv.org/abs/1404.5997>`_ paper.\n",
    "    \"\"\"\n",
    "    model = AlexNet_comb_mid(**kwargs)\n",
    "    return model"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {
    "lines_to_next_cell": 2
   },
   "outputs": [],
   "source": [
    "\n",
    "\n",
    "class AlexNet_comb_rand(nn.Module):\n",
    "    def __init__(self, num_classes=10, qconv_l = 100,qconv_h = 100,qfc_l = 100,qfc_h = 100, aconv_l = 1,aconv_h = 1, afc_l = 1, afc_h = 1):\n",
    "        super(AlexNet_comb_rand, self).__init__()\n",
    "#         self.features = nn.Sequential(\n",
    "        self.conv1 = nn.Conv2d(3, 64, kernel_size=3, stride=2, padding=1)\n",
    "        self.re1 = nn.ReLU(inplace=True)\n",
    "        self.pool1 = nn.MaxPool2d(kernel_size=3, stride=2)\n",
    "        self.conv2 = nn.Conv2d(64, 192, kernel_size=3, stride=1, padding=1)\n",
    "        self.re2 = nn.ReLU(inplace=True)\n",
    "        self.pool2 = nn.MaxPool2d(kernel_size=3, stride=2)\n",
    "        self.conv3 = nn.Conv2d(192, 384, kernel_size=3,stride=1, padding=1)\n",
    "        self.re3 = nn.ReLU(inplace=True)\n",
    "        self.conv4 = nn.Conv2d(384, 256, kernel_size=3,stride=1, padding=1)\n",
    "        self.re4 = nn.ReLU(inplace=True)\n",
    "        self.conv5 = nn.Conv2d(256, 256, kernel_size=3,stride=1, padding=1)\n",
    "        self.re5 = nn.ReLU(inplace=True)\n",
    "        self.pool3 = nn.MaxPool2d(kernel_size=3, stride=2)\n",
    "#         )\n",
    "        \n",
    "#         self.classifier = nn.Sequential(\n",
    "        self.d1 = nn.Dropout()\n",
    "        self.lin1 = nn.Linear(256 * 1 * 1, 4096)\n",
    "        self.re6 = nn.ReLU(inplace=True)\n",
    "        self.d2 = nn.Dropout()\n",
    "        self.lin2 = nn.Linear(4096, 4096)\n",
    "        self.re7 = nn.ReLU(inplace=True)\n",
    "        self.lin3 = nn.Linear(4096, num_classes)\n",
    "        \n",
    "        self.qconv_l = qconv_l\n",
    "        self.qconv_h = qconv_h\n",
    "\n",
    "        self.qfc_l = qfc_l\n",
    "        self.qfc_h = qfc_h\n",
    "        \n",
    "        self.aconv_l = aconv_l\n",
    "        self.aconv_h = aconv_h\n",
    "        self.afc_l = afc_l\n",
    "        self.afc_h= afc_h\n",
    "#         )\n",
    "#         self.classifier = nn.Linear(256, num_classes)\n",
    "\n",
    "    \n",
    "    def forward(self, x):\n",
    "        h1 = self.pool1(self.re1(self.conv1(x)))\n",
    "        h1 = scale_by_percentage_mid(h1, q_l=self.qconv_l, q_h=self.qconv_h, alpha_l = self.aconv_l, alpha_h = self.aconv_h)\n",
    "        h2 = self.pool2(self.re2(self.conv2(h1)))\n",
    "        h2 = scale_by_percentage_mid(h2, q_l=self.qconv_l, q_h=self.qconv_h, alpha_l = self.aconv_l, alpha_h = self.aconv_h)\n",
    "        h3 = self.re3(self.conv3(h2))\n",
    "        h3 = scale_by_percentage_mid(h3, q_l=self.qconv_l, q_h=self.qconv_h, alpha_l = self.aconv_l, alpha_h = self.aconv_h)\n",
    "        h4 = self.re4(self.conv4(h3))\n",
    "        h4 = scale_by_percentage_mid(h4, q_l=self.qconv_l, q_h=self.qconv_h, alpha_l = self.aconv_l, alpha_h = self.aconv_h)\n",
    "        h5 = self.pool3(self.re5(self.conv5(h4)))\n",
    "#         h5 = scale_by_percentage(h5, q=self.q, alpha = self.alpha)\n",
    "#         x = self.avgpool(x)\n",
    "#         x = x.view(x.size(0), -1)\n",
    "        x = torch.flatten(h5, 1)\n",
    "#         print('self.q: ',self.q, ' self.alpha: ',self.alpha )\n",
    "        x = scale_by_percentage_mid(x, q_l=self.qconv_l, q_h=self.qconv_h, alpha_l = self.aconv_l, alpha_h = self.aconv_h)\n",
    "    \n",
    "#    ========================conv feature map output=================================\n",
    "        h6 = self.re6(self.lin1(self.d1(x)))\n",
    "#         h6 = scale_by_percentage_mid(h6, q_l=self.qfc_l, q_h=self.qfc_h, alpha_l=self.afc_l, alpha_h=self.afc_h)\n",
    "        h6 = scale_rand(h6)\n",
    "        h7 = self.re7(self.lin2(self.d2(h6)))\n",
    "#         h7 = scale_by_percentage_mid(h7, q_l=self.qfc_l, q_h=self.qfc_h, alpha_l=self.afc_l, alpha_h=self.afc_h)\n",
    "        h7 = scale_rand(h7)\n",
    "        output = self.lin3(h7)\n",
    "#    ========================fc feature map output=================================                          \n",
    "        return output, h1, h2, h3, h4, h5, h6, h7\n",
    "\n",
    "def scale_by_percentage(x, q=5.0, alpha = 1):\n",
    "    \"\"\"\n",
    "    scale paramters by threshold.\n",
    "\n",
    "    \"\"\"\n",
    "    temp_shape = x.shape\n",
    "    weight = x.data.cpu().numpy()\n",
    "    flattened_weights = np.abs(weight.flatten())\n",
    "    percentile_value = np.percentile(flattened_weights, q)\n",
    "    mask = np.ones(flattened_weights.shape)\n",
    "    new_mask = np.where(flattened_weights >= percentile_value, alpha, mask)\n",
    "    new_mask = new_mask.reshape(temp_shape)\n",
    "    new_feature_map = np.asarray(weight * new_mask, dtype=np.float32)\n",
    "    x.data = torch.from_numpy(new_feature_map).cuda()\n",
    "    return x\n",
    "\n",
    "def scale_rand(x):\n",
    "    \"\"\"\n",
    "    scale paramters by threshold.\n",
    "\n",
    "    \"\"\"\n",
    "#     temp_shape = x.shape\n",
    "    rand = torch.rand(x.size()).cuda() + 0.5\n",
    "    x = x * rand\n",
    "#     weight = x.data.cpu().numpy()\n",
    "#     flattened_weights = np.abs(weight.flatten())\n",
    "#     percentile_value = np.percentile(flattened_weights, q)\n",
    "#     mask = np.ones(flattened_weights.shape)\n",
    "#     new_mask = np.where(flattened_weights >= percentile_value, alpha, mask)\n",
    "#     new_mask = new_mask.reshape(temp_shape)\n",
    "#     new_feature_map = np.asarray(weight * new_mask, dtype=np.float32)\n",
    "#     x.data = torch.from_numpy(new_feature_map).cuda()\n",
    "    return x\n",
    "\n",
    "def scale_by_percentage_mid(x, q_l = 50, q_h = 90, alpha_l = 1, alpha_h = 1):\n",
    "    \"\"\"\n",
    "    scale paramters by threshold.\n",
    "\n",
    "    \"\"\"\n",
    "#     print('q_l: ', q_l, 'q_h: ', q_h)\n",
    "    temp_shape = x.shape\n",
    "    weight = x.data.cpu().numpy()\n",
    "    flattened_weights = np.abs(weight.flatten())\n",
    "    p_value_low = np.percentile(flattened_weights, q_l)\n",
    "    p_value_high = np.percentile(flattened_weights, q_h)\n",
    "#     print('p_value_low: ', p_value_low, 'p_value_high: ',p_value_high)\n",
    "    mask = np.ones(flattened_weights.shape)\n",
    "    new_mask = np.where(flattened_weights >= p_value_low, alpha_l, mask)\n",
    "#     print(new_mask)\n",
    "    new_mask = np.where(flattened_weights >= p_value_high, alpha_h, new_mask)\n",
    "#     print(new_mask)\n",
    "    new_mask = new_mask.reshape(temp_shape)\n",
    "    \n",
    "    new_feature_map = np.asarray(weight * new_mask, dtype=np.float32)\n",
    "    x.data = torch.from_numpy(new_feature_map).cuda()\n",
    "    return x\n",
    "        \n",
    "def alexnet_comb_rand(**kwargs):\n",
    "    r\"\"\"AlexNet model architecture from the\n",
    "    `\"One weird trick...\" <https://arxiv.org/abs/1404.5997>`_ paper.\n",
    "    \"\"\"\n",
    "    model = AlexNet_comb_rand(**kwargs)\n",
    "    return model"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {
    "lines_to_next_cell": 2
   },
   "outputs": [],
   "source": [
    "\n",
    "\n",
    "class AlexNet_comb_rand_mod1(nn.Module):\n",
    "    def __init__(self, num_classes=10, qconv_l = 100,qconv_h = 100,qfc_l = 100,qfc_h = 100, aconv_l = 1,aconv_h = 1, afc_l = 1, afc_h = 1):\n",
    "        super(AlexNet_comb_rand_mod1, self).__init__()\n",
    "#         self.features = nn.Sequential(\n",
    "        self.conv1 = nn.Conv2d(3, 64, kernel_size=3, stride=2, padding=1)\n",
    "        self.re1 = nn.ReLU(inplace=True)\n",
    "        self.pool1 = nn.MaxPool2d(kernel_size=3, stride=2)\n",
    "        self.conv2 = nn.Conv2d(64, 192, kernel_size=3, stride=1, padding=1)\n",
    "        self.re2 = nn.ReLU(inplace=True)\n",
    "        self.pool2 = nn.MaxPool2d(kernel_size=3, stride=2)\n",
    "        self.conv3 = nn.Conv2d(192, 384, kernel_size=3,stride=1, padding=1)\n",
    "        self.re3 = nn.ReLU(inplace=True)\n",
    "        self.conv4 = nn.Conv2d(384, 256, kernel_size=3,stride=1, padding=1)\n",
    "        self.re4 = nn.ReLU(inplace=True)\n",
    "        self.conv5 = nn.Conv2d(256, 256, kernel_size=3,stride=1, padding=1)\n",
    "        self.re5 = nn.ReLU(inplace=True)\n",
    "        self.pool3 = nn.MaxPool2d(kernel_size=3, stride=2)\n",
    "#         )\n",
    "        \n",
    "#         self.classifier = nn.Sequential(\n",
    "        self.d1 = nn.Dropout()\n",
    "        self.lin1 = nn.Linear(256 * 1 * 1, 4096)\n",
    "        self.re6 = nn.ReLU(inplace=True)\n",
    "        self.d2 = nn.Dropout()\n",
    "        self.lin2 = nn.Linear(4096, 4096)\n",
    "        self.re7 = nn.ReLU(inplace=True)\n",
    "        self.lin3 = nn.Linear(4096, num_classes)\n",
    "        \n",
    "        self.qconv_l = qconv_l\n",
    "        self.qconv_h = qconv_h\n",
    "\n",
    "        self.qfc_l = qfc_l\n",
    "        self.qfc_h = qfc_h\n",
    "        \n",
    "        self.aconv_l = aconv_l\n",
    "        self.aconv_h = aconv_h\n",
    "        self.afc_l = afc_l\n",
    "        self.afc_h= afc_h\n",
    "#         )\n",
    "#         self.classifier = nn.Linear(256, num_classes)\n",
    "\n",
    "    \n",
    "    def forward(self, x):\n",
    "        h1 = self.pool1(self.re1(self.conv1(x)))\n",
    "        h1 = scale_by_percentage_mid(h1, q_l=self.qconv_l, q_h=self.qconv_h, alpha_l = self.aconv_l, alpha_h = self.aconv_h)\n",
    "        h2 = self.pool2(self.re2(self.conv2(h1)))\n",
    "        h2 = scale_by_percentage_mid(h2, q_l=self.qconv_l, q_h=self.qconv_h, alpha_l = self.aconv_l, alpha_h = self.aconv_h)\n",
    "        h3 = self.re3(self.conv3(h2))\n",
    "        h3 = scale_by_percentage_mid(h3, q_l=self.qconv_l, q_h=self.qconv_h, alpha_l = self.aconv_l, alpha_h = self.aconv_h)\n",
    "        h4 = self.re4(self.conv4(h3))\n",
    "        h4 = scale_by_percentage_mid(h4, q_l=self.qconv_l, q_h=self.qconv_h, alpha_l = self.aconv_l, alpha_h = self.aconv_h)\n",
    "        h5 = self.pool3(self.re5(self.conv5(h4)))\n",
    "#         h5 = scale_by_percentage(h5, q=self.q, alpha = self.alpha)\n",
    "#         x = self.avgpool(x)\n",
    "#         x = x.view(x.size(0), -1)\n",
    "        x = torch.flatten(h5, 1)\n",
    "#         print('self.q: ',self.q, ' self.alpha: ',self.alpha )\n",
    "#         x = scale_by_percentage_mid(x, q_l=self.qconv_l, q_h=self.qconv_h, alpha_l = self.aconv_l, alpha_h = self.aconv_h)\n",
    "        x = scale_rand(x)\n",
    "#    ========================conv feature map output=================================\n",
    "        h6 = self.re6(self.lin1(self.d1(x)))\n",
    "#         h6 = scale_by_percentage_mid(h6, q_l=self.qfc_l, q_h=self.qfc_h, alpha_l=self.afc_l, alpha_h=self.afc_h)\n",
    "        h6 = scale_rand(h6)\n",
    "        h7 = self.re7(self.lin2(self.d2(h6)))\n",
    "#         h7 = scale_by_percentage_mid(h7, q_l=self.qfc_l, q_h=self.qfc_h, alpha_l=self.afc_l, alpha_h=self.afc_h)\n",
    "        h7 = scale_rand(h7)\n",
    "        output = self.lin3(h7)\n",
    "#    ========================fc feature map output=================================                          \n",
    "        return output, h1, h2, h3, h4, h5, h6, h7\n",
    "\n",
    "def scale_by_percentage(x, q=5.0, alpha = 1):\n",
    "    \"\"\"\n",
    "    scale paramters by threshold.\n",
    "\n",
    "    \"\"\"\n",
    "    temp_shape = x.shape\n",
    "    weight = x.data.cpu().numpy()\n",
    "    flattened_weights = np.abs(weight.flatten())\n",
    "    percentile_value = np.percentile(flattened_weights, q)\n",
    "    mask = np.ones(flattened_weights.shape)\n",
    "    new_mask = np.where(flattened_weights >= percentile_value, alpha, mask)\n",
    "    new_mask = new_mask.reshape(temp_shape)\n",
    "    new_feature_map = np.asarray(weight * new_mask, dtype=np.float32)\n",
    "    x.data = torch.from_numpy(new_feature_map).cuda()\n",
    "    return x\n",
    "\n",
    "def scale_rand(x):\n",
    "    \"\"\"\n",
    "    scale paramters by threshold.\n",
    "\n",
    "    \"\"\"\n",
    "#     temp_shape = x.shape\n",
    "    rand = torch.rand(x.size()).cuda() + 0.5\n",
    "    x = x * rand\n",
    "#     weight = x.data.cpu().numpy()\n",
    "#     flattened_weights = np.abs(weight.flatten())\n",
    "#     percentile_value = np.percentile(flattened_weights, q)\n",
    "#     mask = np.ones(flattened_weights.shape)\n",
    "#     new_mask = np.where(flattened_weights >= percentile_value, alpha, mask)\n",
    "#     new_mask = new_mask.reshape(temp_shape)\n",
    "#     new_feature_map = np.asarray(weight * new_mask, dtype=np.float32)\n",
    "#     x.data = torch.from_numpy(new_feature_map).cuda()\n",
    "    return x\n",
    "\n",
    "def scale_by_percentage_mid(x, q_l = 50, q_h = 90, alpha_l = 1, alpha_h = 1):\n",
    "    \"\"\"\n",
    "    scale paramters by threshold.\n",
    "\n",
    "    \"\"\"\n",
    "#     print('q_l: ', q_l, 'q_h: ', q_h)\n",
    "    temp_shape = x.shape\n",
    "    weight = x.data.cpu().numpy()\n",
    "    flattened_weights = np.abs(weight.flatten())\n",
    "    p_value_low = np.percentile(flattened_weights, q_l)\n",
    "    p_value_high = np.percentile(flattened_weights, q_h)\n",
    "#     print('p_value_low: ', p_value_low, 'p_value_high: ',p_value_high)\n",
    "    mask = np.ones(flattened_weights.shape)\n",
    "    new_mask = np.where(flattened_weights >= p_value_low, alpha_l, mask)\n",
    "#     print(new_mask)\n",
    "    new_mask = np.where(flattened_weights >= p_value_high, alpha_h, new_mask)\n",
    "#     print(new_mask)\n",
    "    new_mask = new_mask.reshape(temp_shape)\n",
    "    \n",
    "    new_feature_map = np.asarray(weight * new_mask, dtype=np.float32)\n",
    "    x.data = torch.from_numpy(new_feature_map).cuda()\n",
    "    return x\n",
    "        \n",
    "def alexnet_comb_rand_mod1(**kwargs):\n",
    "    r\"\"\"AlexNet model architecture from the\n",
    "    `\"One weird trick...\" <https://arxiv.org/abs/1404.5997>`_ paper.\n",
    "    \"\"\"\n",
    "    model = AlexNet_comb_rand_mod1(**kwargs)\n",
    "    return model"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {
    "lines_to_next_cell": 2
   },
   "outputs": [],
   "source": [
    "\n",
    "\n",
    "class AlexNet_mod(nn.Module):\n",
    "    def __init__(self, num_classes=10):\n",
    "        super(AlexNet_mod, self).__init__()\n",
    "#         self.features = nn.Sequential(\n",
    "        self.conv1 = nn.Conv2d(3, 64, kernel_size=3, stride=2, padding=1)\n",
    "        self.re1 = nn.ReLU(inplace=True)\n",
    "        self.pool1 = nn.MaxPool2d(kernel_size=3, stride=2)\n",
    "        self.conv2 = nn.Conv2d(64, 192, kernel_size=3, stride=1, padding=1)\n",
    "        self.re2 = nn.ReLU(inplace=True)\n",
    "        self.pool2 = nn.MaxPool2d(kernel_size=3, stride=2)\n",
    "        self.conv3 = nn.Conv2d(192, 384, kernel_size=3,stride=1, padding=1)\n",
    "        self.re3 = nn.ReLU(inplace=True)\n",
    "        self.conv4 = nn.Conv2d(384, 256, kernel_size=3,stride=1, padding=1)\n",
    "        self.re4 = nn.ReLU(inplace=True)\n",
    "        self.conv5 = nn.Conv2d(256, 256, kernel_size=3,stride=1, padding=1)\n",
    "        self.re5 = nn.ReLU(inplace=True)\n",
    "        self.pool3 = nn.MaxPool2d(kernel_size=3, stride=2)\n",
    "        self.lin4 = nn.Linear(256*1*1, 10)\n",
    "#         )\n",
    "        \n",
    "#         self.classifier = nn.Sequential(\n",
    "        self.d1 = nn.Dropout()\n",
    "        self.lin1 = nn.Linear(256 * 1 * 1, 4096)\n",
    "        self.re6 = nn.ReLU(inplace=True)\n",
    "        self.d2 = nn.Dropout()\n",
    "        self.lin2 = nn.Linear(4096, 4096)\n",
    "        self.re7 = nn.ReLU(inplace=True)\n",
    "        self.lin3 = nn.Linear(4096, num_classes)\n",
    "#         )\n",
    "#         self.classifier = nn.Linear(256, num_classes)\n",
    "    def forward(self, x):\n",
    "        h1 = self.pool1(self.re1(self.conv1(x)))\n",
    "        h2 = self.pool2(self.re2(self.conv2(h1)))\n",
    "        h3 = self.re3(self.conv3(h2))\n",
    "        h4 = self.re4(self.conv4(h3))\n",
    "        h5 = self.pool3(self.re5(self.conv5(h4)))\n",
    "        out_h5 = self.lin4(torch.flatten(h5, 1))\n",
    "#         x = self.avgpool(x)\n",
    "#         x = x.view(x.size(0), -1)\n",
    "        x = torch.flatten(h5, 1)\n",
    "        h6 = self.re6(self.lin1(self.d1(x)))\n",
    "        h7 = self.re7(self.lin2(self.d2(h6)))\n",
    "        output = self.lin3(h7)\n",
    "                          \n",
    "        return output, h1, h2, h3, h4, h5, h6, h7, out_h5\n",
    "\n",
    "def alexnet_mod(**kwargs):\n",
    "    r\"\"\"AlexNet model architecture from the\n",
    "    `\"One weird trick...\" <https://arxiv.org/abs/1404.5997>`_ paper.\n",
    "    \"\"\"\n",
    "    model = AlexNet_mod(**kwargs)\n",
    "    return model"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {
    "lines_to_next_cell": 2
   },
   "outputs": [],
   "source": [
    "\n",
    "\n",
    "class InferenceAttack_HZ(nn.Module):\n",
    "    def __init__(self,num_classes):\n",
    "        self.num_classes=num_classes\n",
    "        super(InferenceAttack_HZ, self).__init__()\n",
    "        self.features=nn.Sequential(\n",
    "            nn.Linear(num_classes,1024),\n",
    "            nn.ReLU(),\n",
    "            nn.Linear(1024,512),\n",
    "            nn.ReLU(),\n",
    "            nn.Linear(512,64),\n",
    "            nn.ReLU(),\n",
    "            )\n",
    "        self.labels=nn.Sequential(\n",
    "#            nn.Linear(num_classes,128),\n",
    "#             nn.ReLU(),\n",
    "#             nn.Linear(128,64),\n",
    "#             nn.ReLU(),\n",
    "#             )\n",
    "            nn.Linear(num_classes,512),\n",
    "            nn.ReLU(),\n",
    "            nn.Linear(512,64),\n",
    "            nn.ReLU(),\n",
    "            )\n",
    "        self.combine=nn.Sequential(\n",
    "#             nn.Linear(64*2,256),\n",
    "            nn.Linear(64*2,512),\n",
    "            \n",
    "            nn.ReLU(),\n",
    "            nn.Linear(512,256),\n",
    "            nn.ReLU(),\n",
    "            nn.Linear(256,128),\n",
    "            \n",
    "            nn.ReLU(),\n",
    "            nn.Linear(128,64),\n",
    "            nn.ReLU(),\n",
    "            nn.Linear(64,1),\n",
    "            )\n",
    "        for key in self.state_dict():\n",
    "            print (key)\n",
    "            if key.split('.')[-1] == 'weight':    \n",
    "                nn.init.normal(self.state_dict()[key], std=0.01)\n",
    "                print (key)\n",
    "                \n",
    "            elif key.split('.')[-1] == 'bias':\n",
    "                self.state_dict()[key][...] = 0\n",
    "        self.output= nn.Sigmoid()\n",
    "    def forward(self,x,l):\n",
    "        \n",
    "        out_x = self.features(x)\n",
    "        out_l = self.labels(l)\n",
    "        \n",
    "\n",
    "        is_member =self.combine( torch.cat((out_x  ,out_l),1))\n",
    "        \n",
    "        \n",
    "        return self.output(is_member)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {
    "lines_to_next_cell": 2
   },
   "outputs": [],
   "source": [
    "\n",
    "\n",
    "# defense model\n",
    "class Defense_Model(nn.Module):\n",
    "    def __init__(self, num_classes=1):\n",
    "        super(Defense_Model, self).__init__()\n",
    "        self.features = nn.Sequential(\n",
    "            nn.Linear(num_classes, 256),\n",
    "\n",
    "            nn.ReLU(),\n",
    "            nn.Linear(256, 128),\n",
    "            nn.ReLU(),\n",
    "            nn.Linear(128, 64),\n",
    "            nn.ReLU(),\n",
    "            nn.Linear(64, 1),\n",
