DkNN_MNIST_demo.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "%matplotlib inline\n",
    "\n",
    "%load_ext autoreload\n",
    "%autoreload 2 "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Loading faiss with AVX2 support.\n"
     ]
    }
   ],
   "source": [
    "import logging\n",
    "import os\n",
    "import time\n",
    "\n",
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "import foolbox\n",
    "import torch\n",
    "import torch.backends.cudnn as cudnn\n",
    "import torch.nn as nn\n",
    "import torch.optim as optim\n",
    "\n",
    "from lib.dataset_utils import *\n",
    "from lib.mnist_model import *\n",
    "from lib.adv_model import *\n",
    "from lib.dknn_attack import DKNNAttack\n",
    "from lib.dknn_attack_v2 import DKNNAttackV2\n",
    "from lib.cwl2_attack import CWL2Attack\n",
    "from lib.dknn import DKNNL2\n",
    "from lib.utils import *\n",
    "from lib.lip_model import *"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "import os\n",
    "os.environ[\"CUDA_DEVICE_ORDER\"] = \"PCI_BUS_ID\"\n",
    "os.environ[\"CUDA_VISIBLE_DEVICES\"] = \"0\""
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Set all random seeds\n",
    "seed = 2019\n",
    "np.random.seed(seed)\n",
    "torch.manual_seed(seed)\n",
    "\n",
    "device = 'cuda' if torch.cuda.is_available() else 'cpu'"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [],
   "source": [
    "(x_train, y_train), (x_valid, y_valid), (x_test, y_test) = load_mnist_all(\n",
    "    '/data', val_size=0.1, seed=seed)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [],
   "source": [
    "model_name = 'mnist_basic.h5'\n",
    "net = BasicModel()\n",
    "\n",
    "# model_name = 'train_mnist_nca_exp2.h5'\n",
    "# model_name = 'adv_mnist_nca_exp42_epoch119.h5'\n",
    "# model_name = 'adv_mnist_nca_exp19.h5'\n",
    "# net = NCAModel(output_dim=100, init_it=1, train_data=(x_train, y_train))\n",
    "# net = NCAModelV3(output_dim=100, init_it=1, train_data=(x_train, y_train))\n",
    "\n",
    "# model_name = 'train_mnist_snnl_exp%d.h5' % exp_id\n",
    "# net = SNNLModel(train_it=True)\n",
    "\n",
    "# model_name = 'train_mnist_hidden_mixup_exp%d.h5' % exp_id\n",
    "# net = HiddenMixupModel()\n",
    "\n",
    "# model_name = 'train_mnist_vae_exp%d.h5' % exp_id\n",
    "# # net = VAE((1, 28, 28), num_classes=10, latent_dim=20)\n",
    "# net = VAE2((1, 28, 28), num_classes=10, latent_dim=128)\n",
    "\n",
    "# model_name = 'train_mnist_cav_exp%d.h5' % exp_id\n",
    "# net = ClassAuxVAE((1, 28, 28), num_classes=10, latent_dim=20)\n",
    "\n",
    "# model_name = 'lip_mnist_exp%d.h5' % exp_id\n",
    "# net = LipschitzModel()\n",
    "\n",
    "# model_name = 'dist_mnist_exp%d.h5' % exp_id\n",
    "# init_it = 1\n",
    "# train_it = False\n",
    "# net = NeighborModel(num_classes=10, init_it=init_it, train_it=train_it)\n",
    "\n",
    "# model_name = 'mnist_at.h5'\n",
    "# model_name = 'adv_mnist_exp6.h5'\n",
    "# basic_net = BasicModel()\n",
    "# # basic_net = BasicModelV2()\n",
    "# config = {'epsilon': 0.3,\n",
    "#           'num_steps': 40,\n",
    "#           'step_size': 0.01,\n",
    "#           'random_start': True,\n",
    "#           'loss_func': 'xent'}\n",
    "# net = PGDL2Model(basic_net, config)\n",
    "\n",
    "# model_name = 'lipae_mnist_exp%d.h5' % exp_id\n",
    "# init_it = 1\n",
    "# train_it = False\n",
    "# latent_dim = 128\n",
    "# alpha = 1e2\n",
    "# net = NCA_AE(latent_dim=latent_dim, init_it=init_it,\n",
    "#              train_it=train_it, alpha=alpha)\n",
    "\n",
    "# orig_model = 'adv_mnist_exp2.h5'\n",
    "# model_name = 'tune%d_%s' % (exp_id, orig_model)\n",