    "        )\n",
    "        self.output = nn.Sigmoid()\n",
    "\n",
    "    def forward(self, x):\n",
    "        is_member = self.features(x)\n",
    "        return self.output(is_member), is_member"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {
    "lines_to_next_cell": 2
   },
   "outputs": [],
   "source": [
    "\n",
    "\n",
    "def train_privatly(trainloader, model,inference_model, criterion, optimizer, epoch, use_cuda,num_batchs=10000,alpha=0.9):\n",
    "    # switch to train mode\n",
    "    model.train()\n",
    "    inference_model.eval()\n",
    "    batch_time = AverageMeter()\n",
    "    data_time = AverageMeter()\n",
    "    losses = AverageMeter()\n",
    "    top1 = AverageMeter()\n",
    "    top5 = AverageMeter()\n",
    "    end = time.time()\n",
    "    \n",
    "    first_id = -1\n",
    "    for batch_idx, (inputs, targets) in (trainloader):\n",
    "        # measure data loading time\n",
    "        data_time.update(time.time() - end)\n",
    "        if first_id == -1:\n",
    "            first_id = batch_idx\n",
    "        \n",
    "        if use_cuda:\n",
    "            inputs, targets = inputs.cuda(), targets.cuda()\n",
    "        inputs, targets = torch.autograd.Variable(inputs), torch.autograd.Variable(targets)\n",
    "\n",
    "        # compute output\n",
    "        outputs = model(inputs)\n",
    "        \n",
    "        \n",
    "        one_hot_tr = torch.from_numpy((np.zeros((outputs.size(0),100))-1)).cuda().type(torch.cuda.FloatTensor)\n",
    "        target_one_hot_tr = one_hot_tr.scatter_(1, targets.type(torch.cuda.LongTensor).view([-1,1]).data,1)\n",
    "        infer_input_one_hot = torch.autograd.Variable(target_one_hot_tr)\n",
    "        \n",
    "        inference_output = inference_model ( outputs,infer_input_one_hot)\n",
    "        #print (inference_output.mean())\n",
    "        \n",
    "#         loss = criterion(outputs, targets) + (alpha)*(((inference_output-1.0).pow(2).mean()))\n",
    "        loss = criterion(outputs, targets) + (alpha)*(((inference_output-0.5).pow(2).mean()))\n",
    "\n",
    "        # measure accuracy and record loss\n",
    "        prec1, prec5 = accuracy(outputs.data, targets.data, topk=(1, 5))\n",
    "#         losses.update(loss.data[0], inputs.size(0))\n",
    "        losses.update(loss.data, inputs.size(0))\n",
    "        top1.update(prec1, inputs.size(0))\n",
    "        top5.update(prec5, inputs.size(0))\n",
    "\n",
    "        # compute gradient and do SGD step\n",
    "        optimizer.zero_grad()\n",
    "        loss.backward()\n",
    "        optimizer.step()\n",
    "\n",
    "        # measure elapsed time\n",
    "        batch_time.update(time.time() - end)\n",
    "        end = time.time()\n",
    "\n",
    "        # plot progress\n",
    "        if batch_idx%100==0:\n",
    "            print  ('modeltrain--({batch}/{size}) Data: {data:.3f}s | Batch: {bt:.3f}s | | Loss: {loss:.4f} | top1: {top1: .4f} | top5: {top5: .4f}'.format(\n",
    "                    batch=batch_idx + 1,\n",
    "                    size=500,\n",
    "                    data=data_time.avg,\n",
    "                    bt=batch_time.avg,\n",
    "                    loss=losses.avg,\n",
    "                    top1=top1.avg,\n",
    "                    top5=top5.avg,\n",
    "                    ))\n",
    "        if batch_idx-first_id >= num_batchs:\n",
    "            break\n",
    "    return (losses.avg, top1.avg)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {
    "lines_to_next_cell": 2
   },
   "outputs": [],
   "source": [
    "\n",
    "\n",
    "def train(trainloader, model, criterion, optimizer, epoch, use_cuda):\n",
    "    # switch to train mode\n",
    "    model.train()\n",
    "    batch_time = AverageMeter()\n",
    "    data_time = AverageMeter()\n",
    "    losses = AverageMeter()\n",
    "    top1 = AverageMeter()\n",
    "    top5 = AverageMeter()\n",
    "    end = time.time()\n",
    "    bar = Bar('Processing', max=len(trainloader))\n",
    "    for batch_idx, (inputs, targets) in enumerate(trainloader):\n",
    "        # measure data loading time\n",
    "        data_time.update(time.time() - end)\n",
    "        if use_cuda:\n",
    "            inputs, targets = inputs.cuda(), targets.cuda()\n",
    "        inputs, targets = torch.autograd.Variable(inputs), torch.autograd.Variable(targets)\n",
    "\n",
    "        # compute output\n",
    "#         outputs,_,_,_,_,_,_,_,_ = model(inputs)\n",
    "        outputs,_,_,_,_,_,_,_ = model(inputs)\n",
    "#         outputs = model(inputs)\n",
    "#         print(outputs.shape)\n",
    "        loss = criterion(outputs, targets)\n",
    "\n",
    "        # measure accuracy and record loss\n",
    "        prec1, prec5 = accuracy(outputs.data, targets.data, topk=(1, 5))\n",
    "#         losses.update(loss.data[0], inputs.size(0))\n",
    "        losses.update(loss.data, inputs.size(0))\n",
    "        top1.update(prec1, inputs.size(0))\n",
    "        top5.update(prec5, inputs.size(0))\n",
    "\n",
    "        # compute gradient and do SGD step\n",
    "        optimizer.zero_grad()\n",
    "        loss.backward()\n",
    "        optimizer.step()\n",
    "\n",
    "        # measure elapsed time\n",
    "        batch_time.update(time.time() - end)\n",
    "        end = time.time()\n",
    "\n",
    "        # plot progress\n",
    "        if batch_idx%100==0:\n",
    "            print  ('({batch}/{size}) Data: {data:.3f}s | Batch: {bt:.3f}s | | Loss: {loss:.4f} | top1: {top1: .4f} | top5: {top5: .4f}'.format(\n",
    "                    batch=batch_idx + 1,\n",
    "                    size=len(trainloader),\n",
    "                    data=data_time.avg,\n",
    "                    bt=batch_time.avg,\n",
    "                    loss=losses.avg,\n",
    "                    top1=top1.avg,\n",
    "                    top5=top5.avg,\n",
    "                    ))\n",
    "    return (losses.avg, top1.avg)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {
    "lines_to_next_cell": 2
   },
   "outputs": [],
   "source": [
    "\n",
    "\n",
    "def test(testloader, model, criterion, epoch, use_cuda):\n",
    "    global best_acc\n",
    "    batch_time = AverageMeter()\n",
    "    data_time = AverageMeter()\n",
    "    losses = AverageMeter()\n",
    "    top1 = AverageMeter()\n",
    "    top5 = AverageMeter()\n",
    "\n",
    "    # switch to evaluate mode\n",
    "    model.eval()\n",
    "    end = time.time()\n",
    "    for batch_idx, (inputs, targets) in enumerate(testloader):\n",
    "        # measure data loading time\n",
    "        data_time.update(time.time() - end)\n",
    "        if use_cuda:\n",
    "            inputs, targets = inputs.cuda(), targets.cuda()\n",
    "        inputs, targets = torch.autograd.Variable(inputs, volatile=True), torch.autograd.Variable(targets)\n",
    "\n",
    "        # compute output\n",
    "#         outputs,_,_,_,_,_,_,_,_ = model(inputs)\n",
    "        outputs,_,_,_,_,_,_,_ = model(inputs)\n",
    "#         outputs = model(inputs)\n",
    "        loss = criterion(outputs, targets)\n",
    "\n",
    "        # measure accuracy and record loss\n",
    "        prec1, prec5 = accuracy(outputs.data, targets.data, topk=(1, 5))\n",
    "#         losses.update(loss.data[0], inputs.size(0))\n",
    "        losses.update(loss.data, inputs.size(0))\n",
    "        top1.update(prec1, inputs.size(0))\n",
    "        top5.update(prec5, inputs.size(0))\n",
    "\n",
    "        # measure elapsed time\n",
    "        batch_time.update(time.time() - end)\n",
    "        end = time.time()\n",
    "\n",
    "        # plot progress\n",
    "        if batch_idx % 20==0:\n",
    "            \n",
    "            print ('({batch}/{size}) Data: {data:.3f}s | Batch: {bt:.3f}s | Loss: {loss:.4f} | top1: {top1: .4f} | top5: {top5: .4f}'.format(\n",
    "                        batch=batch_idx + 1,\n",
    "                        size=len(testloader),\n",
    "                        data=data_time.avg,\n",
    "                        bt=batch_time.avg,\n",
    "                        loss=losses.avg,\n",
    "                        top1=top1.avg,\n",
    "                        top5=top5.avg,\n",
    "                        ))\n",
    "    return (losses.avg, top1.avg)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {
    "lines_to_next_cell": 2
   },
   "outputs": [],
   "source": [
    "\n",
    "\n",
    "def test_by_class(testloader, model, criterion, epoch, use_cuda):\n",
    "    global best_acc\n",
    "    batch_time = AverageMeter()\n",
    "    data_time = AverageMeter()\n",
    "    losses = AverageMeter()\n",
    "    top1 = AverageMeter()\n",
    "    top5 = AverageMeter()\n",
    "\n",
    "    # switch to evaluate mode\n",
    "    model.eval()\n",
    "    end = time.time()\n",
    "    class_count = np.zeros(100)\n",
    "    class_correct = np.zeros(100)\n",
    "    for batch_idx, (inputs, targets) in enumerate(testloader):\n",
    "        # measure data loading time\n",
    "        data_time.update(time.time() - end)\n",
    "        if use_cuda:\n",
    "            inputs, targets = inputs.cuda(), targets.cuda()\n",
    "        inputs, targets = torch.autograd.Variable(inputs, volatile=True), torch.autograd.Variable(targets)\n",
    "\n",
    "        # compute output\n",
    "#         outputs,_,_,_,_,_,_,_,_ = model(inputs)\n",
    "        outputs,_,_,_,_,_,_,_ = model(inputs)\n",
    "#         outputs = model(inputs)\n",
    "        loss = criterion(outputs, targets)\n",
    "\n",
    "        # measure accuracy and record loss\n",
    "        prec1, prec5 = accuracy(outputs.data, targets.data, topk=(1, 5))\n",
    "        _, pred = outputs.topk(1, 1, True, True)\n",
    "        pred = pred.t()\n",
    "        correct = pred.eq(targets.view(1, -1).expand_as(pred))\n",
    "        correct = correct.data.cpu().numpy()\n",
    "        targets = targets.data.cpu().numpy()\n",
    "        for i in range(len(targets)):\n",
    "            class_count[targets[i]] += 1\n",
    "            class_correct[targets[i]] += correct[0,i]\n",
    "#         losses.update(loss.data[0], inputs.size(0))\n",
    "        losses.update(loss.data, inputs.size(0))\n",
    "        top1.update(prec1, inputs.size(0))\n",
    "        top5.update(prec5, inputs.size(0))\n",
    "\n",
    "        # measure elapsed time\n",
    "        batch_time.update(time.time() - end)\n",
    "        end = time.time()\n",
    "\n",
    "        # plot progress\n",
    "        if batch_idx % 20==0:\n",
    "            \n",
    "            print ('({batch}/{size}) Data: {data:.3f}s | Batch: {bt:.3f}s | Loss: {loss:.4f} | top1: {top1: .4f} | top5: {top5: .4f}'.format(\n",
    "                        batch=batch_idx + 1,\n",
    "                        size=len(testloader),\n",
    "                        data=data_time.avg,\n",
    "                        bt=batch_time.avg,\n",
    "                        loss=losses.avg,\n",
    "                        top1=top1.avg,\n",
    "                        top5=top5.avg,\n",
    "                        ))\n",
    "    return (losses.avg, top1.avg, class_count, class_correct)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "\n",
    "\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {
    "lines_to_next_cell": 2
   },
   "outputs": [],
   "source": [
    "\n",
    "\n",
    "def train_one(trainloader, model, criterion, optimizer, epoch, use_cuda):\n",
    "    # switch to train mode\n",
    "    model.train()\n",
    "    batch_time = AverageMeter()\n",
    "    data_time = AverageMeter()\n",
    "    losses = AverageMeter()\n",
    "    top1 = AverageMeter()\n",
    "    top5 = AverageMeter()\n",
    "    end = time.time()\n",
    "    bar = Bar('Processing', max=len(trainloader))\n",
    "    for batch_idx, (inputs, targets) in enumerate(trainloader):\n",
    "        # measure data loading time\n",
    "        data_time.update(time.time() - end)\n",
    "        if use_cuda:\n",
    "            inputs, targets = inputs.cuda(), targets.cuda()\n",
    "        inputs, targets = torch.autograd.Variable(inputs), torch.autograd.Variable(targets)\n",
    "\n",
    "        # compute output\n",
    "        outputs = model(inputs)\n",
    "#         print(outputs.shape)\n",
    "        loss = criterion(outputs, targets)\n",
    "\n",
    "        # measure accuracy and record loss\n",
    "        prec1, prec5 = accuracy(outputs.data, targets.data, topk=(1, 5))\n",
    "#         losses.update(loss.data[0], inputs.size(0))\n",
    "        losses.update(loss.data, inputs.size(0))\n",
    "        top1.update(prec1, inputs.size(0))\n",
    "        top5.update(prec5, inputs.size(0))\n",
    "\n",
    "        # compute gradient and do SGD step\n",
    "        optimizer.zero_grad()\n",
    "        loss.backward()\n",
    "        optimizer.step()\n",
    "\n",
    "        # measure elapsed time\n",
    "        batch_time.update(time.time() - end)\n",
    "        end = time.time()\n",
    "\n",
    "        # plot progress\n",
    "        if batch_idx%100==0:\n",
    "            print  ('({batch}/{size}) Data: {data:.3f}s | Batch: {bt:.3f}s | | Loss: {loss:.4f} | top1: {top1: .4f} | top5: {top5: .4f}'.format(\n",
    "                    batch=batch_idx + 1,\n",
    "                    size=len(trainloader),\n",
    "                    data=data_time.avg,\n",
    "                    bt=batch_time.avg,\n",
    "                    loss=losses.avg,\n",
    "                    top1=top1.avg,\n",
    "                    top5=top5.avg,\n",
    "                    ))\n",
    "    return (losses.avg, top1.avg)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {
    "lines_to_next_cell": 2
   },
   "outputs": [],
   "source": [
    "\n",
    "\n",
    "def test_one(testloader, model, criterion, epoch, use_cuda):\n",
    "    global best_acc\n",
    "    batch_time = AverageMeter()\n",
    "    data_time = AverageMeter()\n",
    "    losses = AverageMeter()\n",
    "    top1 = AverageMeter()\n",
    "    top5 = AverageMeter()\n",
    "\n",
    "    # switch to evaluate mode\n",
    "    model.eval()\n",
    "    end = time.time()\n",
    "    for batch_idx, (inputs, targets) in enumerate(testloader):\n",
    "        # measure data loading time\n",
    "        data_time.update(time.time() - end)\n",
    "        if use_cuda:\n",
    "            inputs, targets = inputs.cuda(), targets.cuda()\n",
    "        inputs, targets = torch.autograd.Variable(inputs, volatile=True), torch.autograd.Variable(targets)\n",
    "\n",
    "        # compute output\n",
    "#         outputs,_,_,_,_,_,_,_,_ = model(inputs)\n",
    "        outputs = model(inputs)\n",
    "        loss = criterion(outputs, targets)\n",
    "\n",
    "        # measure accuracy and record loss\n",
    "        prec1, prec5 = accuracy(outputs.data, targets.data, topk=(1, 5))\n",
    "#         losses.update(loss.data[0], inputs.size(0))\n",
    "        losses.update(loss.data, inputs.size(0))\n",
    "        top1.update(prec1, inputs.size(0))\n",
    "        top5.update(prec5, inputs.size(0))\n",
    "\n",
    "        # measure elapsed time\n",
    "        batch_time.update(time.time() - end)\n",
    "        end = time.time()\n",
    "\n",
    "        # plot progress\n",
    "        if batch_idx % 20==0:\n",
    "            \n",
    "            print ('({batch}/{size}) Data: {data:.3f}s | Batch: {bt:.3f}s | Loss: {loss:.4f} | top1: {top1: .4f} | top5: {top5: .4f}'.format(\n",
    "                        batch=batch_idx + 1,\n",
    "                        size=len(testloader),\n",
    "                        data=data_time.avg,\n",
    "                        bt=batch_time.avg,\n",
    "                        loss=losses.avg,\n",
    "                        top1=top1.avg,\n",
    "                        top5=top5.avg,\n",
    "                        ))\n",
    "    return (losses.avg, top1.avg)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {},
   "outputs": [],
   "source": [
    "\n",
    "\n",
    "def train_half_diff_fc_min_var(trainloader, model, criterion, optimizer, mean_class,var_n, epoch, use_cuda,num_class=100, alpha = 1.0, beta = 1.0):\n",
    "    # switch to train mode\n",
    "    model.train()\n",
    "    softmax = nn.Softmax()\n",
    "\n",
    "    batch_time = AverageMeter()\n",
    "    data_time = AverageMeter()\n",
    "    losses = AverageMeter()\n",
    "    losses_var = AverageMeter()\n",
    "    losses_diff = AverageMeter()\n",
    "    max_train_outputs = AverageMeter()\n",
    "    min_train_outputs = AverageMeter()\n",
    "    top1 = AverageMeter()\n",
    "    top5 = AverageMeter()\n",
    "    \n",
    "    print('alpha: ', alpha, ' beta: ',beta)\n",
    "    end = time.time()\n",
    "#     len_t =  (len(train_data)//batch_size)\n",
    "    \n",
    "#     mean_class = torch.from_numpy(np.zeros((num_class,num_class))).cuda().type(torch.cuda.FloatTensor)\n",
    "#     mean_class = np.zeros((num_class,num_class))\n",
    "#     var_n = np.zeros(num_class)\n",
    "#     mean_var = torch.from_numpy(np.zeros((num_class))).cuda().type(torch.cuda.FloatTensor)\n",
    "#     mean_var = np.zeros((num_class))\n",
    " \n",
    "    \n",
    "    for batch_idx, (inputs, targets) in enumerate(trainloader):\n",
    "        # measure data loading time\n",
    "        data_time.update(time.time() - end)\n",
    "        if use_cuda:\n",
    "            inputs, targets = inputs.cuda(), targets.cuda()\n",
    "        inputs, targets = torch.autograd.Variable(inputs), torch.autograd.Variable(targets)\n",
    "\n",
    "        # compute output\n",
    "        outputs,h1,h2,h3,h4,h5,h6,h7  = model(inputs)\n",
    "        \n",
    "#         fh1 = torch.sum(torch.sum(h1,2),2)\n",
    "#         fh2 = torch.sum(torch.sum(h2,2),2)\n",
    "#         fh3 = torch.sum(torch.sum(h3,2),2)\n",
    "#         fh4 = torch.sum(torch.sum(h4,2),2)\n",
    "#         fh5 = torch.sum(torch.sum(h5,2),2)\n",
    "        \n",
    "        out_list = [h6, h7, outputs]\n",
    "        sum_diff = torch.zeros(out_list[0].shape[0]).cuda()\n",
    "        for out_layer in out_list:\n",
    "\n",
    "            # hidden_outputs.shape\n",
    "            hidden_map = torch.ones(out_layer.shape[1]).cuda()\n",
    "            hidden_map[out_layer.shape[1]//2:] = -1\n",
    "        #     torch.sum(hidden_map)\n",
    "            hd_diff_map = out_layer * hidden_map\n",
    "            # hd_diff_map.shape\n",
    "            hd_diff = torch.sum(hd_diff_map, 1)\n",
    "            var_hd = hd_diff ** 2 / hd_diff_map.shape[1]\n",
    "            sum_diff += var_hd\n",
    "        \n",
    "        diff_var = sum_diff.mean() * alpha\n",
    "        \n",
    "        soft_outputs = softmax(outputs)\n",
    "        max_outputs = torch.max(soft_outputs,1).values\n",
    "        min_outputs = torch.min(max_outputs)\n",
    "        \n",
    "#         mean_outputs = torch.mean(soft_outputs,0)\n",
    "#         var = -torch.mean(torch.sum((soft_outputs - mean_outputs).pow(2),1))\n",
    "\n",
    "        for i in range(inputs.size(0)):\n",
    "#         var_n[targets[i]] = var_n[targets[i]] + 1\n",
    "            var_n[targets[i]] += 1\n",
    "    #         mean_class[targets[i]] = mean_class[targets[i]]*(var_n[targets[i]]-1)/var_n[targets[i]] + soft_outputs[i]/var_n[targets[i]]\n",
    "            mean_class[targets[i]] = mean_class[targets[i]]*(var_n[targets[i]]-1)/var_n[targets[i]] + soft_outputs[i].data.cpu().numpy()/var_n[targets[i]]\n",
    "    #         mean_var[targets[i]] = mean_var[targets[i]]*(var_n[targets[i]]-1)/var_n[targets[i]] + torch.sum((soft_outputs[i] - mean_class[targets[i]]).pow(2))/var_n[targets[i]]\n",
    "    #         mean_var[targets[i]] = mean_var[targets[i]]*(var_n[targets[i]]-1)/var_n[targets[i]] + torch.sum((soft_outputs[i] - torch.from_numpy(mean_class[targets[i]]).cuda()).pow(2))/var_n[targets[i]]\n",
    "\n",
    "        temp_mean = torch.from_numpy(mean_class).cuda()\n",
    "#         mean_var = torch.sum((soft_outputs - temp_mean[targets]).pow(2))\n",
    "        mean_var = torch.sum((soft_outputs - temp_mean[targets]).pow(2),1)\n",
    "    \n",
    "        var = torch.mean(mean_var)*beta\n",
    "#         var = -mean_var * alpha\n",
    "\n",
    "# #         sort_mean_class,_ = torch.sort(temp_mean)\n",
    "# #         sort_mean_output = torch.mean(sort_mean_class,0)\n",
    "#         sort_soft_outputs,_ = torch.sort(soft_outputs)\n",
    "        \n",
    "        \n",
    "# #         sort_var = torch.sum((sort_soft_outputs - sort_mean_output).pow(2),1)\n",
    "# #         var = torch.mean(sort_var) * beta\n",
    "        \n",
    "# #         sort_var = torch.sum((sort_soft_outputs - sort_mean_class[targets]).pow(2),1)\n",
    "# #         var = torch.mean(sort_var)*beta\n",
    "\n",
    "# #         sort_var = torch.sum((sort_soft_outputs - sort_mean_output).pow(2))\n",
    "# #         var = sort_var * beta\n",
    "\n",
    "# #         s_var = sort_var * beta\n",
    "# #         sort_mean_low8 = torch.mean(sort_soft_outputs[:,:8],1).view(-1,1)\n",
    "# #         sort_mean_low8 = sort_mean_low8.expand(-1, 8)\n",
    "        \n",
    "# #         low8_var = torch.sum((sort_soft_outputs[:,:8] - sort_mean_low8).pow(2),1)\n",
    "# #         low8_loss = -torch.mean(low8_var) * alpha\n",
    "#         sort_diff_top = sort_soft_outputs[:,9] - sort_soft_outputs[:,8]\n",
    "#         diff_top = torch.mean(sort_diff_top) * alpha\n",
    "#     #         var = -torch.mean(torch.from_numpy(mean_var)).cuda()*alpha\n",
    "#     #         var = torch.autograd.Variable(var)\n",
    "    \n",
    "#         sort_soft_outputs_mean = torch.mean(sort_soft_outputs,0)\n",
    "# #         mean_diff = torch.abs(torch.sum(sort_soft_outputs_mean[99]) - torch.sum(sort_soft_outputs_mean[:99]))\n",
    "# #         mean_diff = torch.abs(sort_soft_outputs_mean[99] - 0.6)\n",
    "#         mean_diff = torch.mean(torch.sum(sort_soft_outputs[:,5:],1) - torch.sum(sort_soft_outputs[:,:5],1))\n",
    "#         loss_diff = beta * mean_diff\n",
    "        \n",
    "        loss = criterion(outputs, targets) + var + diff_var\n",
    "        \n",
    "#         loss = criterion(outputs, targets)\n",
    "\n",
    "        # measure accuracy and record loss\n",
    "        prec1, prec5 = accuracy(outputs.data, targets.data, topk=(1, 5))\n",
    "        losses.update(loss.data, inputs.size()[0])\n",
    "        losses_var.update(var.data, inputs.size()[0])\n",
    "        losses_diff.update(diff_var.data, inputs.size()[0])\n",
    "        top1.update(prec1, inputs.size()[0])\n",
    "        top5.update(prec5, inputs.size()[0])\n",
    "        max_train_outputs.update(torch.mean(max_outputs), inputs.size()[0])\n",
    "        min_train_outputs.update(min_outputs, inputs.size()[0])\n",
    "        # compute gradient and do SGD step\n",
    "        optimizer.zero_grad()\n",
    "        loss.backward() #retain_graph=True)\n",
    "        optimizer.step()\n",
    "\n",
    "        # measure elapsed time\n",
    "        batch_time.update(time.time() - end)\n",
    "        end = time.time()\n",
    "\n",
    "        # plot progress\n",
    "        if batch_idx%100==0:\n",
    "            print  ('({batch}/{size}) |Batch: {bt:.3f}s| Loss: {loss:.4f} |var: {loss_var:.4f} | diff_top: {loss_d:.4f} | Max: {tmax:.4f} | Min: {tmin:.4f}| top1: {top1: .4f} | top5: {top5: .4f}'.format(\n",
    "                    batch=batch_idx + 1,\n",
    "                    size=len(trainloader),\n",
    "                    bt=batch_time.avg,\n",
    "                    loss=losses.avg,\n",
    "                    loss_var=losses_var.avg,\n",
    "                    loss_d=losses_diff.avg,\n",
    "                    tmax = max_train_outputs.avg,\n",
    "                    tmin = min_train_outputs.avg,\n",
    "                    top1=top1.avg,\n",
    "                    top5=top5.avg,\n",
    "                    ))\n",
    "    return (losses.avg,losses_var.avg, losses_diff.avg,top1.avg)\n",
    "        \n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "\n",
    "\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {
    "lines_to_next_cell": 2
   },
   "outputs": [],
   "source": [
    "\n",
    "\n",
    "def privacy_train(trainloader, model,inference_model, criterion, optimizer, epoch, use_cuda,num_batchs=1000):\n",
    "    global best_acc\n",
    "    batch_time = AverageMeter()\n",
    "    data_time = AverageMeter()\n",
    "    losses = AverageMeter()\n",
    "    top1 = AverageMeter()\n",
    "    mtop1_a = AverageMeter()\n",
    "    mtop5_a = AverageMeter()\n",
    "    \n",
    "    inference_model.train()\n",
    "    model.eval()\n",
    "    # switch to evaluate mode\n",
    "    end = time.time()\n",
    "    first_id = -1\n",
    "    for batch_idx,((tr_input, tr_target) ,(te_input, te_target)) in trainloader:\n",
    "        # measure data loading time\n",
    "        if first_id == -1:\n",
    "            first_id = batch_idx\n",
    "        \n",
    "        data_time.update(time.time() - end)\n",
    "        tr_input = tr_input.cuda()\n",
    "        te_input = te_input.cuda()\n",
    "        tr_target = tr_target.cuda()\n",
    "        te_target = te_target.cuda()\n",
    "        \n",
    "        \n",
    "        v_tr_input = torch.autograd.Variable(tr_input)\n",
    "        v_te_input = torch.autograd.Variable(te_input)\n",
    "        v_tr_target = torch.autograd.Variable(tr_target)\n",
    "        v_te_target = torch.autograd.Variable(te_target)\n",
    "        \n",
    "        # compute output\n",
    "        model_input =torch.cat((v_tr_input,v_te_input))\n",
    "        \n",
    "        pred_outputs,_,_,_,_,_,_,_,_  = model(model_input)\n",
    "        \n",
    "        infer_input= torch.cat((v_tr_target,v_te_target))\n",
    "        \n",
    "        mtop1, mtop5 =accuracy(pred_outputs.data, infer_input.data, topk=(1, 5))\n",
    "        \n",
    "        mtop1_a.update(mtop1, model_input.size(0))\n",
    "        mtop5_a.update(mtop5, model_input.size(0))\n",
    "        \n",
    "        \n",
    "        one_hot_tr = torch.from_numpy((np.zeros((infer_input.size(0),10))-1)).cuda().type(torch.cuda.FloatTensor)\n",
    "        target_one_hot_tr = one_hot_tr.scatter_(1, infer_input.type(torch.cuda.LongTensor).view([-1,1]).data,1)\n",
    "        infer_input_one_hot = torch.autograd.Variable(target_one_hot_tr)\n",
    "        \n",
    "        attack_model_input = pred_outputs #torch.cat((pred_outputs,infer_input_one_hot),1)\n",
    "        member_output = inference_model(attack_model_input,infer_input_one_hot)\n",
    "        \n",
    "#         is_member_labels = torch.from_numpy(np.reshape(np.concatenate((np.zeros(v_tr_input.size(0)),np.ones(v_te_input.size(0)))),[-1,1])).cuda()\n",
    "        is_member_labels = torch.from_numpy(np.reshape(np.concatenate((np.ones(v_tr_input.size(0)),np.zeros(v_te_input.size(0)))),[-1,1])).cuda()\n",
    "\n",
    "        v_is_member_labels = torch.autograd.Variable(is_member_labels).type(torch.cuda.FloatTensor)\n",
    "        loss = criterion(member_output, v_is_member_labels)\n",
    "\n",
    "        # measure accuracy and record loss\n",
    "        prec1=np.mean((member_output.data.cpu().numpy() >0.5)==v_is_member_labels.data.cpu().numpy())\n",
    "        losses.update(loss.data, model_input.size(0))\n",
    "        top1.update(prec1, model_input.size(0))\n",
    "        \n",
    "#         print ( \"member_output: \" ,member_output.data.cpu().numpy())\n",
    "#         print(\"v_is_member_labels: \",v_is_member_labels.data.cpu().numpy())\n",
    "        # compute gradient and do SGD step\n",
    "        optimizer.zero_grad()\n",
    "        loss.backward()\n",
    "        optimizer.step()\n",
    "\n",
    "        # measure elapsed time\n",
    "        batch_time.update(time.time() - end)\n",
    "        end = time.time()\n",
    "        if batch_idx-first_id > num_batchs:\n",
    "            break\n",
    "\n",
    "        # plot progress\n",
    "        if batch_idx%10==0:\n",
    "            print  ('attack_train--({batch}/{size}) Data: {data:.3f}s | Batch: {bt:.3f}s | | Loss: {loss:.4f} | top1: {top1: .4f} '.format(\n",
    "                    batch=batch_idx ,\n",
    "                    size=100,\n",
    "                    data=data_time.avg,\n",
    "                    bt=batch_time.avg,\n",
    "                    loss=losses.avg,\n",
    "                    top1=top1.avg,\n",
    "                    ))\n",
    "    return (losses.avg, top1.avg)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "metadata": {
    "lines_to_next_cell": 2
   },
   "outputs": [],
   "source": [
    "\n",
    "\n",
    "def privacy_test(trainloader, model, inference_model, criterion, optimizer, epoch, use_cuda,num_batchs=1000):\n",
    "    global best_acc\n",
    "    batch_time = AverageMeter()\n",
    "    data_time = AverageMeter()\n",
    "    losses = AverageMeter()\n",
    "    top1 = AverageMeter()\n",
    "    mtop1_a = AverageMeter()\n",
    "    mtop5_a = AverageMeter()\n",
    "    first_id = -1\n",
    "    inference_model.eval()\n",
    "    model.eval()\n",
    "    # switch to evaluate mode\n",
    "    end = time.time()\n",
    "    for batch_idx,((tr_input, tr_target) ,(te_input, te_target)) in trainloader:\n",
    "        # measure data loading time\n",
    "        if first_id == -1:\n",
    "            first_id = batch_idx\n",
    "        data_time.update(time.time() - end)\n",
    "        tr_input = tr_input.cuda()\n",
    "        te_input = te_input.cuda()\n",
    "        tr_target = tr_target.cuda()\n",
    "        te_target = te_target.cuda()\n",
    "        \n",
    "        \n",
    "        v_tr_input = torch.autograd.Variable(tr_input)\n",
    "        v_te_input = torch.autograd.Variable(te_input)\n",
    "        v_tr_target = torch.autograd.Variable(tr_target)\n",
    "        v_te_target = torch.autograd.Variable(te_target)\n",
    "        \n",
    "        # compute output\n",
    "        model_input =torch.cat((v_tr_input,v_te_input))\n",
    "        \n",
    "        pred_outputs ,_,_,_,_,_,_,_,_ = model(model_input)\n",
    "        \n",
    "        infer_input= torch.cat((v_tr_target,v_te_target))\n",
    "        \n",
    "        mtop1, mtop5 =accuracy(pred_outputs.data, infer_input.data, topk=(1, 5))\n",
    "        \n",
    "        mtop1_a.update(mtop1, model_input.size(0))\n",
    "        mtop5_a.update(mtop5, model_input.size(0))\n",
    "        \n",
    "        \n",
    "        one_hot_tr = torch.from_numpy((np.zeros((infer_input.size(0),10))-1)).cuda().type(torch.cuda.FloatTensor)\n",
    "        target_one_hot_tr = one_hot_tr.scatter_(1, infer_input.type(torch.cuda.LongTensor).view([-1,1]).data,1)\n",
    "        infer_input_one_hot = torch.autograd.Variable(target_one_hot_tr)\n",
    "        \n",
    "        attack_model_input = pred_outputs#torch.cat((pred_outputs,infer_input_one_hot),1)\n",
    "        member_output = inference_model(attack_model_input,infer_input_one_hot)\n",
    "        \n",
    "        \n",
    "#         is_member_labels = torch.from_numpy(np.reshape(np.concatenate((np.zeros(v_tr_input.size(0)),np.ones(v_te_input.size(0)))),[-1,1])).cuda()\n",
    "        is_member_labels = torch.from_numpy(np.reshape(np.concatenate((np.ones(v_tr_input.size(0)),np.zeros(v_te_input.size(0)))),[-1,1])).cuda()\n",
    "        \n",
    "        v_is_member_labels = torch.autograd.Variable(is_member_labels).type(torch.cuda.FloatTensor)\n",
    "        loss = criterion(member_output, v_is_member_labels)\n",
    "\n",
    "        # measure accuracy and record loss\n",
    "        prec1=np.mean((member_output.data.cpu().numpy() >0.5)==v_is_member_labels.data.cpu().numpy())\n",
    "        losses.update(loss.data, model_input.size(0))\n",
    "        top1.update(prec1, model_input.size(0))\n",
    "\n",
    "        # compute gradient and do SGD step\n",
    "        \n",
    "        # measure elapsed time\n",
    "        batch_time.update(time.time() - end)\n",
    "        end = time.time()\n",
    "        if batch_idx-first_id > num_batchs:\n",
    "            break\n",
    "\n",
    "#         plot progress\n",
    "        if batch_idx%10==0:\n",
    "            print  ('privacy_test--({batch}/{size}) Data: {data:.3f}s | Batch: {bt:.3f}s | | Loss: {loss:.4f} | top1: {top1: .4f} '.format(\n",
    "                    batch=batch_idx ,\n",
    "                    size=100,\n",
    "                    data=data_time.avg,\n",
    "                    bt=batch_time.avg,\n",
    "                    loss=losses.avg,\n",
    "                    top1=top1.avg,\n",
    "                    ))\n",
    "            if epoch == 9:\n",
    "                print ( \"member_output: \" ,member_output.data.cpu().numpy())\n",
    "                print(\"v_is_member_labels: \",v_is_member_labels.data.cpu().numpy())\n",
    "    return (losses.avg, top1.avg)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "metadata": {
    "lines_to_next_cell": 2
   },
   "outputs": [],
   "source": [
    "\n",
    "\n",
    "def privacy_train_softmax(trainloader, model,inference_model, criterion, optimizer, epoch, use_cuda,num_batchs=1000):\n",
    "    global best_acc\n",
    "    batch_time = AverageMeter()\n",
    "    data_time = AverageMeter()\n",
    "    losses = AverageMeter()\n",
    "    top1 = AverageMeter()\n",
    "    mtop1_a = AverageMeter()\n",
    "    mtop5_a = AverageMeter()\n",
    "    \n",
    "    softmax = nn.Softmax()\n",
    "    \n",
    "    inference_model.train()\n",
    "    model.eval()\n",
    "    # switch to evaluate mode\n",
    "    end = time.time()\n",
    "    first_id = -1\n",
    "    for batch_idx,((tr_input, tr_target) ,(te_input, te_target)) in trainloader:\n",
    "        # measure data loading time\n",
    "        if first_id == -1:\n",
    "            first_id = batch_idx\n",
    "        \n",
    "        data_time.update(time.time() - end)\n",
    "        tr_input = tr_input.cuda()\n",
    "        te_input = te_input.cuda()\n",
    "        tr_target = tr_target.cuda()\n",
    "        te_target = te_target.cuda()\n",
    "        \n",
    "        \n",
    "        v_tr_input = torch.autograd.Variable(tr_input)\n",
    "        v_te_input = torch.autograd.Variable(te_input)\n",
    "        v_tr_target = torch.autograd.Variable(tr_target)\n",
    "        v_te_target = torch.autograd.Variable(te_target)\n",
    "        \n",
    "        # compute output\n",
    "        model_input =torch.cat((v_tr_input,v_te_input))\n",
    "        \n",
    "        pred_outputs,_,_,_,_,_,_,_  = model(model_input)\n",
    "#         pred_outputs = model(model_input)\n",
    "        \n",
    "        infer_input= torch.cat((v_tr_target,v_te_target))\n",
    "        \n",
    "        mtop1, mtop5 =accuracy(pred_outputs.data, infer_input.data, topk=(1, 5))\n",
    "        \n",
    "        mtop1_a.update(mtop1, model_input.size(0))\n",
    "        mtop5_a.update(mtop5, model_input.size(0))\n",
    "        \n",
    "        \n",
    "        one_hot_tr = torch.from_numpy((np.zeros((infer_input.size(0),pred_outputs.size(1)))-1)).cuda().type(torch.cuda.FloatTensor)\n",
    "        target_one_hot_tr = one_hot_tr.scatter_(1, infer_input.type(torch.cuda.LongTensor).view([-1,1]).data,1)\n",
    "        infer_input_one_hot = torch.autograd.Variable(target_one_hot_tr)\n",
    "        \n",
    "        attack_model_input = softmax(pred_outputs) #torch.cat((pred_outputs,infer_input_one_hot),1)\n",
    "#         print(attack_model_input.shape, infer_input_one_hot.shape)\n",
    "        member_output = inference_model(attack_model_input,infer_input_one_hot)\n",
    "        \n",
    "#         is_member_labels = torch.from_numpy(np.reshape(np.concatenate((np.zeros(v_tr_input.size(0)),np.ones(v_te_input.size(0)))),[-1,1])).cuda()\n",
    "        is_member_labels = torch.from_numpy(np.reshape(np.concatenate((np.ones(v_tr_input.size(0)),np.zeros(v_te_input.size(0)))),[-1,1])).cuda()\n",
    "\n",
    "        v_is_member_labels = torch.autograd.Variable(is_member_labels).type(torch.cuda.FloatTensor)\n",
    "        loss = criterion(member_output, v_is_member_labels)\n",
    "\n",
    "        # measure accuracy and record loss\n",
    "        prec1=np.mean((member_output.data.cpu().numpy() >0.5)==v_is_member_labels.data.cpu().numpy())\n",
    "        losses.update(loss.data, model_input.size(0))\n",
    "        top1.update(prec1, model_input.size(0))\n",
    "        \n",
    "#         print ( \"member_output: \" ,member_output.data.cpu().numpy())\n",
    "#         print(\"v_is_member_labels: \",v_is_member_labels.data.cpu().numpy())\n",
    "        # compute gradient and do SGD step\n",
    "        optimizer.zero_grad()\n",
    "        loss.backward()\n",
    "        optimizer.step()\n",
    "\n",
    "        # measure elapsed time\n",
    "        batch_time.update(time.time() - end)\n",
    "        end = time.time()\n",
    "        if batch_idx-first_id > num_batchs:\n",
    "            break\n",
    "\n",
    "        # plot progress\n",
    "        if batch_idx%10==0:\n",
    "            print  ('attack_train--({batch}/{size}) Data: {data:.3f}s | Batch: {bt:.3f}s | | Loss: {loss:.4f} | top1: {top1: .4f} '.format(\n",
    "                    batch=batch_idx ,\n",
    "                    size=100,\n",
    "                    data=data_time.avg,\n",
    "                    bt=batch_time.avg,\n",
    "                    loss=losses.avg,\n",
    "                    top1=top1.avg,\n",
    "                    ))\n",
    "    return (losses.avg, top1.avg)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "metadata": {
    "lines_to_next_cell": 2
   },
   "outputs": [],
   "source": [
    "\n",
    "\n",
    "def privacy_test_softmax(trainloader, model, inference_model, criterion, optimizer, epoch, use_cuda,num_batchs=1000):\n",
    "    global best_acc\n",
    "    batch_time = AverageMeter()\n",
    "    data_time = AverageMeter()\n",
    "    losses = AverageMeter()\n",
    "    top1 = AverageMeter()\n",
    "    mtop1_a = AverageMeter()\n",
    "    mtop5_a = AverageMeter()\n",
    "    first_id = -1\n",
    "    \n",
    "    softmax = nn.Softmax()\n",
    "    \n",
    "    inference_model.eval()\n",
    "    model.eval()\n",
    "    # switch to evaluate mode\n",
    "    end = time.time()\n",
    "    for batch_idx,((tr_input, tr_target) ,(te_input, te_target)) in trainloader:\n",
    "        # measure data loading time\n",
    "        if first_id == -1:\n",
    "            first_id = batch_idx\n",
    "        data_time.update(time.time() - end)\n",
    "        tr_input = tr_input.cuda()\n",
    "        te_input = te_input.cuda()\n",
    "        tr_target = tr_target.cuda()\n",
    "        te_target = te_target.cuda()\n",
    "        \n",
    "        \n",
    "        v_tr_input = torch.autograd.Variable(tr_input)\n",
    "        v_te_input = torch.autograd.Variable(te_input)\n",
    "        v_tr_target = torch.autograd.Variable(tr_target)\n",
    "        v_te_target = torch.autograd.Variable(te_target)\n",
    "        \n",
    "        # compute output\n",
    "        model_input =torch.cat((v_tr_input,v_te_input))\n",
    "        \n",
    "        pred_outputs,_,_,_,_,_,_,_  = model(model_input)\n",
    "#         pred_outputs = model(model_input)\n",
    "        \n",
    "        infer_input= torch.cat((v_tr_target,v_te_target))\n",
    "        \n",
    "        mtop1, mtop5 =accuracy(pred_outputs.data, infer_input.data, topk=(1, 5))\n",
    "        \n",
    "        mtop1_a.update(mtop1, model_input.size(0))\n",
    "        mtop5_a.update(mtop5, model_input.size(0))\n",