    "# net = BasicModel()\n",
    "# from tune_mnist import Identity\n",
    "# net.fc = Identity()\n",
    "\n",
    "# model_name = 'rot_mnist_exp%d.h5' % exp_id\n",
    "# net = BasicModel(num_classes=4)\n",
    "\n",
    "# model_name = 'adv_rot_mnist_exp%d.h5' % exp_id\n",
    "# basic_net = BasicModel(num_classes=4)\n",
    "# config = {'num_steps': 20,\n",
    "#           'step_size': 0.05,\n",
    "#           'random_start': True,\n",
    "#           'loss_func': 'xent'}\n",
    "# net = PGDL2Model(basic_net, config)\n",
    "\n",
    "# model_name = 'ae_mnist_exp%d.h5' % exp_id\n",
    "# net = Autoencoder((1, 28, 28), 128)\n",
    "\n",
    "# model_name = 'adv_mnist_ae_exp%d.h5' % exp_id\n",
    "# basic_net = Autoencoder((1, 28, 28), latent_dim=128)\n",
    "# config = {'num_steps': 40,\n",
    "#               'step_size': 0.1,\n",
    "#               'random_start': True,\n",
    "#               'loss_func': 'xent'}\n",
    "# net = PGDL2Model(basic_net, config)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "BasicModel(\n",
       "  (conv1): Conv2d(1, 64, kernel_size=(8, 8), stride=(2, 2), padding=(3, 3))\n",
       "  (relu1): ReLU(inplace=True)\n",
       "  (conv2): Conv2d(64, 128, kernel_size=(6, 6), stride=(2, 2), padding=(3, 3))\n",
       "  (relu2): ReLU(inplace=True)\n",
       "  (conv3): Conv2d(128, 128, kernel_size=(5, 5), stride=(1, 1))\n",
       "  (relu3): ReLU(inplace=True)\n",
       "  (fc): Linear(in_features=2048, out_features=10, bias=True)\n",
       ")"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Set up model directory\n",
    "save_dir = os.path.join(os.getcwd(), 'saved_models/mnist/')\n",
    "# save_dir = os.path.join(os.getcwd(), 'saved_models/')\n",
    "if not os.path.isdir(save_dir):\n",
    "    os.makedirs(save_dir)\n",
    "model_path = os.path.join(save_dir, model_name)\n",
    "\n",
    "net = net.to(device)\n",
    "# if device == 'cuda':\n",
    "#     net = torch.nn.DataParallel(net)\n",
    "#     cudnn.benchmark = True\n",
    "net.load_state_dict(torch.load(model_path))\n",
    "# net = net.module\n",
    "# net = net.basic_net\n",
    "net.eval()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "acc:  0.9878\n"
     ]
    }
   ],
   "source": [
    "with torch.no_grad():\n",
    "    y_pred = net(x_test.to(device))\n",
    "    ind = np.where(y_pred.argmax(1).cpu() == y_test)[0]\n",
    "    print('acc: ', len(ind) / y_test.size(0))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [],
   "source": [
    "net_knn = KNNModel()\n",
    "layers = ['identity']\n",
    "dknn = DKNNL2(net_knn, x_train, y_train, \n",
    "              x_test, y_test, layers, \n",
    "              k=100, num_classes=10)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {
    "scrolled": true
   },
   "outputs": [],
   "source": [
    "layers = ['relu1', 'relu2', 'relu3', 'fc']\n",
    "# layers = ['relu3']\n",
    "# layers = ['fc']\n",
    "# layers = ['en_conv3']\n",
    "# layers = ['en_mu']\n",
    "# layers = ['maxpool1', 'maxpool2', 'relu3', 'fc2']\n",
    "# layers = ['maxpool2']\n",
    "\n",
    "# dknn = DKNN(net, x_train, y_train, x_valid, y_valid, layers, \n",
    "#             k=75, num_classes=10)\n",
    "dknn = DKNNL2(net, x_train, y_train, x_valid, y_valid, layers, \n",
    "              cosine=True, k=75, num_classes=10)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "with torch.no_grad():\n",
    "    y_pred = dknn.classify(x_test)\n",
    "    ind = np.where(y_pred.argmax(1) == y_test.numpy())[0]\n",
    "    print((y_pred.argmax(1) == y_test.numpy()).sum() / y_test.size(0))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 96,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.9704"