    "        \n",
    "        \n",
    "        one_hot_tr = torch.from_numpy((np.zeros((infer_input.size(0),pred_outputs.size(1)))-1)).cuda().type(torch.cuda.FloatTensor)\n",
    "        target_one_hot_tr = one_hot_tr.scatter_(1, infer_input.type(torch.cuda.LongTensor).view([-1,1]).data,1)\n",
    "        infer_input_one_hot = torch.autograd.Variable(target_one_hot_tr)\n",
    "        \n",
    "        attack_model_input = softmax(pred_outputs)#torch.cat((pred_outputs,infer_input_one_hot),1)\n",
    "        member_output = inference_model(attack_model_input,infer_input_one_hot)\n",
    "        \n",
    "        \n",
    "#         is_member_labels = torch.from_numpy(np.reshape(np.concatenate((np.zeros(v_tr_input.size(0)),np.ones(v_te_input.size(0)))),[-1,1])).cuda()\n",
    "        is_member_labels = torch.from_numpy(np.reshape(np.concatenate((np.ones(v_tr_input.size(0)),np.zeros(v_te_input.size(0)))),[-1,1])).cuda()\n",
    "        \n",
    "        v_is_member_labels = torch.autograd.Variable(is_member_labels).type(torch.cuda.FloatTensor)\n",
    "        loss = criterion(member_output, v_is_member_labels)\n",
    "\n",
    "        # measure accuracy and record loss\n",
    "        prec1=np.mean((member_output.data.cpu().numpy() >0.5)==v_is_member_labels.data.cpu().numpy())\n",
    "        losses.update(loss.data, model_input.size(0))\n",
    "        top1.update(prec1, model_input.size(0))\n",
    "\n",
    "        # compute gradient and do SGD step\n",
    "        \n",
    "        # measure elapsed time\n",
    "        batch_time.update(time.time() - end)\n",
    "        end = time.time()\n",
    "        if batch_idx-first_id > num_batchs:\n",
    "            break\n",
    "\n",
    "#         plot progress\n",
    "        if batch_idx%10==0:\n",
    "            print  ('privacy_test--({batch}/{size}) Data: {data:.3f}s | Batch: {bt:.3f}s | | Loss: {loss:.4f} | top1: {top1: .4f} '.format(\n",
    "                    batch=batch_idx ,\n",
    "                    size=100,\n",
    "                    data=data_time.avg,\n",
    "                    bt=batch_time.avg,\n",
    "                    loss=losses.avg,\n",
    "                    top1=top1.avg,\n",
    "                    ))\n",
    "#             if epoch == 9:\n",
    "#                 print ( \"member_output: \" ,member_output.data.cpu().numpy())\n",
    "#                 print(\"v_is_member_labels: \",v_is_member_labels.data.cpu().numpy())\n",
    "    return (losses.avg, top1.avg)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "metadata": {
    "lines_to_next_cell": 2
   },
   "outputs": [],
   "source": [
    "\n",
    "\n",
    "def train_attack_sort_softmax(trainloader, model, attack_model, criterion,\n",
    "                              attack_criterion, optimizer, attack_optimizer, epoch, use_cuda, num_batchs=100000,\n",
    "                              skip_batch=0):\n",
    "    # switch to train mode\n",
    "    model.eval()\n",
    "    attack_model.train()\n",
    "\n",
    "    softmax = nn.Softmax()\n",
    "    batch_size = att_batch_size\n",
    "    data_time = AverageMeter()\n",
    "    batch_time = AverageMeter()\n",
    "    losses = AverageMeter()\n",
    "    top1 = AverageMeter()\n",
    "    top5 = AverageMeter()\n",
    "\n",
    "    end = time.time()\n",
    "    # len_t = min((len(attack_data) // att_batch_size), (len(train_data) // att_batch_size)) + 1\n",
    "\n",
    "    # print (skip_batch, len_t)\n",
    "\n",
    "    for ind, ((tr_input, tr_target), (te_input, te_target)) in trainloader:\n",
    "        data_time.update(time.time() - end)\n",
    "        if use_cuda:\n",
    "            inputs, targets = tr_input.cuda(), tr_target.cuda()\n",
    "            inputs_attack, targets_attack = te_input.cuda(), te_target.cuda()\n",
    "\n",
    "        inputs, targets = torch.autograd.Variable(inputs), torch.autograd.Variable(targets)\n",
    "        inputs_attack, targets_attack = torch.autograd.Variable(inputs_attack), torch.autograd.Variable(targets_attack)\n",
    "\n",
    "        # compute output\n",
    "        outputs,_,_,_,_,_,_,_  = model(inputs)\n",
    "        outputs_non,_,_,_,_,_,_,_ = model(inputs_attack)\n",
    "\n",
    "        # classifier_input = torch.cat((inputs, inputs_attack))\n",
    "        comb_inputs = torch.cat((outputs, outputs_non))\n",
    "        sort_inputs, indices = torch.sort(comb_inputs)\n",
    "        # print (comb_inputs.size(),comb_targets.size())\n",
    "        attack_input = softmax(sort_inputs)  # torch.cat((comb_inputs,comb_targets),1)\n",
    "\n",
    "        attack_output, _ = attack_model(attack_input)\n",
    "        attack_output = attack_output.view([-1])\n",
    "        # attack_output = attack_model(attack_input).view([-1])\n",
    "        att_labels = np.zeros((inputs.size()[0] + inputs_attack.size()[0]))\n",
    "        att_labels[:inputs.size()[0]] = 1.0\n",
    "        att_labels[inputs.size()[0]:] = 0.0\n",
    "        is_member_labels = torch.from_numpy(att_labels).type(torch.FloatTensor)\n",
    "\n",
    "        if use_cuda:\n",
    "            is_member_labels = is_member_labels.cuda()\n",
    "\n",
    "        v_is_member_labels = torch.autograd.Variable(is_member_labels)\n",
    "\n",
    "        #         classifier_targets = comb_targets.clone().view([-1]).type(torch.cuda.LongTensor)\n",
    "\n",
    "        loss_attack = attack_criterion(attack_output, v_is_member_labels)\n",
    "\n",
    "        # measure accuracy and record loss\n",
    "        # prec1,p5 = accuracy(attack_output.data, v_is_member_labels.data, topk=(1,2))\n",
    "\n",
    "        prec1 = np.mean(\n",
    "            np.equal((attack_output.data.cpu().numpy() > 0.5), (v_is_member_labels.data.cpu().numpy() > 0.5)))\n",
    "        losses.update(loss_attack.data, attack_input.size()[0])\n",
    "        top1.update(prec1, attack_input.size()[0])\n",
    "\n",
    "        # print ( attack_output.data.cpu().numpy(),v_is_member_labels.data.cpu().numpy() ,attack_input.data.cpu().numpy())\n",
    "        # raise\n",
    "\n",
    "        # compute gradient and do SGD step\n",
    "        attack_optimizer.zero_grad()\n",
    "        loss_attack.backward()\n",
    "        attack_optimizer.step()\n",
    "\n",
    "        # measure elapsed time\n",
    "        batch_time.update(time.time() - end)\n",
    "        end = time.time()\n",
    "\n",
    "        # plot progress\n",
    "        if ind % 10 == 0:\n",
    "            print(\n",
    "                '({batch}/{size}) Data: {data:.3f}s | Batch: {bt:.3f}s | | Loss: {loss:.4f} | top1: {top1: .4f} '.format(\n",
    "                    batch=ind + 1,\n",
    "                    size=25,\n",
    "                    data=data_time.avg,\n",
    "                    bt=batch_time.avg,\n",
    "                    loss=losses.avg,\n",
    "                    top1=top1.avg,\n",
    "                ))\n",
    "    return (losses.avg, top1.avg)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "metadata": {
    "lines_to_next_cell": 2
   },
   "outputs": [],
   "source": [
    "\n",
    "\n",
    "def test_attack_sort_softmax(testloader, model, attack_model, criterion, attack_criterion,\n",
    "                             optimizer, attack_optimizer, epoch, use_cuda):\n",
    "    model.eval()\n",
    "    attack_model.eval()\n",
    "\n",
    "    softmax = nn.Softmax()\n",
    "    batch_size = att_batch_size\n",
    "    data_time = AverageMeter()\n",
    "    batch_time = AverageMeter()\n",
    "    losses = AverageMeter()\n",
    "    top1 = AverageMeter()\n",
    "    top5 = AverageMeter()\n",
    "\n",
    "    sum_correct = 0.0\n",
    "\n",
    "    end = time.time()\n",
    "    # len_t = min((len(attack_data) // batch_size), (len(train_data) // batch_size)) + 1\n",
    "\n",
    "    for ind, ((tr_input, tr_target), (te_input, te_target)) in testloader:\n",
    "\n",
    "        data_time.update(time.time() - end)\n",
    "        if use_cuda:\n",
    "            inputs, targets = tr_input.cuda(), tr_target.cuda()\n",
    "            inputs_attack, targets_attack = te_input.cuda(), te_target.cuda()\n",
    "        inputs, targets = torch.autograd.Variable(inputs), torch.autograd.Variable(targets)\n",
    "        inputs_attack, targets_attack = torch.autograd.Variable(inputs_attack), torch.autograd.Variable(targets_attack)\n",
    "\n",
    "        # compute output\n",
    "        outputs,_,_,_,_,_,_,_  = model(inputs)\n",
    "        outputs_non,_,_,_,_,_,_,_  = model(inputs_attack)\n",
    "\n",
    "        comb_inputs = torch.cat((outputs, outputs_non))\n",
    "        sort_inputs, indices = torch.sort(comb_inputs)\n",
    "        # print (comb_inputs.size(),comb_targets.size())\n",
    "        attack_input = softmax(sort_inputs)  # torch.cat((comb_inputs,comb_targets),1)\n",
    "\n",
    "        # attack_output = attack_model(att_inp).view([-1])\n",
    "        attack_output, _ = attack_model(attack_input)\n",
    "        attack_output = attack_output.view([-1])\n",
    "        # attack_output = attack_model(attack_input).view([-1])\n",
    "        att_labels = np.zeros((inputs.size()[0] + inputs_attack.size()[0]))\n",
    "        att_labels[:inputs.size()[0]] = 1.0\n",
    "        att_labels[inputs.size()[0]:] = 0.0\n",
    "        is_member_labels = torch.from_numpy(att_labels).type(torch.FloatTensor)\n",
    "        if use_cuda:\n",
    "            is_member_labels = is_member_labels.cuda()\n",
    "\n",
    "        v_is_member_labels = torch.autograd.Variable(is_member_labels)\n",
    "\n",
    "        loss = attack_criterion(attack_output, v_is_member_labels)\n",
    "\n",
    "        # measure accuracy and record loss\n",
    "        # prec1,p5 = accuracy(attack_output.data, v_is_member_labels.data, topk=(1,2))\n",
    "\n",
    "        prec1 = np.mean(\n",
    "            np.equal((attack_output.data.cpu().numpy() > 0.5), (v_is_member_labels.data.cpu().numpy() > 0.5)))\n",
    "        losses.update(loss.data, attack_input.size()[0])\n",
    "        top1.update(prec1, attack_input.size()[0])\n",
    "\n",
    "        correct = np.sum(\n",
    "            np.equal((attack_output.data.cpu().numpy() > 0.5), (v_is_member_labels.data.cpu().numpy() > 0.5)))\n",
    "        sum_correct += correct\n",
    "        # raise\n",
    "\n",
    "        # compute gradient and do SGD step\n",
    "        # measure elapsed time\n",
    "        batch_time.update(time.time() - end)\n",
    "        end = time.time()\n",
    "\n",
    "        # plot progress\n",
    "        if ind % 10 == 0:\n",
    "            print(\n",
    "                '({batch}/{size}) Data: {data:.3f}s | Batch: {bt:.3f}s | | Loss: {loss:.4f} | top1: {top1: .4f} '.format(\n",
    "                    batch=ind + 1,\n",
    "                    size=25,\n",
    "                    data=data_time.avg,\n",
    "                    bt=batch_time.avg,\n",
    "                    loss=losses.avg,\n",
    "                    top1=top1.avg,\n",
    "                ))\n",
    "\n",
    "    #         break\n",
    "    #     return (losses.avg, top1.avg)  #,prec1,attack_output,  v_is_member_labels)\n",
    "\n",
    "    return (losses.avg, top1.avg, sum_correct)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "\n",
    "\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "metadata": {
    "lines_to_next_cell": 2
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "./checkpoints_cifar100\n"
     ]
    }
   ],
   "source": [
    "\n",
    "\n",
    "dataset='cifar100'\n",
    "checkpoint_path='./checkpoints_cifar100'\n",
    "train_batch=200\n",
    "test_batch=100\n",
    "lr=0.001\n",
    "epochs=60\n",
    "state={}\n",
    "state['lr']=lr\n",
    "print(checkpoint_path)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "metadata": {
    "lines_to_next_cell": 2
   },
   "outputs": [],
   "source": [
    "\n",
    "\n",
    "def save_checkpoint(state, is_best, checkpoint='checkpoint', filename='checkpoint.pth.tar'):\n",
    "    filepath = os.path.join(checkpoint, filename)\n",
    "    torch.save(state, filepath)\n",
    "    if is_best:\n",
    "        shutil.copyfile(filepath, os.path.join(checkpoint, 'model_best.pth.tar'))\n",
    "\n",
    "def save_checkpoint_adversary(state, is_best, checkpoint='checkpoint', filename='checkpoint.pth.tar'):\n",
    "    filepath = os.path.join(checkpoint, filename)\n",
    "    torch.save(state, filepath)\n",
    "    if is_best:\n",
    "        shutil.copyfile(filepath, os.path.join(checkpoint, 'model_adversary_best.pth.tar'))\n",
    "        \n",
    "if not os.path.isdir(checkpoint_path):\n",
    "    mkdir_p(checkpoint_path)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "metadata": {
    "lines_to_next_cell": 2
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "==> Preparing dataset cifar100\n"
     ]
    }
   ],
   "source": [
    "\n",
    "\n",
    "# global best_acc\n",
    "start_epoch = 0  # start_ from epoch 0 or last checkpoint epoch\n",
    "\n",
    "# Data\n",
    "print('==> Preparing dataset %s' % dataset)\n",
    "mean = [x / 255 for x in [125.3, 123.0, 113.9]]\n",
    "std = [x / 255 for x in [63.0, 62.1, 66.7]]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "metadata": {
    "lines_to_next_cell": 2
   },
   "outputs": [],
   "source": [
    "\n",
    "\n",
    "transform_train = transforms.Compose([\n",
    "        #transforms.RandomCrop(32, padding=4),\n",
    "        #transforms.RandomHorizontalFlip(),\n",
    "        transforms.ToTensor(),\n",
    "        transforms.Normalize((0.4914, 0.4822, 0.4465), (0.2023, 0.1994, 0.2010)),\n",
    "    ])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "metadata": {},
   "outputs": [],
   "source": [
    "\n",
    "\n",
    "transform_test = transforms.Compose([\n",
    "        transforms.ToTensor(),\n",
    "        transforms.Normalize((0.4914, 0.4822, 0.4465), (0.2023, 0.1994, 0.2010)),\n",
    "    ])\n",
    "    \n",
    "    \n",
    "mean = [x / 255 for x in [125.3, 123.0, 113.9]]\n",
    "std = [x / 255 for x in [63.0, 62.1, 66.7]]\n",
    "\n",
    "\n",
    "# prepare test data parts"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "metadata": {},
   "outputs": [],
   "source": [
    "\n",
    "\n",
    "transform_test = transforms.Compose(\n",
    "    [transforms.ToTensor(), transforms.Normalize(mean, std)])\n",
    "    \n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 46,
   "metadata": {},
   "outputs": [],
   "source": [
    "\n",
    "\n",
    "if dataset == 'cifar10':\n",
    "    dataloader = datasets.CIFAR10\n",
    "    num_classes = 10\n",
    "else:\n",
    "    dataloader = datasets.CIFAR100\n",
    "    num_classes = 100\n",
    "\n",
    "\n",
    "# Model"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 47,
   "metadata": {
    "lines_to_next_cell": 2
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "==> creating model \n"
     ]
    }
   ],
   "source": [
    "\n",
    "\n",
    "print(\"==> creating model \")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 48,
   "metadata": {
    "lines_to_next_cell": 2
   },
   "outputs": [],
   "source": [
    "\n",
    "\n",
    "model = AlexNet(num_classes)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 49,
   "metadata": {
    "lines_to_next_cell": 2
   },
   "outputs": [],
   "source": [
    "\n",
    "\n",
    "model = model.cuda()\n",
    "# inferenece_model = torch.nn.DataParallel(inferenece_model).cuda()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 50,
   "metadata": {
    "lines_to_next_cell": 2
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "    Total params: 20.50M\n"
     ]
    }
   ],
   "source": [
    "\n",
    "\n",
    "cudnn.benchmark = True\n",
    "print('    Total params: %.2fM' % (sum(p.numel() for p in model.parameters())/1000000.0))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 51,
   "metadata": {
    "lines_to_next_cell": 2
   },
   "outputs": [],
   "source": [
    "\n",
    "\n",
    "criterion = nn.CrossEntropyLoss()\n",
    "criterion_attack = nn.MSELoss()\n",
    "optimizer = optim.SGD(model.parameters(), lr=lr, momentum=0.9, weight_decay=5e-4)\n",
    "optimizer_admm = optim.Adam(model.parameters(), lr=lr)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 52,
   "metadata": {
    "lines_to_next_cell": 2
   },
   "outputs": [],
   "source": [
    "\n",
    "\n",
    "# Resume\n",
    "title = 'cifar-100'"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 53,
   "metadata": {
    "lines_to_next_cell": 2
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Files already downloaded and verified\n"
     ]
    }
   ],
   "source": [
    "\n",
    "\n",
    "batch_privacy=200\n",
    "trainset = dataloader(root='./data100', train=True, download=True, transform=transform_train)\n",
    "trainloader = data.DataLoader(trainset, batch_size=batch_privacy, shuffle=True, num_workers=1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 54,
   "metadata": {
    "lines_to_next_cell": 2
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Files already downloaded and verified\n"
     ]
    }
   ],
   "source": [
    "\n",
    "\n",
    "trainset_private = dataloader(root='./data100', train=True, download=True, transform=transform_test)\n",
    "trainloader_private = data.DataLoader(trainset, batch_size=batch_privacy, shuffle=True, num_workers=1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 55,
   "metadata": {
    "lines_to_next_cell": 2
   },
   "outputs": [],
   "source": [
    "\n",
    "\n",
    "testset = dataloader(root='./data100', train=False, download=False, transform=transform_test)\n",
    "testloader = data.DataLoader(testset, batch_size=batch_privacy, shuffle=True, num_workers=1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 56,
   "metadata": {
    "lines_to_next_cell": 2
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Files already downloaded and verified\n"
     ]
    }
   ],
   "source": [
    "\n",
    "\n",
    "batch_privacy=100\n",
    "trainset = dataloader(root='./data100', train=True, download=True, transform=transform_test)\n",
    "testset = dataloader(root='./data100', train=False, download=False, transform=transform_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 57,
   "metadata": {
    "lines_to_next_cell": 2
   },
   "outputs": [],
   "source": [
    "\n",
    "\n",
    "r = np.arange(50000)\n",
    "# np.random.shuffle(r)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 58,
   "metadata": {
    "lines_to_next_cell": 2
   },
   "outputs": [],
   "source": [
    "\n",
    "\n",
    "private_trainset_intrain = []\n",
    "private_trainset_intest = []"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 59,
   "metadata": {
    "lines_to_next_cell": 2
   },
   "outputs": [],
   "source": [
    "\n",
    "\n",
    "private_testset_intrain =[] \n",
    "private_testset_intest =[] "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 60,
   "metadata": {
    "lines_to_next_cell": 2
   },
   "outputs": [],
   "source": [
    "\n",
    "\n",
    "for i in range(25000):\n",
    "    private_trainset_intrain.append(trainset[r[i]])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 61,
   "metadata": {
    "lines_to_next_cell": 2
   },
   "outputs": [],
   "source": [
    "\n",
    "\n",
    "for i in range(25000,50000):\n",
    "    private_testset_intrain.append(trainset[r[i]])\n",
    "    \n",
    "r = np.arange(10000)\n",
    "# np.random.shuffle(r)\n",
    "  \n",
    "for i in range(5000):\n",
    "    private_trainset_intest.append(testset[r[i]])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 62,
   "metadata": {
    "lines_to_next_cell": 2
   },
   "outputs": [],
   "source": [
    "\n",
    "\n",
    "for i in range(5000,10000):\n",
    "    private_testset_intest.append(testset[r[i]])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 63,
   "metadata": {
    "lines_to_next_cell": 2
   },
   "outputs": [],
   "source": [
    "\n",
    "\n",
    "private_trainloader_intrain = data.DataLoader(private_trainset_intrain, batch_size=batch_privacy, shuffle=True, num_workers=1)\n",
    "private_trainloader_intest = data.DataLoader(private_trainset_intest, batch_size=batch_privacy, shuffle=True, num_workers=1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 64,
   "metadata": {
    "lines_to_next_cell": 2
   },
   "outputs": [],
   "source": [
    "\n",
    "\n",
    "private_testloader_intrain = data.DataLoader(private_testset_intrain, batch_size=batch_privacy, shuffle=True, num_workers=1)\n",
    "private_testloader_intest = data.DataLoader(private_testset_intest, batch_size=batch_privacy, shuffle=True, num_workers=1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 65,
   "metadata": {
    "lines_to_next_cell": 2
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "./checkpoints_cifar100\n"
     ]
    }
   ],
   "source": [
    "\n",
    "\n",
    "# start train\n",
    "dataset='cifar100'\n",
    "checkpoint_path='./checkpoints_cifar100'\n",
    "# train_batch=400\n",
    "# test_batch=200\n",
    "lr=0.01\n",
    "# lr=0.0001\n",
    "epochs=100\n",
    "state={}\n",
    "state['lr']=lr\n",
    "print(checkpoint_path)\n",
    "# model = AlexNet(num_classes)\n",
    "# model = AlexNet_fc(num_classes, q = 90, alpha = 0.5)\n",
    "# model = AlexNet_fc_replace(num_classes, q = 90, alpha = 1)\n",
    "# model = AlexNet_conv(num_classes, q = 90, alpha = 1.5)\n",
    "model = AlexNet_comb_mid(num_classes,qconv_l = 100,qconv_h = 90, qfc_h =100, aconv_h = 2, afc_h = 1)\n",
    "# model = AlexNet_comb_rand(num_classes, qconv_h = 90, aconv_h = 2)\n",
    "# model = AlexNet_comb_rand_mod1(num_classes, qconv_h = 90, aconv_h = 2)\n",
    "\n",
    "# model = AlexNet_b(num_classes)\n",
    "# model = AlexNet_mod(num_classes)\n",
    "model = model.cuda()\n",
    "criterion = nn.CrossEntropyLoss()\n",
    "optimizer = optim.SGD(model.parameters(), lr=lr, momentum=0.9, weight_decay=5e-4)\n",
    "# optimizer = optim.Adam(model.parameters(), lr=lr)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "\n",
    "\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "lines_to_next_cell": 2
   },
   "outputs": [],
   "source": [
    "\n",
    "\n",
    "print(model)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "lines_to_next_cell": 2
   },
   "outputs": [],
   "source": [
    "\n",
    "\n",
    "def adjust_learning_rate(optimizer, epoch):\n",
    "    global state\n",
    "    if epoch in [700, 1500]:\n",
    "        state['lr'] *= 10\n",
    "        for param_group in optimizer.param_groups:\n",
    "            param_group['lr'] = state['lr']\n",
    "    if epoch in [1800,100,900,150]:\n",
    "        state['lr'] *= 0.1 \n",
    "#         state['lr'] *= 1\n",
    "        for param_group in optimizer.param_groups:\n",
    "            param_group['lr'] = state['lr']\n",
    "#     elif epoch == 60:\n",
    "#         state['lr'] = 0.005\n",
    "#         for param_group in optimizer.param_groups:\n",
    "#             param_group['lr'] = state['lr']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "\n",
    "\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "lines_to_next_cell": 2
   },
   "outputs": [],
   "source": [
    "# code for training defense model\n",
    "\n",
    "#  with random clip\n",
    "is_best=False\n",
    "best_acc=0.0\n",
    "start_epoch=0\n",
    "alpha = 2\n",
    "beta = 0\n",
    "num_class = 100\n",
    "epochs=150\n",
    "mean_class = np.zeros((num_class,num_class))\n",
    "var_n = np.zeros(num_class)\n",
    "\n",
    "test_acc_res = []\n",
    "test_loss_res = []\n",
    "train_acc_res = []\n",
    "train_loss_res = []\n",
    "# Train and val\n",
    "for epoch in range(start_epoch, epochs):\n",
    "    adjust_learning_rate(optimizer, epoch)\n",
    "    print('\\nEpoch: [%d | %d] LR: %f' % (epoch + 1, epochs, state['lr']))\n",
    "#     train_enum = enumerate(trainloader)\n",
    "#     train_private_enum = enumerate(zip(trainloader_private,testloader))\n",
    "    train_loss,train_var,train_svar, train_acc = train_half_diff_fc_min_var(trainloader, model, criterion, optimizer, mean_class, var_n, epoch, use_cuda, num_class = num_class, alpha = alpha, beta = beta)\n",
    "\n",
    "    train_acc_res.append(train_acc.item())\n",
    "    train_loss_res.append(train_loss.item())\n",
    "    \n",
    "    test_loss, test_acc = test(testloader, model, criterion, epoch, use_cuda)\n",
    "    test_acc_res.append(test_acc.item())\n",
    "    test_loss_res.append(test_loss.item())\n",
    "    \n",
    "    is_best = test_acc>best_acc\n",
    "    best_acc = max(test_acc, best_acc)\n",
    "    print ('test_loss: {test_loss: .4f}, test_acc: {test_acc: .4f}, best_acc: {best_acc: .4f}'.format(test_loss=test_loss, test_acc=test_acc, best_acc=best_acc))\n",
    "    \n",
    "    # save model\n",
    "#     is_best = test_acc>best_acc\n",
    "#     best_acc = max(test_acc, best_acc) \n",
    "    if (is_best) or epoch+1 == epochs:\n",
    "        if test_acc > 36:\n",
    "            save_checkpoint({\n",
    "                    'epoch': epoch + 1,\n",
    "                    'state_dict': model.state_dict(),\n",
    "                    'acc': test_acc,\n",
    "                    'best_acc': best_acc,\n",
    "                    'optimizer' : optimizer.state_dict(),\n",
    "                }, is_best, checkpoint=checkpoint_path,filename='cifar100_fc_balance_ouput_partition_alpha2_conv90a1.5_defense_epoch%d'%(epoch+1))\n",
    "\n",
    "    \n",
    "print('Best acc:')\n",
    "print(best_acc)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "\n",
    "\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "# coda for trianing the non-defensive model\n",
    "\n",
    "#  with random clip\n",
    "is_best=False\n",
    "# best_acc=0.0\n",
    "start_epoch=60\n",
    "# test_acc_res = []\n",
    "# test_loss_res = []\n",
    "# train_acc_res = []\n",
    "# train_loss_res = []\n",
    "epochs=100\n",
    "# Train and val\n",
    "for epoch in range(start_epoch, epochs):\n",
    "    adjust_learning_rate(optimizer, epoch)\n",
    "    print('\\nEpoch: [%d | %d] LR: %f' % (epoch + 1, epochs, state['lr']))\n",
    "\n",
    "    train_loss, train_acc = train(trainloader, model, criterion, optimizer, epoch, use_cuda)\n",
    "    train_acc_res.append(train_acc.item())\n",
    "    train_loss_res.append(train_loss.item())\n",
    "    \n",
    "    test_loss, test_acc = test(testloader, model, criterion, epoch, use_cuda)\n",
    "    test_acc_res.append(test_acc.item())\n",
    "    test_loss_res.append(test_loss.item())\n",
    "    \n",
    "    is_best = test_acc>best_acc\n",
    "    best_acc = max(test_acc, best_acc)\n",
    "    print ('test_loss: {test_loss: .4f}, test_acc: {test_acc: .4f}, best_acc: {best_acc: .4f}'.format(test_loss=test_loss, test_acc=test_acc, best_acc=best_acc))\n",
    "    \n",
    "    # save model\n",
    "#     is_best = test_acc>best_acc\n",
    "#     best_acc = max(test_acc, best_acc) \n",
    "    if (is_best) or epoch+1 == epochs:\n",
    "        if best_acc > 30:\n",
    "            save_checkpoint({\n",
    "                    'epoch': epoch + 1,\n",
    "                    'state_dict': model.state_dict(),\n",
    "                    'acc': test_acc,\n",
    "                    'best_acc': best_acc,\n",
    "                    'optimizer' : optimizer.state_dict(),\n",
    "                }, is_best, checkpoint=checkpoint_path,filename='cifar100_ori_epoch%d'%(epoch+1))\n",
    "    \n",
    "    \n",
    "print('Best acc:')\n",
    "print(best_acc)\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 66,
   "metadata": {
    "lines_to_next_cell": 2
   },
   "outputs": [],
   "source": [
    "# load saved model\n",
    "\n",
    "# model = AlexNet(num_classes)\n",
    "# model = AlexNet_fc(num_classes, q = 50, alpha = 20)\n",
    "# model = AlexNet_conv(num_classes, q = 75, alpha = 2)\n",
    "model = AlexNet_comb_mid(num_classes,qconv_l = 100,qconv_h = 75, qfc_h =100, afc_h =1, aconv_l = 1, aconv_h=2)\n",
    "# model = AlexNet_comb_rand(num_classes, qconv_l = 70, aconv_l = 2, qconv_h = 100, aconv_h = 1.5)\n",
    "# model = AlexNet_comb_rand(num_classes,  qconv_h = 70, aconv_h = 2)\n",
    "# model = AlexNet_comb_rand_mod1(num_classes, qconv_h = 60, aconv_h = 2)\n",
    "# model = AlexNet_mod(num_classes)s\n",
    "model = model.cuda()\n",
    "criterion = nn.CrossEntropyLoss()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 67,
   "metadata": {
    "lines_to_next_cell": 2
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "==> Resuming from checkpoint..\n"
     ]
    }
   ],
   "source": [
    "\n",
    "\n",
    "#training and testing the attack result from the checkpoint\n",
    "\n",
    "resume='./checkpoints_cifar100/cifar100_min_var_beta1000_conv90a2_nonzero_defense_epoch44'\n",
    "# resume='./checkpoints_cifar100/cifar100_alexnet_epoch19'\n",
    "# resume='./checkpoints_cifar100/cifar100_fc_balance_ouput_partition_alpha2_conv90a1.5_defense_epoch61'\n",
    "# resume='./checkpoints_cifar100/cifar100_epoch21'\n",
    "# resume='./checkpoints_cifar100/adversary_reg_1_alpha6_epoch61'\n",
    "# resume='./checkpoints_cifar100/cifar100_min_var_beta2000_defense_epoch62'\n",
    "# resume='./checkpoints_cifar100/cifar100_min_var_beta200_defense_epoch62'\n",
    "print('==> Resuming from checkpoint..')\n",
    "assert os.path.isfile(resume), 'Error: no checkpoint directory found!'\n",
    "checkpoint = torch.load(resume)\n",
    "\n",
    "model.load_state_dict(checkpoint['state_dict'])\n",
    "optimizer.load_state_dict(checkpoint['optimizer']) \n",
    "epoch=checkpoint['epoch']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 68,
   "metadata": {
    "lines_to_next_cell": 2
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/tmp/ipykernel_2273635/298495222.py:17: UserWarning: volatile was removed and now has no effect. Use `with torch.no_grad():` instead.\n",
      "  inputs, targets = torch.autograd.Variable(inputs, volatile=True), torch.autograd.Variable(targets)\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(1/50) Data: 0.112s | Batch: 2.721s | Loss: 4.0362 | top1:  42.5000 | top5:  68.0000\n",
      "(21/50) Data: 0.009s | Batch: 0.218s | Loss: 4.0403 | top1:  42.3810 | top5:  70.8333\n",
      "(41/50) Data: 0.005s | Batch: 0.157s | Loss: 4.0400 | top1:  42.6098 | top5:  70.7195\n",
      "Classification accuracy: 42.72\n",
      "(1/250) Data: 0.154s | Batch: 0.328s | Loss: 3.8386 | top1:  63.5000 | top5:  88.5000\n",
      "(21/250) Data: 0.009s | Batch: 0.107s | Loss: 3.8901 | top1:  58.0476 | top5:  84.0000\n",
      "(41/250) Data: 0.006s | Batch: 0.103s | Loss: 3.8958 | top1:  57.2195 | top5:  83.7805\n",
      "(61/250) Data: 0.005s | Batch: 0.101s | Loss: 3.8945 | top1:  57.2951 | top5:  83.9344\n",
      "(81/250) Data: 0.004s | Batch: 0.100s | Loss: 3.8961 | top1:  57.0000 | top5:  83.8395\n",
      "(101/250) Data: 0.004s | Batch: 0.099s | Loss: 3.8959 | top1:  56.8564 | top5:  83.8168\n",
      "(121/250) Data: 0.003s | Batch: 0.099s | Loss: 3.8960 | top1:  56.9421 | top5:  83.7851\n",
      "(141/250) Data: 0.003s | Batch: 0.099s | Loss: 3.8961 | top1:  56.9291 | top5:  83.7234\n",
      "(161/250) Data: 0.003s | Batch: 0.099s | Loss: 3.8950 | top1:  56.8447 | top5:  83.7702\n",
      "(181/250) Data: 0.003s | Batch: 0.099s | Loss: 3.8942 | top1:  57.0055 | top5:  83.7983\n",
      "(201/250) Data: 0.003s | Batch: 0.099s | Loss: 3.8951 | top1:  56.8881 | top5:  83.6443\n",
      "(221/250) Data: 0.003s | Batch: 0.099s | Loss: 3.8940 | top1:  56.9706 | top5:  83.7262\n",
      "(241/250) Data: 0.003s | Batch: 0.099s | Loss: 3.8935 | top1:  57.0332 | top5:  83.7635\n",
      "Trainset Classification accuracy: 56.99\n"
     ]
    }
   ],
   "source": [
    "\n",
    "\n",
    "test_loss, final_test_acc = test(testloader, model, criterion, epoch, use_cuda)\n",
    "print ('Classification accuracy: %.2f'%(final_test_acc))\n",
    "\n",
    "train_loss, final_train_acc = test(trainloader, model, criterion, epoch, use_cuda)\n",
    "print ('Trainset Classification accuracy: %.2f'%(final_train_acc))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "\n",
    "\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "\n",
    "\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 89,
   "metadata": {
    "lines_to_next_cell": 2
   },
   "outputs": [],
   "source": [
    "# load for c&w label-only attack\n",
    "\n",
    "def get_max_accuracy(y_true, probs, thresholds=None):\n",
    "    \n",
    "    \"\"\"Return the max accuracy possible given the correct labels and guesses. Will try all thresholds unless passed.\"\"\"\n",
    "\n",
    "    \"\"\"\n",
    "      Args:\n",
    "        y_true: True label of `in' or `out' (member or non-member, 1/0)\n",
    "        probs: The scalar to threshold\n",
    "        thresholds: In a blackbox setup with a shadow/source model, the threshold obtained by the source model can be passed\n",
    "          here for attackin the target model. This threshold will then be used.\n",
    "\n",
    "      Returns: max accuracy possible, accuracy at the threshold passed (if one was passed), the max precision possible,\n",
    "       and the precision at the threshold passed.\n",
    "    \"\"\"\n",
    "    if thresholds is None:\n",
    "        fpr, tpr, thresholds = roc_curve(y_true, probs)\n",
    "\n",
    "    accuracy_scores = []\n",
    "    precision_scores = []\n",
    "    for thresh in thresholds:\n",
    "        accuracy_scores.append(accuracy_score(y_true,\n",
    "                                              [1 if m > thresh else 0 for m in probs]))\n",
    "        precision_scores.append(precision_score(y_true, [1 if m > thresh else 0 for m in probs]))\n",
    "\n",
    "    accuracies = np.array(accuracy_scores)\n",
    "    precisions = np.array(precision_scores)\n",
    "    max_accuracy = accuracies.max()\n",
    "    max_precision = precisions.max()\n",
    "    max_accuracy_threshold = thresholds[accuracies.argmax()]\n",
    "    max_precision_threshold = thresholds[precisions.argmax()]\n",
    "    return max_accuracy, max_accuracy_threshold, max_precision, max_precision_threshold\n",
    "\n",
    "\n",
    "\n",
    "def get_threshold(source_m, source_stats, target_m, target_stats):\n",
    "    \"\"\" Train a threshold attack model and get teh accuracy on source and target models.\n",
    "\n",
    "  Args:\n",
    "    source_m: membership labels for source dataset (1 for member, 0 for non-member)\n",
    "    source_stats: scalar values to threshold (attack features) for source dataset\n",
    "    target_m: membership labels for target dataset (1 for member, 0 for non-member)\n",
    "    target_stats: scalar values to threshold (attack features) for target dataset\n",
    "\n",
    "  Returns: best acc from source thresh, precision @ same threshold, threshold for best acc,\n",
    "    precision at the best threshold for precision. all tuned on source model.\n",
    "\n",
    "    \"\"\"\n",
    "    # find best threshold on source data\n",
    "    acc_source, t, prec_source, tprec = get_max_accuracy(source_m, source_stats)\n",
    "\n",
    "    # find best accuracy on test data (just to check how much we overfit)\n",
    "    acc_test, _, prec_test, _ = get_max_accuracy(target_m, target_stats)\n",
    "\n",
    "    # get the test accuracy at the threshold selected on the source data\n",
    "    acc_test_t, _, _, _ = get_max_accuracy(target_m, target_stats, thresholds=[t])\n",
    "    _, _, prec_test_t, _ = get_max_accuracy(target_m, target_stats, thresholds=[tprec])\n",
    "    print(\"acc src: {}, acc test (best thresh): {}, acc test (src thresh): {}, thresh: {}\".format(acc_source, acc_test,\n",
    "                                                                                                acc_test_t, t))\n",
    "    print(\n",
    "    \"prec src: {}, prec test (best thresh): {}, prec test (src thresh): {}, thresh: {}\".format(prec_source, prec_test,\n",
    "                                                                                               prec_test_t, tprec))\n",
    "\n",
    "    return acc_test_t, prec_test_t, t, tprec"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 91,
   "metadata": {
    "lines_to_next_cell": 2
   },
   "outputs": [],
   "source": [
    "#  max_iterations is set to be 100 to make the attack faster, the orginal results are done with max_iterations=1000\n",
    "\n",
    "\n",
    "INF = float(\"inf\")\n",
    "\n",
    "\n",
    "def carlini_wagner_l2(\n",
    "    model_fn,\n",
    "    x,\n",
    "    n_classes,\n",
    "    y=None,\n",
    "    targeted=False,\n",
    "    lr=5e-3,\n",
    "    confidence=0,\n",
    "    clip_min=0,\n",
    "    clip_max=1,\n",
    "    initial_const=1e-2,\n",
    "    binary_search_steps=5,\n",
    "    max_iterations=100,\n",
    "):\n",
    "    \"\"\"\n",
    "    This attack was originally proposed by Carlini and Wagner. It is an\n",
    "    iterative attack that finds adversarial examples on many defenses that\n",
    "    are robust to other attacks.\n",
    "    Paper link: https://arxiv.org/abs/1608.04644\n",
    "\n",
    "    At a high level, this attack is an iterative attack using Adam and\n",
    "    a specially-chosen loss function to find adversarial examples with\n",
    "    lower distortion than other attacks. This comes at the cost of speed,\n",
    "    as this attack is often much slower than others.\n",
    "\n",
    "    :param model_fn: a callable that takes an input tensor and returns\n",
    "              the model logits. The logits should be a tensor of shape\n",
    "              (n_examples, n_classes).\n",
    "    :param x: input tensor of shape (n_examples, ...), where ... can\n",
    "              be any arbitrary dimension that is compatible with\n",
    "              model_fn.\n",
    "    :param n_classes: the number of classes.\n",
    "    :param y: (optional) Tensor with true labels. If targeted is true,\n",
    "              then provide the target label. Otherwise, only provide\n",
    "              this parameter if you'd like to use true labels when\n",
    "              crafting adversarial samples. Otherwise, model predictions\n",
    "              are used as labels to avoid the \"label leaking\" effect\n",
    "              (explained in this paper:\n",
    "              https://arxiv.org/abs/1611.01236). If provide y, it\n",
    "              should be a 1D tensor of shape (n_examples, ).\n",
    "              Default is None.\n",
    "    :param targeted: (optional) bool. Is the attack targeted or\n",
    "              untargeted? Untargeted, the default, will try to make the\n",
    "              label incorrect. Targeted will instead try to move in the\n",
    "              direction of being more like y.\n",
    "    :param lr: (optional) float. The learning rate for the attack\n",
    "              algorithm. Default is 5e-3.\n",
    "    :param confidence: (optional) float. Confidence of adversarial\n",
    "              examples: higher produces examples with larger l2\n",
    "              distortion, but more strongly classified as adversarial.\n",
    "              Default is 0.\n",
    "    :param clip_min: (optional) float. Minimum float value for\n",
    "              adversarial example components. Default is 0.\n",
    "    :param clip_max: (optional) float. Maximum float value for\n",
    "              adversarial example components. Default is 1.\n",
    "    :param initial_const: The initial tradeoff-constant to use to tune the\n",
    "              relative importance of size of the perturbation and\n",
    "              confidence of classification. If binary_search_steps is\n",
    "              large, the initial constant is not important. A smaller\n",
    "              value of this constant gives lower distortion results.\n",