      ]
     },
     "execution_count": 96,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "(np.take_along_axis(y_pred, y_test.unsqueeze(-1).numpy(), axis=1).flatten() == y_pred.max(1)).mean()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [],
   "source": [
    "cred = dknn.credibility(y_pred)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(array([ 958.,  959.,  956.,  785., 1190.,    0.,    0.,    0.,    0.,\n",
       "        5152.]),\n",
       " array([0.00366667, 0.1033    , 0.20293333, 0.30256667, 0.4022    ,\n",
       "        0.50183333, 0.60146667, 0.7011    , 0.80073333, 0.90036667,\n",
       "        1.        ]),\n",
       " <a list of 10 Patch objects>)"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.hist(cred)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [],
   "source": [
    "correct = np.argmax(y_pred, 1) == y_test.numpy()\n",
    "num_correct_by_cred = np.zeros((10, ))\n",
    "num_cred = np.zeros((10, ))\n",
    "for i in np.arange(10):\n",
    "    ind = (cred > i * 0.1) & (cred <= i* 0.1 + 0.1)\n",
    "    num_cred[i] = np.sum(ind)\n",
    "    num_correct_by_cred[i] = np.sum(correct[ind])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<BarContainer object of 10 artists>"
      ]
     },
     "execution_count": 22,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig = plt.figure()\n",
    "ax = fig.add_subplot(111)\n",
    "ax.bar(np.arange(10) * 0.1, num_cred, width=0.05)\n",
    "ax.bar(np.arange(10) * 0.1 + 0.05, num_correct_by_cred, width=0.05)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/home/user/miniconda/envs/py36/lib/python3.6/site-packages/ipykernel_launcher.py:1: RuntimeWarning: invalid value encountered in true_divide\n",
      "  \"\"\"Entry point for launching an IPython kernel.\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "array([0.52764613, 0.83124478, 0.94539249, 0.98878343, 0.99498495,\n",
       "              nan,        nan,        nan,        nan, 0.99922103])"
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "num_correct_by_cred / num_cred"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "---\n",
    "\n",
    "## DkNN Attack"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 105,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "    step: 0; loss: 0.869; l2dist: 0.000\n",
      "    step: 100; loss: 0.866; l2dist: 0.044\n",
      "    step: 200; loss: 0.867; l2dist: 0.047\n",
      "binary step: 0; num successful adv: 1/100\n",
      "binary step: 0; num successful adv so far: 1/100\n",
      "    step: 0; loss: 8.632; l2dist: 0.000\n",
      "    step: 100; loss: 8.325; l2dist: 0.472\n",
      "    step: 200; loss: 8.325; l2dist: 0.472\n",
      "binary step: 1; num successful adv: 7/100\n",
      "binary step: 1; num successful adv so far: 7/100\n",
      "    step: 0; loss: 82.638; l2dist: 0.000\n",
      "    step: 100; loss: 64.817; l2dist: 2.652\n",
      "    step: 200; loss: 59.685; l2dist: 2.772\n",
      "    step: 300; loss: 53.118; l2dist: 3.369\n",
      "    step: 400; loss: 52.504; l2dist: 3.457\n",
      "    step: 500; loss: 52.339; l2dist: 3.486\n",
      "    step: 600; loss: 52.294; l2dist: 3.498\n",
      "    step: 700; loss: 52.278; l2dist: 3.503\n",
      "    step: 800; loss: 52.273; l2dist: 3.505\n",
      "binary step: 2; num successful adv: 75/100\n",
      "binary step: 2; num successful adv so far: 77/100\n",
      "    step: 0; loss: 249.718; l2dist: 0.000\n",
      "    step: 100; loss: 207.365; l2dist: 2.538\n",
      "    step: 200; loss: 169.587; l2dist: 2.788\n",
      "    step: 300; loss: 161.377; l2dist: 3.210\n",