    "              Default is 1e-2.\n",
    "    :param binary_search_steps: (optional) int. The number of times we\n",
    "              perform binary search to find the optimal tradeoff-constant\n",
    "              between norm of the perturbation and confidence of the\n",
    "              classification. Default is 5.\n",
    "    :param max_iterations: (optional) int. The maximum number of\n",
    "              iterations. Setting this to a larger value will produce\n",
    "              lower distortion results. Using only a few iterations\n",
    "              requires a larger learning rate, and will produce larger\n",
    "              distortion results. Default is 1000.\n",
    "    \"\"\"\n",
    "\n",
    "    def compare(pred, label, is_logits=False):\n",
    "        \"\"\"\n",
    "        A helper function to compare prediction against a label.\n",
    "        Returns true if the attack is considered successful.\n",
    "\n",
    "        :param pred: can be either a 1D tensor of logits or a predicted\n",
    "                class (int).\n",
    "        :param label: int. A label to compare against.\n",
    "        :param is_logits: (optional) bool. If True, treat pred as an\n",
    "                array of logits. Default is False.\n",
    "        \"\"\"\n",
    "\n",
    "        # Convert logits to predicted class if necessary\n",
    "        if is_logits:\n",
    "            pred_copy = pred.clone().detach()\n",
    "            pred_copy[label] += -confidence if targeted else confidence\n",
    "            pred = torch.argmax(pred_copy)\n",
    "\n",
    "        return pred == label if targeted else pred != label\n",
    "\n",
    "    if y is None:\n",
    "        # Using model predictions as ground truth to avoid label leaking\n",
    "        pred = model_fn(x)\n",
    "        y = torch.argmax(pred, 1)\n",
    "\n",
    "    # Initialize some values needed for binary search on const\n",
    "    lower_bound = [0.0] * len(x)\n",
    "    upper_bound = [1e10] * len(x)\n",
    "    const = x.new_ones(len(x), 1) * initial_const\n",
    "\n",
    "    o_bestl2 = [INF] * len(x)\n",
    "    o_bestscore = [-1.0] * len(x)\n",
    "    x = torch.clamp(x, clip_min, clip_max)\n",
    "    ox = x.clone().detach()  # save the original x\n",
    "    o_bestattack = x.clone().detach()\n",
    "\n",
    "    # Map images into the tanh-space\n",
    "    x = (x - clip_min) / (clip_max - clip_min)\n",
    "    x = torch.clamp(x, 0, 1)\n",
    "    x = x * 2 - 1\n",
    "    x = torch.arctanh(x * 0.999999)\n",
    "    # x = torch.atanh(x * 0.999999)\n",
    "\n",
    "    # Prepare some variables\n",
    "    modifier = torch.zeros_like(x, requires_grad=True)\n",
    "    y_onehot = torch.nn.functional.one_hot(y, n_classes).to(torch.float)\n",
    "\n",
    "    # Define loss functions and optimizer\n",
    "    f_fn = lambda real, other, targeted: torch.max(\n",
    "        ((other - real) if targeted else (real - other)) + confidence,\n",
    "        torch.tensor(0.0).to(real.device),\n",
    "    )\n",
    "    l2dist_fn = lambda x, y: torch.pow(x - y, 2).sum(list(range(len(x.size())))[1:])\n",
    "    optimizer = torch.optim.Adam([modifier], lr=lr)\n",
    "\n",
    "    # Outer loop performing binary search on const\n",
    "    for outer_step in range(binary_search_steps):\n",
    "        # Initialize some values needed for the inner loop\n",
    "        bestl2 = [INF] * len(x)\n",
    "        bestscore = [-1.0] * len(x)\n",
    "\n",
    "        # Inner loop performing attack iterations\n",
    "        for i in range(max_iterations):\n",
    "            # One attack step\n",
    "            new_x = (torch.tanh(modifier + x) + 1) / 2\n",
    "            new_x = new_x * (clip_max - clip_min) + clip_min\n",
    "            logits,_,_,_,_,_,_,_ = model_fn(new_x)\n",
    "#             print(logits.shape)\n",
    "#             print(logits[0])\n",
    "#             print(y_onehot.shape)\n",
    "#             print(y_onehot[0])\n",
    "            real = torch.sum(y_onehot * logits, 1)\n",
    "            other, _ = torch.max((1 - y_onehot) * logits - y_onehot * 1e4, 1)\n",
    "\n",
    "            optimizer.zero_grad()\n",
    "            f = f_fn(real, other, targeted)\n",
    "            l2 = l2dist_fn(new_x, ox)\n",
    "            loss = (const * f + l2).sum()\n",
    "            loss.backward()\n",
    "            optimizer.step()\n",
    "\n",
    "            # Update best results\n",
    "            for n, (l2_n, logits_n, new_x_n) in enumerate(zip(l2, logits, new_x)):\n",
    "                y_n = y[n]\n",
    "                succeeded = compare(logits_n, y_n, is_logits=True)\n",
    "                if l2_n < o_bestl2[n] and succeeded:\n",
    "                    pred_n = torch.argmax(logits_n)\n",
    "                    o_bestl2[n] = l2_n\n",
    "                    o_bestscore[n] = pred_n\n",
    "                    o_bestattack[n] = new_x_n\n",
    "                    # l2_n < o_bestl2[n] implies l2_n < bestl2[n] so we modify inner loop variables too\n",
    "                    bestl2[n] = l2_n\n",
    "                    bestscore[n] = pred_n\n",
    "                elif l2_n < bestl2[n] and succeeded:\n",
    "                    bestl2[n] = l2_n\n",
    "                    bestscore[n] = torch.argmax(logits_n)\n",
    "\n",
    "        # Binary search step\n",
    "        for n in range(len(x)):\n",
    "            y_n = y[n]\n",
    "\n",
    "            if compare(bestscore[n], y_n) and bestscore[n] != -1:\n",
    "                # Success, divide const by two\n",
    "                upper_bound[n] = min(upper_bound[n], const[n])\n",
    "                if upper_bound[n] < 1e9:\n",
    "                    const[n] = (lower_bound[n] + upper_bound[n]) / 2\n",
    "            else:\n",
    "                # Failure, either multiply by 10 if no solution found yet\n",
    "                # or do binary search with the known upper bound\n",
    "                lower_bound[n] = max(lower_bound[n], const[n])\n",
    "                if upper_bound[n] < 1e9:\n",
    "                    const[n] = (lower_bound[n] + upper_bound[n]) / 2\n",
    "                else:\n",
    "                    const[n] *= 10\n",
    "\n",
    "    return o_bestattack.detach()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 92,
   "metadata": {
    "lines_to_next_cell": 2
   },
   "outputs": [],
   "source": [
    "\n",
    "\n",
    "def dists(model_fn, dataloader, attack=\"CW\", max_samples=100, input_dim=[None, 32, 32, 3], n_classes=10):\n",
    "    \"\"\"Calculate untargeted distance to decision boundary for Adv-x MI attack.\n",
    "      :param model: model to approximate distances on (attack).\n",
    "      :param ds: tf dataset should be either the training set or the test set.\n",
    "      :param attack: \"CW\" for carlini wagner or \"HSJ\" for hop skip jump\n",
    "      :param max_samples: maximum number of samples to take from the ds\n",
    "      :return: an array of the first samples from the ds, of len max_samples, with the untargeted distances. \n",
    "    \"\"\"\n",
    "#   # switch to TF1 style\n",
    "#   sess = K.get_session()\n",
    "#   x = tf.placeholder(dtype=tf.float32, shape=input_dim)\n",
    "#   y = tf.placeholder(dtype=tf.int32, shape=[None, n_classes])\n",
    "#   output = model_(x)\n",
    "#   model = CallableModelWrapper(lambda x: model_(x), \"logits\")\n",
    "    \n",
    "    mean = (0.4914, 0.4822, 0.4465)\n",
    "    std = (0.2023, 0.1994, 0.2010)\n",
    "    if attack == \"CW\":\n",
    "        acc = []\n",
    "        acc_adv = []\n",
    "        dist_adv = []\n",
    "        num_samples = 0\n",
    "\n",
    "        for batch_idx, (inputs, targets) in enumerate(dataloader):\n",
    "            # measure data loading time\n",
    "            if use_cuda:\n",
    "                inputs, targets = inputs.cuda(), targets.cuda()\n",
    "            inputs, targets = torch.autograd.Variable(inputs, volatile=True), torch.autograd.Variable(targets)\n",
    "\n",
    "            # compute output\n",
    "        #         outputs,_,_,_,_,_,_,_,_ = model(inputs)\n",
    "            outputs,_,_,_,_,_,_,_ = model(inputs)\n",
    "\n",
    "            outputs_value = outputs.data.cpu().numpy()\n",
    "            correct = torch.argmax(outputs, axis=-1) == targets\n",
    "            acc.extend(correct)\n",
    "            \n",
    "            input_shape = inputs.shape\n",
    "            x_adv_list = []\n",
    "            for i in range(len(inputs)):\n",
    "                if correct[i]:\n",
    "                    x_adv_curr = carlini_wagner_l2(model, inputs[i].reshape(1,input_shape[1],input_shape[2],input_shape[3]), \n",
    "                                                   n_classes, targeted=False, y=targets[i].reshape(1),clip_min=inputs[i].min(),\n",
    "                                                   clip_max = inputs[i].max())\n",
    "                else:\n",
    "                    x_adv_curr = inputs[i:i+1]\n",
    "                x_adv_list.append(x_adv_curr)\n",
    "            x_adv_list = torch.cat(x_adv_list, axis=0)\n",
    "            y_pred_adv,_,_,_,_,_,_,_ = model(x_adv_list)\n",
    "            corr_adv = torch.argmax(y_pred_adv, axis=-1) == targets\n",
    "            acc_adv.extend(corr_adv)\n",
    "\n",
    "            n_img = inputs.permute(0,2,3,1).data.cpu().numpy()\n",
    "            img = (n_img*std)+mean\n",
    "            n_x_adv_list = x_adv_list.permute(0,2,3,1).data.cpu().numpy()\n",
    "            x = (n_x_adv_list*std)+mean\n",
    "\n",
    "            d = np.sqrt(np.sum(np.square(x-img), axis=(1,2,3)))\n",
    "#             d = torch.sqrt(torch.sum(torch.square(x_adv_list-inputs), axis=(1,2,3)))\n",
    "#             dist_adv.extend(d.data.cpu().numpy())\n",
    "            dist_adv.extend(d)\n",
    "\n",
    "            num_samples += len(outputs)\n",
    "            print(\"processed {} examples\".format(num_samples))\n",
    "            if num_samples > max_samples:\n",
    "                return dist_adv[:max_samples]\n",
    "    \n",
    "    elif attack == \"HSJ\":\n",
    "        \n",
    "        acc = []\n",
    "        acc_adv = []\n",
    "        dist_adv = []\n",
    "        num_samples = 0\n",
    "\n",
    "        for batch_idx, (inputs, targets) in enumerate(dataloader):\n",
    "            # measure data loading time\n",
    "            if use_cuda:\n",
    "                inputs, targets = inputs.cuda(), targets.cuda()\n",
    "            inputs, targets = torch.autograd.Variable(inputs, volatile=True), torch.autograd.Variable(targets)\n",
    "\n",
    "            # compute output\n",
    "        #         outputs,_,_,_,_,_,_,_,_ = model(inputs)\n",
    "            outputs,_,_,_,_,_,_,_ = model(inputs)\n",
    "\n",
    "            outputs_value = outputs.data.cpu().numpy()\n",
    "            correct = torch.argmax(outputs, axis=-1) == targets\n",
    "            acc.extend(correct)\n",
    "            \n",
    "            input_shape = inputs.shape\n",
    "            x_adv_list = []\n",
    "            for i in range(len(inputs)):\n",
    "                if correct[i]:\n",
    "#                     stime = time.time()\n",
    "                    x_adv_curr = hop_skip_jump_attack(model, inputs[i:i+1],\n",
    "                                                      verbose=False,clip_min=inputs[i:i+1].min(),clip_max = inputs[i:i+1].max())\n",
    "#                     print('generate one data takes: ', time.time()-stime)\n",
    "                else:\n",
    "                    x_adv_curr = inputs[i:i+1]\n",
    "                x_adv_list.append(x_adv_curr)\n",
    "            x_adv_list = torch.cat(x_adv_list, axis=0)\n",
    "            y_pred_adv,_,_,_,_,_,_,_ = model(x_adv_list)\n",
    "            corr_adv = torch.argmax(y_pred_adv, axis=-1) == targets\n",
    "            acc_adv.extend(corr_adv)\n",
    "            n_img = inputs.permute(0,2,3,1).data.cpu().numpy()\n",
    "            img = (n_img*std)+mean\n",
    "            n_x_adv_list = x_adv_list.permute(0,2,3,1).data.cpu().numpy()\n",
    "            x = (n_x_adv_list*std)+mean\n",
    "\n",
    "            d = np.sqrt(np.sum(np.square(x-img), axis=(1,2,3)))\n",
    "#             d = torch.sqrt(torch.sum(torch.square(x_adv_list-inputs), axis=(1,2,3)))\n",
    "#             dist_adv.extend(d.data.cpu().numpy())\n",
    "            dist_adv.extend(d)\n",
    "            num_samples += len(outputs)\n",
    "            print(\"processed {} examples\".format(num_samples))\n",
    "        \n",
    "    else:\n",
    "        raise ValueError(\"Unknown attack {}\".format(attack))\n",
    "\n",
    "#   next_element = ds.make_one_shot_iterator().get_next()\n",
    "\n",
    "\n",
    "    return dist_adv[:max_samples]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "\n",
    "\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 93,
   "metadata": {
    "lines_to_next_cell": 2
   },
   "outputs": [],
   "source": [
    "\n",
    "\n",
    "\n",
    "private_trainset_intrain = []\n",
    "private_trainset_intest = []\n",
    "\n",
    "private_testset_intrain =[] \n",
    "private_testset_intest =[] \n",
    "\n",
    "\n",
    "r = np.arange(50000)\n",
    "batch_privacy = 50\n",
    "np.random.seed(1111)\n",
    "np.random.shuffle(r)\n",
    "for i in range(1000):\n",
    "    private_trainset_intrain.append(trainset[r[i]])\n",
    "    \n",
    "for i in range(25000,26000): \n",
    "    private_trainset_intest.append(trainset[r[i]])\n",
    "    \n",
    "r = np.arange(10000)\n",
    "# np.random.seed(6)\n",
    "np.random.shuffle(r)\n",
    "\n",
    "for i in range(1000):\n",
    "    private_testset_intrain.append(testset[r[i]])\n",
    "\n",
    "for i in range(5000,6000):\n",
    "    private_testset_intest.append(testset[r[i]])\n",
    "\n",
    "private_trainloader_intrain = data.DataLoader(private_trainset_intrain, batch_size=batch_privacy, shuffle=True, num_workers=1)\n",
    "private_trainloader_intest = data.DataLoader(private_trainset_intest, batch_size=batch_privacy, shuffle=True, num_workers=1)\n",
    "\n",
    "private_testloader_intrain = data.DataLoader(private_testset_intrain, batch_size=batch_privacy, shuffle=True, num_workers=1)\n",
    "private_testloader_intest = data.DataLoader(private_testset_intest, batch_size=batch_privacy, shuffle=True, num_workers=1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 94,
   "metadata": {
    "lines_to_next_cell": 2
   },
   "outputs": [],
   "source": [
    "\n",
    "\n",
    "# ================================attack==============================\n",
    "\n",
    "source_train_ds = private_trainloader_intrain \n",
    "target_train_ds = private_trainloader_intest \n",
    "source_test_ds = private_testloader_intrain \n",
    "target_test_ds = private_testloader_intest\n",
    "\n",
    "source_model = model\n",
    "target_model = model\n",
    "\n",
    "input_dim = [None, 32,32,3]\n",
    "max_samples = 1000\n",
    "n_classes = 100"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 95,
   "metadata": {
    "lines_to_next_cell": 2
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/tmp/ipykernel_2273635/298495222.py:17: UserWarning: volatile was removed and now has no effect. Use `with torch.no_grad():` instead.\n",
      "  inputs, targets = torch.autograd.Variable(inputs, volatile=True), torch.autograd.Variable(targets)\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(1/20) Data: 0.134s | Batch: 0.222s | Loss: 3.9149 | top1:  52.0000 | top5:  80.0000\n",
      "train_loss:  3.8795, train_acc:  56.8000\n",
      "(1/20) Data: 0.118s | Batch: 0.158s | Loss: 3.8572 | top1:  60.0000 | top5:  94.0000\n",
      "test_loss:  3.8826, test_acc:  58.5000\n",
      "(1/20) Data: 0.111s | Batch: 0.155s | Loss: 4.0102 | top1:  44.0000 | top5:  76.0000\n",
      "train_loss:  4.0260, train_acc:  43.7000\n",
      "(1/20) Data: 0.133s | Batch: 0.177s | Loss: 4.0666 | top1:  34.0000 | top5:  72.0000\n",
      "test_loss:  4.0374, test_acc:  43.1000\n"
     ]
    }
   ],
   "source": [
    "\n",
    "\n",
    "test_loss, train_acc1 = test(source_train_ds, model, criterion, epoch, use_cuda)\n",
    "print ('train_loss: {test_loss: .4f}, train_acc: {test_acc: .4f}'.format(test_loss=test_loss, test_acc=train_acc1))\n",
    "\n",
    "test_loss, train_acc2 = test(target_train_ds, model, criterion, epoch, use_cuda)\n",
    "print ('test_loss: {test_loss: .4f}, test_acc: {test_acc: .4f}'.format(test_loss=test_loss, test_acc=train_acc2))\n",
    "\n",
    "test_loss, test_acc1 = test(source_test_ds, model, criterion, epoch, use_cuda)\n",
    "print ('train_loss: {test_loss: .4f}, train_acc: {test_acc: .4f}'.format(test_loss=test_loss, test_acc=test_acc1))\n",
    "\n",
    "test_loss, test_acc2 = test(target_test_ds, model, criterion, epoch, use_cuda)\n",
    "print ('test_loss: {test_loss: .4f}, test_acc: {test_acc: .4f}'.format(test_loss=test_loss, test_acc=test_acc2))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 96,
   "metadata": {
    "lines_to_next_cell": 2
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "tensor(56.5500, device='cuda:0')\n",
      "tensor(57.7000, device='cuda:0')\n"
     ]
    }
   ],
   "source": [
    "\n",
    "\n",
    "print((train_acc1 - test_acc1)/2 + 50)\n",
    "\n",
    "print((train_acc2 - test_acc2)/2 + 50)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "lines_to_next_cell": 2
   },
   "outputs": [],
   "source": [
    "\n",
    "\n",
    "# ================================cw==============================\n",
    "\n",
    "\n",
    "# NOTICE:  max_samples is set to be 50 to do a quick attack, launch attack on 1000 samples will take a long time\n",
    "# max_samples=1000 \n",
    "max_samples = 50\n",
    "n_classes = 100\n",
    "\n",
    "source_m = np.concatenate([np.ones(max_samples),\n",
    "                         np.zeros(max_samples)], axis=0)\n",
    "target_m = np.concatenate([np.ones(max_samples),\n",
    "                         np.zeros(max_samples)], axis=0)\n",
    "# attack with C&W\n",
    "dists_source_in_cw1 = dists(source_model, source_train_ds, attack=\"CW\", max_samples=max_samples,\n",
    "                                          input_dim=[None, input_dim[0], input_dim[1], input_dim[2]],\n",
    "                                          n_classes=n_classes)\n",
    "dists_source_out_cw1 = dists(source_model, source_test_ds, attack=\"CW\", max_samples=max_samples,\n",
    "                                          input_dim=[None, input_dim[0], input_dim[1], input_dim[2]],\n",
    "                                          n_classes=n_classes)\n",
    "dists_source_cw1 = np.concatenate([dists_source_in_cw1, dists_source_out_cw1], axis=0)\n",
    "# dists_target_in = dists(target_model, target_train_ds, attack=\"CW\", max_samples=max_samples,\n",
    "#                                           input_dim=[None, input_dim[0], input_dim[1], input_dim[2]],\n",
    "#                                           n_classes=n_classes)\n",
    "# dists_target_out = dists(target_model, target_test_ds, attack=\"CW\", max_samples=max_samples,\n",
    "#                                           input_dim=[None, input_dim[0], input_dim[1], input_dim[2]],\n",
    "#                                           n_classes=n_classes)\n",
    "dists_target_in_cw1 = dists_source_in_cw1\n",
    "dists_target_out_cw1 = dists_source_out_cw1\n",
    "dists_target_cw1 = np.concatenate([dists_target_in_cw1, dists_target_out_cw1], axis=0)\n",
    "print(\"threshold on C&W:\")\n",
    "acc11, prec11, _, _ = get_threshold(source_m, dists_source_cw1, target_m, dists_target_cw1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "# processed 50 examples\n",
    "# processed 100 examples\n",
    "# processed 50 examples\n",
    "# processed 100 examples\n",
    "# threshold on C&W:\n",
    "# acc src: 0.52, acc test (best thresh): 0.52, acc test (src thresh): 0.52, thresh: 0.023501037922521815\n",
    "# prec src: 0.5555555555555556, prec test (best thresh): 0.5555555555555556, prec test (src thresh): 0.5555555555555556, thresh: 0.023501037922521815\n",
    "\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 69,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Files already downloaded and verified\n"
     ]
    }
   ],
   "source": [
    "\n",
    "# load membership inference attack \n",
    "\n",
    "\n",
    "batch_privacy=100\n",
    "trainset = dataloader(root='./data100', train=True, download=True, transform=transform_train)\n",
    "testset = dataloader(root='./data100', train=False, download=False, transform=transform_test)\n",
    "\n",
    "r = np.arange(50000)\n",
    "# np.random.shuffle(r)\n",
    "\n",
    "private_trainset_intrain = []\n",
    "private_trainset_intest = []\n",
    "\n",
    "private_testset_intrain =[] \n",
    "private_testset_intest =[] \n",
    "\n",
    "for i in range(0,25000):\n",
    "    private_trainset_intrain.append(trainset[r[i]])\n",
    "\n",
    "for i in range(25000,50000):\n",
    "    private_trainset_intest.append(trainset[r[i]])\n",
    "\n",
    "r = np.arange(10000)\n",
    "# np.random.shuffle(r)\n",
    "\n",
    "for i in range(0, 5000):\n",
    "    private_testset_intrain.append(testset[r[i]])\n",
    "\n",
    "for i in range(5000,10000):\n",
    "    private_testset_intest.append(testset[r[i]])\n",
    "\n",
    "# private_trainloader_intrain = data.DataLoader(private_trainset_intrain, batch_size=batch_privacy, shuffle=True, num_workers=1)\n",
    "# private_trainloader_intest = data.DataLoader(private_trainset_intest, batch_size=batch_privacy, shuffle=True, num_workers=1)\n",
    "\n",
    "# private_testloader_intrain = data.DataLoader(private_testset_intrain, batch_size=batch_privacy, shuffle=True, num_workers=1)\n",
    "# private_testloader_intest = data.DataLoader(private_testset_intest, batch_size=batch_privacy, shuffle=True, num_workers=1)\n",
    "    \n",
    "    \n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 70,
   "metadata": {
    "lines_to_next_cell": 2
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "features.0.weight\n",
      "features.0.weight\n",
      "features.0.bias\n",
      "features.2.weight\n",
      "features.2.weight\n",
      "features.2.bias\n",
      "features.4.weight\n",
      "features.4.weight\n",
      "features.4.bias\n",
      "labels.0.weight\n",
      "labels.0.weight\n",
      "labels.0.bias\n",
      "labels.2.weight\n",
      "labels.2.weight\n",
      "labels.2.bias\n",
      "combine.0.weight\n",
      "combine.0.weight\n",
      "combine.0.bias\n",
      "combine.2.weight\n",
      "combine.2.weight\n",
      "combine.2.bias\n",
      "combine.4.weight\n",
      "combine.4.weight\n",
      "combine.4.bias\n",
      "combine.6.weight\n",
      "combine.6.weight\n",
      "combine.6.bias\n",
      "combine.8.weight\n",
      "combine.8.weight\n",
      "combine.8.bias\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/tmp/ipykernel_2273635/2203992965.py:41: UserWarning: nn.init.normal is now deprecated in favor of nn.init.normal_.\n",
      "  nn.init.normal(self.state_dict()[key], std=0.01)\n"
     ]
    }
   ],
   "source": [
    "\n",
    "\n",
    "inferenece_model = InferenceAttack_HZ(100).cuda()\n",
    "# inferenece_model = torch.nn.DataParallel(inferenece_model).cuda()\n",
    "at_lr = 0.001\n",
    "state = {}\n",
    "state['lr'] = at_lr\n",
    "\n",
    "optimizer_mem = optim.Adam(inferenece_model.parameters(), lr=at_lr )\n",
    "criterion_attack = nn.MSELoss()\n",
    "best_acc= 0.0\n",
    "batch_size=100\n",
    "epochs= 100"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 71,
   "metadata": {
    "lines_to_next_cell": 2
   },
   "outputs": [],
   "source": [
    "\n",
    "\n",
    "train_tr_attack = data.DataLoader(private_trainset_intrain, batch_size=batch_privacy, shuffle=True, num_workers=1)\n",
    "train_te_attack = data.DataLoader(private_trainset_intest, batch_size=batch_privacy, shuffle=True, num_workers=1)\n",
    "\n",
    "test_tr_attack = data.DataLoader(private_testset_intrain, batch_size=batch_privacy, shuffle=True, num_workers=1)\n",
    "test_te_attack = data.DataLoader(private_testset_intest, batch_size=batch_privacy, shuffle=True, num_workers=1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 72,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "==> Resuming from checkpoint..\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "<All keys matched successfully>"
      ]
     },
     "execution_count": 72,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "resume_defense='./checkpoints_cifar100/NSH_attack_softmax_best'\n",
    "print('==> Resuming from checkpoint..')\n",
    "assert os.path.isfile(resume), 'Error: no checkpoint directory found!'\n",
    "# checkpoint_defense = os.path.dirname(resume_defense)\n",
    "checkpoint_defense = torch.load(resume_defense)\n",
    "inferenece_model.load_state_dict(checkpoint_defense['state_dict'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 73,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/tmp/ipykernel_2273635/3728568573.py:51: UserWarning: Implicit dimension choice for softmax has been deprecated. Change the call to include dim=X as an argument.\n",
      "  attack_model_input = softmax(pred_outputs)#torch.cat((pred_outputs,infer_input_one_hot),1)\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "privacy_test--(0/100) Data: 0.055s | Batch: 0.180s | | Loss: 0.2500 | top1:  0.4850 \n",
      "privacy_test--(10/100) Data: 0.008s | Batch: 0.108s | | Loss: 0.2500 | top1:  0.5027 \n",
      "privacy_test--(20/100) Data: 0.005s | Batch: 0.104s | | Loss: 0.2500 | top1:  0.5002 \n",
      "privacy_test--(30/100) Data: 0.005s | Batch: 0.103s | | Loss: 0.2500 | top1:  0.4987 \n",
      "privacy_test--(40/100) Data: 0.004s | Batch: 0.102s | | Loss: 0.2500 | top1:  0.4979 \n",
      "test acc 0.4975 test_loss:  tensor(0.2500, device='cuda:0')\n"
     ]
    }
   ],
   "source": [
    "\n",
    "test_attack_enum = enumerate(zip(train_te_attack,test_te_attack))\n",
    "test_loss, test_acc = privacy_test_softmax(test_attack_enum, model, inferenece_model, criterion_attack, optimizer_mem, epoch, use_cuda, 1000)\n",
    "print ('test acc', test_acc, 'test_loss: ',test_loss)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 74,
   "metadata": {
    "lines_to_next_cell": 2
   },
   "outputs": [],
   "source": [
    "\n",
    "\n",
    "def adjust_learning_rate_nsh(optimizer, epoch):\n",
    "    global state\n",
    "#     if epoch in [10, 80, 150]:\n",
    "#         state['lr'] *= 0.1\n",
    "#         for param_group in optimizer.param_groups:\n",
    "#             param_group['lr'] = state['lr']\n",
    "    if epoch in [30]:\n",
    "        state['lr'] *= 0.1 \n",
    "#         state['lr'] *= 1\n",
    "        for param_group in optimizer.param_groups:\n",
    "            param_group['lr'] = state['lr']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 75,
   "metadata": {
    "lines_to_next_cell": 2
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/tmp/ipykernel_2273635/298495222.py:17: UserWarning: volatile was removed and now has no effect. Use `with torch.no_grad():` instead.\n",
      "  inputs, targets = torch.autograd.Variable(inputs, volatile=True), torch.autograd.Variable(targets)\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(1/250) Data: 0.103s | Batch: 0.197s | Loss: 3.9994 | top1:  51.0000 | top5:  76.0000\n",
      "(21/250) Data: 0.006s | Batch: 0.056s | Loss: 3.9083 | top1:  56.8571 | top5:  82.1429\n",
      "(41/250) Data: 0.004s | Batch: 0.052s | Loss: 3.8996 | top1:  57.1220 | top5:  83.2195\n",
      "(61/250) Data: 0.003s | Batch: 0.051s | Loss: 3.9001 | top1:  56.4426 | top5:  83.0820\n",
      "(81/250) Data: 0.003s | Batch: 0.051s | Loss: 3.8987 | top1:  56.3827 | top5:  82.8519\n",
      "(101/250) Data: 0.002s | Batch: 0.050s | Loss: 3.8965 | top1:  56.8119 | top5:  83.3564\n",
      "(121/250) Data: 0.002s | Batch: 0.050s | Loss: 3.8966 | top1:  56.9174 | top5:  83.3140\n",
      "(141/250) Data: 0.002s | Batch: 0.050s | Loss: 3.8948 | top1:  57.0496 | top5:  83.4681\n",
      "(161/250) Data: 0.002s | Batch: 0.050s | Loss: 3.8950 | top1:  56.9193 | top5:  83.6460\n",
      "(181/250) Data: 0.002s | Batch: 0.050s | Loss: 3.8954 | top1:  56.9282 | top5:  83.6519\n",
      "(201/250) Data: 0.002s | Batch: 0.050s | Loss: 3.8945 | top1:  57.0299 | top5:  83.7562\n",
      "(221/250) Data: 0.002s | Batch: 0.050s | Loss: 3.8955 | top1:  56.8959 | top5:  83.6561\n",
      "(241/250) Data: 0.002s | Batch: 0.051s | Loss: 3.8950 | top1:  57.0249 | top5:  83.7552\n",
      "Classification accuracy: 56.90\n",
      "(1/250) Data: 0.101s | Batch: 0.164s | Loss: 3.8917 | top1:  60.0000 | top5:  80.0000\n",
      "(21/250) Data: 0.006s | Batch: 0.056s | Loss: 3.8859 | top1:  57.5238 | top5:  83.6190\n",
      "(41/250) Data: 0.004s | Batch: 0.053s | Loss: 3.8891 | top1:  57.4634 | top5:  83.7317\n",
      "(61/250) Data: 0.003s | Batch: 0.052s | Loss: 3.8967 | top1:  56.2295 | top5:  83.4098\n",
      "(81/250) Data: 0.003s | Batch: 0.052s | Loss: 3.8973 | top1:  56.0741 | top5:  83.2222\n",
      "(101/250) Data: 0.002s | Batch: 0.051s | Loss: 3.8960 | top1:  56.5248 | top5:  83.5247\n",
      "(121/250) Data: 0.002s | Batch: 0.051s | Loss: 3.8945 | top1:  56.5372 | top5:  83.6116\n",
      "(141/250) Data: 0.002s | Batch: 0.051s | Loss: 3.8915 | top1:  56.8085 | top5:  83.8298\n",
      "(161/250) Data: 0.002s | Batch: 0.051s | Loss: 3.8923 | top1:  56.7453 | top5:  83.8012\n",
      "(181/250) Data: 0.002s | Batch: 0.051s | Loss: 3.8939 | top1:  56.6630 | top5:  83.6961\n",
      "(201/250) Data: 0.002s | Batch: 0.050s | Loss: 3.8923 | top1:  56.8806 | top5:  83.7562\n",
      "(221/250) Data: 0.002s | Batch: 0.050s | Loss: 3.8926 | top1:  56.8778 | top5:  83.7647\n",
      "(241/250) Data: 0.002s | Batch: 0.050s | Loss: 3.8914 | top1:  56.9585 | top5:  83.8423\n",
      "Classification accuracy: 56.95\n",
      "(1/50) Data: 0.092s | Batch: 0.149s | Loss: 4.0280 | top1:  44.0000 | top5:  76.0000\n",
      "(21/50) Data: 0.007s | Batch: 0.057s | Loss: 4.0309 | top1:  43.5714 | top5:  71.0476\n",
      "(41/50) Data: 0.004s | Batch: 0.054s | Loss: 4.0327 | top1:  43.0732 | top5:  71.3902\n",
      "Classification accuracy: 43.06\n",
      "(1/50) Data: 0.096s | Batch: 0.158s | Loss: 3.9866 | top1:  47.0000 | top5:  75.0000\n",
      "(21/50) Data: 0.006s | Batch: 0.054s | Loss: 4.0424 | top1:  42.2381 | top5:  70.5714\n",
      "(41/50) Data: 0.004s | Batch: 0.051s | Loss: 4.0452 | top1:  42.6098 | top5:  70.4390\n",
      "Classification accuracy: 42.88\n"
     ]
    }
   ],
   "source": [
    "\n",
    "\n",
    "test_loss, final_test_acc = test(train_tr_attack, model, criterion, epoch, use_cuda)\n",
    "print ('Classification accuracy: %.2f'%(final_test_acc))\n",
    "\n",
    "test_loss, final_test_acc = test(train_te_attack, model, criterion, epoch, use_cuda)\n",
    "print ('Classification accuracy: %.2f'%(final_test_acc))\n",
    "\n",
    "test_loss, final_test_acc = test(test_tr_attack, model, criterion, epoch, use_cuda)\n",
    "print ('Classification accuracy: %.2f'%(final_test_acc))\n",
    "\n",
    "test_loss, final_test_acc = test(test_te_attack, model, criterion, epoch, use_cuda)\n",
    "print ('Classification accuracy: %.2f'%(final_test_acc))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "\n",
    "\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "lines_to_next_cell": 2
   },
   "outputs": [],
   "source": [
    "# code for training attack model\n",
    "\n",
    "epochs= 100\n",
    "for epoch in range(0, epochs):\n",
    "    adjust_learning_rate_nsh(optimizer, epoch)\n",
    "    train_attack_enum = enumerate(zip(train_tr_attack,test_tr_attack))\n",
    "    \n",
    "    print('\\nEpoch: [%d | %d]' % (epoch + 1, epochs))\n",
    "    \n",
    "    train_loss, train_acc = privacy_train_softmax(train_attack_enum,model,inferenece_model,criterion_attack,optimizer_mem,epoch,use_cuda, 10000)\n",
    "    \n",
    "    test_attack_enum = enumerate(zip(train_te_attack,test_te_attack))\n",
    "    \n",
    "    print ('train acc', train_acc)\n",
    "    test_loss, test_acc = privacy_test_softmax(test_attack_enum, model, inferenece_model, criterion_attack, optimizer_mem, epoch, use_cuda, 1000)\n",
    "    \n",
    "    is_best = (test_acc>best_acc) #or ((1-test_acc)>best_acc)\n",
    "    best_acc = max(test_acc, best_acc)\n",
    "#     best_acc = max( best_acc, (1-test_acc))\n",
    "\n",
    "    print ('test acc', test_acc, best_acc)\n",
    "\n",
    "    # save model\n",
    "    if is_best:\n",
    "        best_epoch = epoch+1;\n",
    "        save_checkpoint({\n",
    "                'epoch': epoch + 1,\n",
    "                'state_dict': inferenece_model.state_dict(),\n",
    "                'acc': test_acc,\n",
    "                'best_acc': best_acc,\n",
    "                'optimizer' : optimizer_mem.state_dict(),\n",
    "            }, False, checkpoint=checkpoint_path,filename='NSH_attack_softmax_best')\n",
    "        \n",
    "print('model train acc: ', final_train_acc, '  model test acc: ', final_test_acc)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "\n",
    "\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 76,
   "metadata": {
    "lines_to_next_cell": 2
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "DataParallel(\n",
      "  (module): Defense_Model(\n",
      "    (features): Sequential(\n",
      "      (0): Linear(in_features=100, out_features=256, bias=True)\n",
      "      (1): ReLU()\n",
      "      (2): Linear(in_features=256, out_features=128, bias=True)\n",
      "      (3): ReLU()\n",
      "      (4): Linear(in_features=128, out_features=64, bias=True)\n",
      "      (5): ReLU()\n",
      "      (6): Linear(in_features=64, out_features=1, bias=True)\n",
      "    )\n",
      "    (output): Sigmoid()\n",
      "  )\n",
      ")\n"
     ]
    }
   ],
   "source": [
    "\n",
    "# load NN attack model\n",
    "defense_model = Defense_Model(100)\n",
    "defense_model = torch.nn.DataParallel(defense_model).cuda()\n",
    "defense_criterion = nn.MSELoss()\n",
    "# defense_criterion = nn.CrossEntropyLoss()\n",
    "att_batch_size = 100\n",
    "at_lr = 0.001\n",
    "state = {}\n",
    "state['lr'] = at_lr\n",
    "defense_optimizer = torch.optim.Adam(defense_model.parameters(), lr=at_lr)\n",
    "print(defense_model)\n",
    "best_acc = 0.0\n",
    "epochs = 100"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 77,
   "metadata": {
    "lines_to_next_cell": 2
   },
   "outputs": [],
   "source": [
    "\n",
    "\n",
    "def adjust_learning_rate_attack(optimizer, epoch):\n",
    "    global state\n",
    "    if epoch in [40, 80]:\n",
    "        #     if (epoch+1)%100 == 0:\n",
    "        state['lr'] *= 0.1\n",
    "        #         state['lr'] *= 1\n",
    "        for param_group in optimizer.param_groups:\n",
    "            param_group['lr'] = state['lr']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 78,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "==> Resuming from checkpoint..\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/tmp/ipykernel_2273635/3828901059.py:35: UserWarning: Implicit dimension choice for softmax has been deprecated. Change the call to include dim=X as an argument.\n",
      "  attack_input = softmax(sort_inputs)  # torch.cat((comb_inputs,comb_targets),1)\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(1/25) Data: 0.005s | Batch: 0.116s | | Loss: 0.2420 | top1:  0.5750 \n",
      "(11/25) Data: 0.003s | Batch: 0.106s | | Loss: 0.2465 | top1:  0.5605 \n",
      "(21/25) Data: 0.003s | Batch: 0.104s | | Loss: 0.2468 | top1:  0.5533 \n",
      "(31/25) Data: 0.003s | Batch: 0.103s | | Loss: 0.2454 | top1:  0.5637 \n",
      "(41/25) Data: 0.003s | Batch: 0.104s | | Loss: 0.2456 | top1:  0.5610 \n",
      "test acc 0.562 test_loss:  tensor(0.2455, device='cuda:0')\n"
     ]
    }
   ],
   "source": [
    "resume_defense='./checkpoints_cifar100/softmax_sort_NN_best'\n",
    "print('==> Resuming from checkpoint..')\n",
    "assert os.path.isfile(resume), 'Error: no checkpoint directory found!'\n",
    "checkpoint_defense = os.path.dirname(resume_defense)\n",
    "checkpoint_defense = torch.load(resume_defense)\n",
    "defense_model.load_state_dict(checkpoint_defense['state_dict'])\n",
    "\n",
    "\n",
    "test_attack_enum = enumerate(zip(train_te_attack,test_te_attack))\n",
    "test_loss, test_acc, sum_correct = test_attack_sort_softmax(test_attack_enum, model, defense_model,\n",
    "                                                                criterion, defense_criterion, optimizer,\n",
    "                                                                defense_optimizer,\n",
    "                                                                epoch, use_cuda)\n",
    "\n",
    "print ('test acc', test_acc, 'test_loss: ',test_loss)\n",
    "\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "lines_to_next_cell": 2
   },
   "outputs": [],
   "source": [
    "# code for training attack model\n",
    "\n",
    "for epoch in range(0, epochs):\n",
    "    adjust_learning_rate_attack(defense_optimizer, epoch)\n",
    "\n",
    "    print('\\nEpoch: [%d | %d] , lr : %f' % (epoch + 1, epochs, state['lr']))\n",
    "    train_attack_enum = enumerate(zip(train_tr_attack, test_tr_attack))\n",
    "    train_loss, train_acc = train_attack_sort_softmax(train_attack_enum, model,\n",
    "                                                      defense_model, criterion, defense_criterion, optimizer,\n",
    "                                                      defense_optimizer, epoch, use_cuda)\n",
    "\n",
    "    print('train acc:', train_acc)\n",
    "    test_attack_enum = enumerate(zip(train_te_attack, test_te_attack))\n",
    "    test_loss, test_acc, sum_correct = test_attack_sort_softmax(test_attack_enum, model, defense_model,\n",
    "                                                                criterion, defense_criterion, optimizer,\n",
    "                                                                defense_optimizer,\n",
    "                                                                epoch, use_cuda)\n",
    "\n",
    "    is_best = test_acc > best_acc\n",
    "# save model\n",
    "    best_acc = max(test_acc, best_acc)\n",
    "    if is_best or epoch + 1 == epochs:\n",
    "        save_checkpoint({\n",
    "            'epoch': epoch + 1,\n",
    "            'state_dict': defense_model.state_dict(),\n",
    "            'acc': test_acc,\n",
    "            'best_acc': best_acc,\n",
    "            'optimizer': defense_optimizer.state_dict(),\n",
    "        }, False, checkpoint=checkpoint_path, filename='softmax_sort_NN_best')\n",
    "\n",
    "    print('test acc', test_acc, best_acc)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "\n",
    "\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 79,
   "metadata": {
    "lines_to_next_cell": 2
   },
   "outputs": [],
   "source": [
    "# load for metric base attack\n",
    "\n",
    "class black_box_benchmarks(object):\n",
    "    \n",
    "    def __init__(self, shadow_train_performance, shadow_test_performance, \n",
    "                 target_train_performance, target_test_performance, num_classes):\n",
    "        '''\n",
    "        each input contains both model predictions (shape: num_data*num_classes) and ground-truth labels. \n",
    "        '''\n",
    "        self.num_classes = num_classes\n",
    "        \n",
    "        self.s_tr_outputs, self.s_tr_labels = shadow_train_performance\n",
    "        self.s_te_outputs, self.s_te_labels = shadow_test_performance\n",
    "        self.t_tr_outputs, self.t_tr_labels = target_train_performance\n",