      "    step: 400; loss: 160.912; l2dist: 3.344\n",
      "    step: 500; loss: 160.715; l2dist: 3.392\n",
      "    step: 600; loss: 160.636; l2dist: 3.409\n",
      "    step: 700; loss: 160.613; l2dist: 3.418\n",
      "    step: 800; loss: 160.603; l2dist: 3.441\n",
      "binary step: 3; num successful adv: 72/100\n",
      "binary step: 3; num successful adv so far: 96/100\n",
      "    step: 0; loss: 491.773; l2dist: 0.000\n",
      "    step: 100; loss: 417.167; l2dist: 2.346\n",
      "    step: 200; loss: 354.969; l2dist: 2.559\n",
      "    step: 300; loss: 346.283; l2dist: 3.036\n",
      "    step: 400; loss: 345.687; l2dist: 3.169\n",
      "    step: 500; loss: 345.499; l2dist: 3.208\n",
      "    step: 600; loss: 345.415; l2dist: 3.228\n",
      "    step: 700; loss: 345.386; l2dist: 3.238\n",
      "binary step: 4; num successful adv: 66/100\n",
      "binary step: 4; num successful adv so far: 98/100\n",
      "    step: 0; loss: 1993.351; l2dist: 0.000\n",
      "    step: 100; loss: 1726.367; l2dist: 2.223\n",
      "    step: 200; loss: 1486.220; l2dist: 2.390\n",
      "    step: 300; loss: 1472.505; l2dist: 2.932\n",
      "    step: 400; loss: 1471.543; l2dist: 3.043\n",
      "    step: 500; loss: 1471.272; l2dist: 3.083\n",
      "    step: 600; loss: 1471.163; l2dist: 3.110\n",
      "binary step: 5; num successful adv: 61/100\n",
      "binary step: 5; num successful adv so far: 99/100\n",
      "    step: 0; loss: 9579.433; l2dist: 0.000\n",
      "    step: 100; loss: 8845.111; l2dist: 2.111\n",
      "    step: 200; loss: 7858.021; l2dist: 2.241\n",
      "    step: 300; loss: 7829.258; l2dist: 2.773\n",
      "    step: 400; loss: 7828.405; l2dist: 2.917\n",
      "    step: 500; loss: 7828.115; l2dist: 2.980\n",
      "binary step: 6; num successful adv: 48/100\n",
      "binary step: 6; num successful adv so far: 99/100\n",
      "    step: 0; loss: 89883.758; l2dist: 0.000\n",
      "    step: 100; loss: 83622.070; l2dist: 2.087\n",
      "    step: 200; loss: 74529.055; l2dist: 2.203\n",
      "    step: 300; loss: 74317.867; l2dist: 2.753\n",
      "    step: 400; loss: 74316.383; l2dist: 2.906\n",
      "binary step: 7; num successful adv: 44/100\n",
      "binary step: 7; num successful adv so far: 99/100\n",
      "    step: 0; loss: 895136.125; l2dist: 0.000\n",
      "    step: 100; loss: 833173.688; l2dist: 2.103\n",
      "    step: 200; loss: 742706.688; l2dist: 2.224\n",
      "    step: 300; loss: 740655.312; l2dist: 2.797\n",
      "    step: 400; loss: 740647.750; l2dist: 2.963\n",
      "binary step: 8; num successful adv: 56/100\n",
      "binary step: 8; num successful adv so far: 99/100\n",
      "    step: 0; loss: 8948251687596851200.000; l2dist: 0.000\n",
      "    step: 100; loss: 8329156822170599424.000; l2dist: 2.166\n",
      "    step: 200; loss: 7424856337080647680.000; l2dist: 2.291\n",
      "    step: 300; loss: 7404408169583083520.000; l2dist: 2.889\n",
      "    step: 400; loss: 7404343298397044736.000; l2dist: 3.026\n",
      "binary step: 9; num successful adv: 90/100\n",
      "binary step: 9; num successful adv so far: 99/100\n"
     ]
    }
   ],
   "source": [
    "# Attack for L2 DkNN\n",
    "\n",
    "attack = DKNNAttackV2()\n",
    "\n",
    "def attack_batch(x, y, batch_size):\n",
    "    x_adv = torch.zeros_like(x)\n",
    "    total_num = x.size(0)\n",
    "    num_batches = int(np.ceil(total_num / batch_size))\n",
    "    for i in range(num_batches):\n",
    "        begin = i * batch_size\n",
    "        end = (i + 1) * batch_size\n",
    "        x_adv[begin:end] = attack(\n",
    "            dknn, x[begin:end], y[begin:end],\n",
    "            guide_layer=layers[0], m=10, binary_search_steps=10,\n",