    "        self.t_te_outputs, self.t_te_labels = target_test_performance\n",
    "        \n",
    "        self.s_tr_corr = (np.argmax(self.s_tr_outputs, axis=1)==self.s_tr_labels).astype(int)\n",
    "        self.s_te_corr = (np.argmax(self.s_te_outputs, axis=1)==self.s_te_labels).astype(int)\n",
    "        self.t_tr_corr = (np.argmax(self.t_tr_outputs, axis=1)==self.t_tr_labels).astype(int)\n",
    "        self.t_te_corr = (np.argmax(self.t_te_outputs, axis=1)==self.t_te_labels).astype(int)\n",
    "        \n",
    "        self.s_tr_conf = np.array([self.s_tr_outputs[i, self.s_tr_labels[i]] for i in range(len(self.s_tr_labels))])\n",
    "        self.s_te_conf = np.array([self.s_te_outputs[i, self.s_te_labels[i]] for i in range(len(self.s_te_labels))])\n",
    "        self.t_tr_conf = np.array([self.t_tr_outputs[i, self.t_tr_labels[i]] for i in range(len(self.t_tr_labels))])\n",
    "        self.t_te_conf = np.array([self.t_te_outputs[i, self.t_te_labels[i]] for i in range(len(self.t_te_labels))])\n",
    "        \n",
    "        self.s_tr_entr = self._entr_comp(self.s_tr_outputs)\n",
    "        self.s_te_entr = self._entr_comp(self.s_te_outputs)\n",
    "        self.t_tr_entr = self._entr_comp(self.t_tr_outputs)\n",
    "        self.t_te_entr = self._entr_comp(self.t_te_outputs)\n",
    "        \n",
    "        self.s_tr_m_entr = self._m_entr_comp(self.s_tr_outputs, self.s_tr_labels)\n",
    "        self.s_te_m_entr = self._m_entr_comp(self.s_te_outputs, self.s_te_labels)\n",
    "        self.t_tr_m_entr = self._m_entr_comp(self.t_tr_outputs, self.t_tr_labels)\n",
    "        self.t_te_m_entr = self._m_entr_comp(self.t_te_outputs, self.t_te_labels)\n",
    "        \n",
    "    \n",
    "    def _log_value(self, probs, small_value=1e-30):\n",
    "        return -np.log(np.maximum(probs, small_value))\n",
    "    \n",
    "    def _entr_comp(self, probs):\n",
    "        return np.sum(np.multiply(probs, self._log_value(probs)),axis=1)\n",
    "    \n",
    "    def _m_entr_comp(self, probs, true_labels):\n",
    "        log_probs = self._log_value(probs)\n",
    "        reverse_probs = 1-probs\n",
    "        log_reverse_probs = self._log_value(reverse_probs)\n",
    "        modified_probs = np.copy(probs)\n",
    "        modified_probs[range(true_labels.size), true_labels] = reverse_probs[range(true_labels.size), true_labels]\n",
    "        modified_log_probs = np.copy(log_reverse_probs)\n",
    "        modified_log_probs[range(true_labels.size), true_labels] = log_probs[range(true_labels.size), true_labels]\n",
    "        return np.sum(np.multiply(modified_probs, modified_log_probs),axis=1)\n",
    "    \n",
    "    def _thre_setting(self, tr_values, te_values):\n",
    "        value_list = np.concatenate((tr_values, te_values))\n",
    "        thre, max_acc = 0, 0\n",
    "        for value in value_list:\n",
    "            tr_ratio = np.sum(tr_values>=value)/(len(tr_values)+0.0)\n",
    "            te_ratio = np.sum(te_values<value)/(len(te_values)+0.0)\n",
    "            acc = 0.5*(tr_ratio + te_ratio)\n",
    "            if acc > max_acc:\n",
    "                thre, max_acc = value, acc\n",
    "        return thre\n",
    "    \n",
    "    def _mem_inf_via_corr(self):\n",
    "        # perform membership inference attack based on whether the input is correctly classified or not\n",
    "        t_tr_acc = np.sum(self.t_tr_corr)/(len(self.t_tr_corr)+0.0)\n",
    "        t_te_acc = np.sum(self.t_te_corr)/(len(self.t_te_corr)+0.0)\n",
    "        mem_inf_acc = 0.5*(t_tr_acc + 1 - t_te_acc)\n",
    "        print('For membership inference attack via correctness, the attack acc is {acc1:.3f}, with train acc {acc2:.3f} and test acc {acc3:.3f}'.format(acc1=mem_inf_acc, acc2=t_tr_acc, acc3=t_te_acc) )\n",
    "        return\n",
    "    \n",
    "    def _mem_inf_thre(self, v_name, s_tr_values, s_te_values, t_tr_values, t_te_values):\n",
    "        # perform membership inference attack by thresholding feature values: the feature can be prediction confidence,\n",
    "        # (negative) prediction entropy, and (negative) modified entropy\n",
    "        t_tr_mem, t_te_non_mem = 0, 0\n",
    "        for num in range(self.num_classes):\n",
    "            thre = self._thre_setting(s_tr_values[self.s_tr_labels==num], s_te_values[self.s_te_labels==num])\n",
    "            t_tr_mem += np.sum(t_tr_values[self.t_tr_labels==num]>=thre)\n",
    "            t_te_non_mem += np.sum(t_te_values[self.t_te_labels==num]<thre)\n",
    "        mem_inf_acc = 0.5*(t_tr_mem/(len(self.t_tr_labels)+0.0) + t_te_non_mem/(len(self.t_te_labels)+0.0))\n",
    "        print('For membership inference attack via {n}, the attack acc is {acc:.3f}'.format(n=v_name,acc=mem_inf_acc))\n",
    "        return\n",
    "    \n",
    "\n",
    "    \n",
    "    def _mem_inf_benchmarks(self, all_methods=True, benchmark_methods=[]):\n",
    "        if (all_methods) or ('correctness' in benchmark_methods):\n",
    "            self._mem_inf_via_corr()\n",
    "        if (all_methods) or ('confidence' in benchmark_methods):\n",
    "            self._mem_inf_thre('confidence', self.s_tr_conf, self.s_te_conf, self.t_tr_conf, self.t_te_conf)\n",
    "        if (all_methods) or ('entropy' in benchmark_methods):\n",
    "            self._mem_inf_thre('entropy', -self.s_tr_entr, -self.s_te_entr, -self.t_tr_entr, -self.t_te_entr)\n",
    "        if (all_methods) or ('modified entropy' in benchmark_methods):\n",
    "            self._mem_inf_thre('modified entropy', -self.s_tr_m_entr, -self.s_te_m_entr, -self.t_tr_m_entr, -self.t_te_m_entr)\n",
    "\n",
    "        return self"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "\n",
    "\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 80,
   "metadata": {
    "lines_to_next_cell": 2
   },
   "outputs": [],
   "source": [
    "\n",
    "\n",
    "# load me evaluation\n",
    "\n",
    "# np.random.seed(1123)\n",
    "r = np.arange(25000)\n",
    "np.random.shuffle(r)\n",
    "\n",
    "private_trainset_intrain = []\n",
    "private_trainset_intest = []\n",
    "\n",
    "private_testset_intrain =[] \n",
    "private_testset_intest =[] \n",
    "\n",
    "for i in range(0,5000):\n",
    "    private_trainset_intrain.append(trainset[r[i]])\n",
    "\n",
    "for i in range(5000,10000):\n",
    "    private_trainset_intest.append(trainset[r[i]+25000])\n",
    "\n",
    "r = np.arange(10000)\n",
    "np.random.shuffle(r)\n",
    "\n",
    "for i in range(0, 5000):\n",
    "    private_testset_intrain.append(testset[r[i]])\n",
    "\n",
    "for i in range(5000,10000):\n",
    "    private_testset_intest.append(testset[r[i]])\n",
    "\n",
    "private_trainloader_intrain = data.DataLoader(private_trainset_intrain, batch_size=batch_privacy, shuffle=True, num_workers=1)\n",
    "private_trainloader_intest = data.DataLoader(private_trainset_intest, batch_size=batch_privacy, shuffle=True, num_workers=1)\n",
    "\n",
    "private_testloader_intrain = data.DataLoader(private_testset_intrain, batch_size=batch_privacy, shuffle=True, num_workers=1)\n",
    "private_testloader_intest = data.DataLoader(private_testset_intest, batch_size=batch_privacy, shuffle=True, num_workers=1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 81,
   "metadata": {
    "lines_to_next_cell": 2
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/tmp/ipykernel_2273635/298495222.py:17: UserWarning: volatile was removed and now has no effect. Use `with torch.no_grad():` instead.\n",
      "  inputs, targets = torch.autograd.Variable(inputs, volatile=True), torch.autograd.Variable(targets)\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(1/50) Data: 0.114s | Batch: 0.181s | Loss: 3.8608 | top1:  56.0000 | top5:  91.0000\n",
      "(21/50) Data: 0.007s | Batch: 0.057s | Loss: 3.8967 | top1:  56.8095 | top5:  82.9524\n",
      "(41/50) Data: 0.005s | Batch: 0.055s | Loss: 3.8881 | top1:  57.3415 | top5:  83.3902\n",
      "Classification accuracy: 57.36\n",
      "(1/50) Data: 0.122s | Batch: 0.184s | Loss: 3.9494 | top1:  48.0000 | top5:  87.0000\n",
      "(21/50) Data: 0.008s | Batch: 0.059s | Loss: 3.8909 | top1:  57.3333 | top5:  84.8095\n",
      "(41/50) Data: 0.005s | Batch: 0.054s | Loss: 3.8907 | top1:  57.2439 | top5:  84.0000\n",
      "Classification accuracy: 57.02\n",
      "(1/50) Data: 0.120s | Batch: 0.180s | Loss: 4.0499 | top1:  37.0000 | top5:  69.0000\n",
      "(21/50) Data: 0.008s | Batch: 0.058s | Loss: 4.0369 | top1:  42.2381 | top5:  71.0476\n",
      "(41/50) Data: 0.005s | Batch: 0.055s | Loss: 4.0452 | top1:  41.4878 | top5:  70.2439\n",
      "Classification accuracy: 41.84\n",
      "(1/50) Data: 0.129s | Batch: 0.187s | Loss: 3.9983 | top1:  49.0000 | top5:  76.0000\n",
      "(21/50) Data: 0.008s | Batch: 0.057s | Loss: 4.0437 | top1:  43.3333 | top5:  71.2857\n",
      "(41/50) Data: 0.005s | Batch: 0.055s | Loss: 4.0366 | top1:  43.6829 | top5:  71.2683\n",
      "Classification accuracy: 43.62\n"
     ]
    }
   ],
   "source": [
    "\n",
    "\n",
    "test_loss, final_test_acc = test(private_trainloader_intrain, model, criterion, epoch, use_cuda)\n",
    "print ('Classification accuracy: %.2f'%(final_test_acc))\n",
    "\n",
    "test_loss, final_test_acc = test(private_trainloader_intest, model, criterion, epoch, use_cuda)\n",
    "print ('Classification accuracy: %.2f'%(final_test_acc))\n",
    "\n",
    "test_loss, final_test_acc = test(private_testloader_intrain, model, criterion, epoch, use_cuda)\n",
    "print ('Classification accuracy: %.2f'%(final_test_acc))\n",
    "\n",
    "test_loss, final_test_acc = test(private_testloader_intest, model, criterion, epoch, use_cuda)\n",
    "print ('Classification accuracy: %.2f'%(final_test_acc))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 82,
   "metadata": {
    "lines_to_next_cell": 2
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/tmp/ipykernel_2273635/791256014.py:19: UserWarning: volatile was removed and now has no effect. Use `with torch.no_grad():` instead.\n",
      "  inputs, targets = torch.autograd.Variable(inputs, volatile=True), torch.autograd.Variable(targets)\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(1/50) Data: 0.109s | Batch: 0.192s | Loss: 4.0068 | top1:  45.0000 | top5:  78.0000\n",
      "(21/50) Data: 0.007s | Batch: 0.059s | Loss: 3.8815 | top1:  57.3810 | top5:  84.6190\n",
      "(41/50) Data: 0.004s | Batch: 0.055s | Loss: 3.8901 | top1:  57.0244 | top5:  83.7561\n",
      "Classification accuracy: 57.20\n",
      "(1/50) Data: 0.131s | Batch: 0.197s | Loss: 3.9216 | top1:  51.0000 | top5:  80.0000\n",
      "(21/50) Data: 0.008s | Batch: 0.058s | Loss: 3.8946 | top1:  57.4286 | top5:  82.7143\n",
      "(41/50) Data: 0.005s | Batch: 0.056s | Loss: 3.8905 | top1:  57.2927 | top5:  83.9268\n",
      "Classification accuracy: 57.36\n",
      "(1/50) Data: 0.143s | Batch: 0.216s | Loss: 4.0859 | top1:  37.0000 | top5:  67.0000\n",
      "(21/50) Data: 0.008s | Batch: 0.060s | Loss: 4.0399 | top1:  42.7143 | top5:  71.2381\n",
      "(41/50) Data: 0.005s | Batch: 0.057s | Loss: 4.0437 | top1:  41.4878 | top5:  70.4390\n",
      "Classification accuracy: 41.74\n",
      "(1/50) Data: 0.117s | Batch: 0.192s | Loss: 4.0377 | top1:  43.0000 | top5:  74.0000\n",
      "(21/50) Data: 0.007s | Batch: 0.060s | Loss: 4.0385 | top1:  42.3333 | top5:  71.0000\n",
      "(41/50) Data: 0.005s | Batch: 0.056s | Loss: 4.0354 | top1:  43.3659 | top5:  71.4146\n",
      "Classification accuracy: 43.64\n",
      "[ 97.  99. 102. 100. 101. 101.  95. 100.  86. 109. 106. 100. 110.  99.\n",
      "  94.  99. 120.  93. 107.  90. 103.  89. 105.  92.  97.  93.  94.  81.\n",
      "  98. 108.  93. 111.  96.  96. 106.  96. 115. 114. 103. 116.  98.  85.\n",
      "  91. 103.  88.  97.  94. 122. 104. 112. 106. 106. 111. 100. 116. 105.\n",
      "  84.  95.  97.  86. 101.  98. 104.  96.  99. 101.  85. 106. 102. 104.\n",
      "  86. 118. 101. 101.  97. 103. 101. 100.  87.  88.  99. 102.  99.  93.\n",
      " 102.  99. 105.  96.  91. 109.  99.  97. 101. 111. 109. 114.  89.  88.\n",
      " 103. 102.]\n",
      "[36. 37.  7. 13.  6. 35. 26. 28. 25. 47.  8. 16. 32. 29. 32. 29. 45. 36.\n",
      " 26. 27. 56. 47. 14. 31. 47. 17. 24. 15. 47. 26. 28. 39. 10. 28. 29. 11.\n",
      " 38. 33.  4. 41. 31. 37. 14. 33.  4. 16.  7. 34. 45. 52.  0. 30. 49. 43.\n",
      " 36.  4. 38. 38. 46. 12. 36. 33. 42. 23. 11.  0. 35. 23. 47. 52. 32. 49.\n",
      "  8. 35. 15. 47. 49. 23. 10. 14.  2. 24. 49. 38. 21. 42. 43. 33. 30. 33.\n",
      " 21. 42. 10. 10. 55. 32. 13. 31. 23. 30.]\n"
     ]
    }
   ],
   "source": [
    "\n",
    "\n",
    "test_loss, final_test_acc, s_tr_class, s_tr_correct = test_by_class(private_trainloader_intrain, model, criterion, epoch, use_cuda)\n",
    "print ('Classification accuracy: %.2f'%(final_test_acc))\n",
    "\n",
    "test_loss, final_test_acc, t_tr_class, t_tr_correct = test_by_class(private_trainloader_intest, model, criterion, epoch, use_cuda)\n",
    "print ('Classification accuracy: %.2f'%(final_test_acc))\n",
    "\n",
    "test_loss, final_test_acc, s_te_class, s_te_correct = test_by_class(private_testloader_intrain, model, criterion, epoch, use_cuda)\n",
    "print ('Classification accuracy: %.2f'%(final_test_acc))\n",
    "\n",
    "test_loss, final_test_acc, t_te_class, t_te_correct = test_by_class(private_testloader_intest, model, criterion, epoch, use_cuda)\n",
    "print ('Classification accuracy: %.2f'%(final_test_acc))\n",
    "\n",
    "print( s_tr_class + t_tr_class)\n",
    "print(s_tr_correct)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 83,
   "metadata": {
    "lines_to_next_cell": 2
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[0.79381443 0.81818182 0.15686275 0.18       0.16831683 0.65346535\n",
      " 0.77894737 0.63       0.60465116 0.85321101 0.28301887 0.21\n",
      " 0.63636364 0.63636364 0.72340426 0.55555556 0.61666667 0.77419355\n",
      " 0.55140187 0.6        0.96116505 0.92134831 0.40952381 0.65217391\n",
      " 0.95876289 0.27956989 0.61702128 0.37037037 0.76530612 0.50925926\n",
      " 0.53763441 0.68468468 0.25       0.47916667 0.48113208 0.20833333\n",
      " 0.70434783 0.53508772 0.10679612 0.81896552 0.56122449 0.76470588\n",
      " 0.41758242 0.7184466  0.17045455 0.3814433  0.15957447 0.53278689\n",
      " 0.98076923 0.83035714 0.03773585 0.5754717  0.96396396 0.85\n",
      " 0.68965517 0.0952381  0.8452381  0.72631579 0.91752577 0.3255814\n",
      " 0.69306931 0.70408163 0.79807692 0.44791667 0.17171717 0.01980198\n",
      " 0.6        0.47169811 0.91176471 0.90384615 0.6744186  0.8220339\n",
      " 0.13861386 0.65346535 0.37113402 0.95145631 0.87128713 0.46\n",
      " 0.2183908  0.45454545 0.03030303 0.49019608 0.92929293 0.68817204\n",
      " 0.42156863 0.82828283 0.8        0.72916667 0.71428571 0.64220183\n",
      " 0.49494949 0.75257732 0.17821782 0.20720721 0.9266055  0.77192982\n",
      " 0.30337079 0.61363636 0.48543689 0.64705882]\n",
      "[0.79 0.67 0.2  0.09 0.11 0.4  0.53 0.48 0.49 0.65 0.23 0.13 0.4  0.38\n",
      " 0.45 0.29 0.36 0.64 0.36 0.37 0.76 0.79 0.35 0.49 0.71 0.2  0.42 0.21\n",
      " 0.57 0.39 0.44 0.35 0.11 0.26 0.21 0.29 0.41 0.18 0.09 0.55 0.43 0.69\n",
      " 0.3  0.56 0.19 0.09 0.16 0.44 0.91 0.69 0.02 0.4  0.87 0.73 0.57 0.04\n",
      " 0.69 0.48 0.63 0.15 0.65 0.64 0.59 0.35 0.04 0.01 0.47 0.41 0.67 0.71\n",
      " 0.56 0.76 0.04 0.5  0.27 0.82 0.8  0.25 0.17 0.41 0.01 0.5  0.83 0.46\n",
      " 0.28 0.57 0.57 0.55 0.52 0.55 0.37 0.6  0.18 0.08 0.7  0.52 0.22 0.36\n",
      " 0.26 0.6 ]\n"
     ]
    }
   ],
   "source": [
    "\n",
    "\n",
    "train_acc = (s_tr_correct + t_tr_correct)/(s_tr_class + t_tr_class)\n",
    "print(train_acc)\n",
    "test_acc = (s_te_correct + t_te_correct)/(s_te_class+t_te_class)\n",
    "print(test_acc)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 84,
   "metadata": {
    "lines_to_next_cell": 2
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Dataset CIFAR100\n",
       "    Number of datapoints: 50000\n",
       "    Root location: ./data100\n",
       "    Split: Train\n",
       "    StandardTransform\n",
       "Transform: Compose(\n",
       "               ToTensor()\n",
       "               Normalize(mean=(0.4914, 0.4822, 0.4465), std=(0.2023, 0.1994, 0.201))\n",
       "           )"
      ]
     },
     "execution_count": 84,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "\n",
    "\n",
    "trainset"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 85,
   "metadata": {
    "lines_to_next_cell": 2
   },
   "outputs": [],
   "source": [
    "\n",
    "\n",
    "def softmax_by_row(logits, T = 1.0):\n",
    "    mx = np.max(logits, axis=-1, keepdims=True)\n",
    "    exp = np.exp((logits - mx)/T)\n",
    "    denominator = np.sum(exp, axis=-1, keepdims=True)\n",
    "    return exp/denominator\n",
    "\n",
    "\n",
    "\n",
    "def _model_predictions(model, dataloader):\n",
    "    return_outputs, return_labels = [], []\n",
    "\n",
    "    for (inputs, labels) in dataloader:\n",
    "        return_labels.append(labels.numpy())\n",
    "        outputs, h1, h2, h3, h4, h5, h6, h7 = model.forward(inputs.cuda()) \n",
    "        return_outputs.append( softmax_by_row(outputs.data.cpu().numpy()) )\n",
    "    return_outputs = np.concatenate(return_outputs)\n",
    "    return_labels = np.concatenate(return_labels)\n",
    "    return (return_outputs, return_labels)\n",
    "\n",
    "shadow_train_performance = _model_predictions(model, private_trainloader_intrain)\n",
    "shadow_test_performance = _model_predictions(model, private_testloader_intrain)\n",
    "\n",
    "target_train_performance = _model_predictions(model, private_trainloader_intest)\n",
    "target_test_performance = _model_predictions(model, private_testloader_intest)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 86,
   "metadata": {
    "lines_to_next_cell": 2
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(5000, 100)"
      ]
     },
     "execution_count": 86,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "\n",
    "\n",
    "target_test_performance[0].shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 87,
   "metadata": {
    "lines_to_next_cell": 2
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Perform membership inference attacks!!!\n",
      "For membership inference attack via correctness, the attack acc is 0.568, with train acc 0.571 and test acc 0.435\n",
      "For membership inference attack via confidence, the attack acc is 0.585\n",
      "For membership inference attack via entropy, the attack acc is 0.548\n",
      "For membership inference attack via modified entropy, the attack acc is 0.585\n"
     ]
    }
   ],
   "source": [
    "\n",
    "\n",
    "print('Perform membership inference attacks!!!')\n",
    "MIA = black_box_benchmarks(shadow_train_performance,shadow_test_performance,\n",
    "                     target_train_performance,target_test_performance,num_classes=100)\n",
    "res = MIA._mem_inf_benchmarks()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "\n",
    "\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 88,
   "metadata": {
    "lines_to_next_cell": 2
   },
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 1500x1500 with 100 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "\n",
    "risk_score = calculate_risk_score(MIA.s_tr_m_entr, MIA.s_te_m_entr, MIA.s_tr_labels, MIA.s_te_labels, MIA.t_tr_m_entr, MIA.t_tr_labels)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "\n",
    "\n",
    "\n"
   ]
  }
 ],
 "metadata": {
  "jupytext": {
   "cell_metadata_filter": "-all",
   "encoding": "# coding: utf-8",
   "executable": "/usr/bin/env python",
   "main_language": "python",
   "notebook_metadata_filter": "-all"
  },
  "kernelspec": {
   "display_name": "Python 3",
   "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": "3.7.10"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 4
}