    "            max_iterations=1000, learning_rate=1e-2, guide_mode=2,\n",
    "            initial_const=1e-1, abort_early=True, random_start=False)\n",
    "    return x_adv\n",
    "\n",
    "num = 100\n",
    "x_adv = attack_batch(x_test[:num].cuda(), y_test[:num], 100)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 98,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "    step: 0; loss: 9.193; l2dist: 0.000\n",
      "    step: 100; loss: 5.806; l2dist: 1.497\n",
      "    step: 200; loss: 5.825; l2dist: 1.508\n",
      "    step: 300; loss: 5.700; l2dist: 1.489\n",
      "    step: 400; loss: 5.724; l2dist: 1.498\n",
      "    step: 500; loss: 5.719; l2dist: 1.494\n",
      "    step: 600; loss: 5.708; l2dist: 1.508\n",
      "    step: 700; loss: 5.735; l2dist: 1.511\n",
      "    step: 800; loss: 5.710; l2dist: 1.516\n",
      "    step: 900; loss: 5.742; l2dist: 1.515\n",
      "binary step: 0; num successful adv: 18/100\n",
      "binary step: 0; num successful adv so far: 22/100\n",
      "    step: 0; loss: 85.556; l2dist: 0.000\n",
      "    step: 100; loss: 14.715; l2dist: 2.686\n",
      "    step: 200; loss: 13.328; l2dist: 2.651\n",
      "    step: 300; loss: 11.021; l2dist: 2.637\n",
      "    step: 400; loss: 11.123; l2dist: 2.609\n",
      "    step: 500; loss: 10.034; l2dist: 2.641\n",
      "    step: 600; loss: 10.146; l2dist: 2.640\n",
      "    step: 700; loss: 9.722; l2dist: 2.641\n",
      "    step: 800; loss: 9.791; l2dist: 2.627\n",
      "    step: 900; loss: 9.821; l2dist: 2.661\n",
      "binary step: 1; num successful adv: 79/100\n",
      "binary step: 1; num successful adv so far: 98/100\n",
      "    step: 0; loss: 125.340; l2dist: 0.000\n",
      "    step: 100; loss: 23.663; l2dist: 2.574\n",
      "    step: 200; loss: 17.133; l2dist: 2.581\n",
      "    step: 300; loss: 13.227; l2dist: 2.558\n",
      "    step: 400; loss: 13.981; l2dist: 2.563\n",
      "    step: 500; loss: 12.683; l2dist: 2.580\n",
      "    step: 600; loss: 12.144; l2dist: 2.562\n",
      "    step: 700; loss: 11.813; l2dist: 2.571\n",
      "    step: 800; loss: 11.949; l2dist: 2.590\n",
      "    step: 900; loss: 12.002; l2dist: 2.622\n",
      "binary step: 2; num successful adv: 72/100\n",
      "binary step: 2; num successful adv so far: 99/100\n",
      "    step: 0; loss: 159.852; l2dist: 0.000\n",
      "    step: 100; loss: 45.072; l2dist: 2.445\n",
      "    step: 200; loss: 42.559; l2dist: 2.422\n",
      "    step: 300; loss: 31.647; l2dist: 2.475\n",
      "    step: 400; loss: 23.996; l2dist: 2.471\n",
      "    step: 500; loss: 15.516; l2dist: 2.476\n",
      "    step: 600; loss: 14.397; l2dist: 2.463\n",
      "    step: 700; loss: 11.872; l2dist: 2.462\n",
      "    step: 800; loss: 10.607; l2dist: 2.501\n",
      "    step: 900; loss: 8.957; l2dist: 2.492\n",
      "binary step: 3; num successful adv: 63/100\n",
      "binary step: 3; num successful adv so far: 100/100\n",
      "    step: 0; loss: 102.661; l2dist: 0.000\n",
      "    step: 100; loss: 29.743; l2dist: 2.389\n",
      "    step: 200; loss: 27.542; l2dist: 2.381\n",
      "    step: 300; loss: 21.222; l2dist: 2.427\n",
      "    step: 400; loss: 17.131; l2dist: 2.407\n",
      "    step: 500; loss: 12.552; l2dist: 2.446\n",
      "    step: 600; loss: 10.815; l2dist: 2.437\n",
      "    step: 700; loss: 9.093; l2dist: 2.419\n",
      "    step: 800; loss: 8.829; l2dist: 2.418\n",
      "    step: 900; loss: 8.777; l2dist: 2.413\n",
      "binary step: 4; num successful adv: 61/100\n",
      "binary step: 4; num successful adv so far: 100/100\n",
      "    step: 0; loss: 77.464; l2dist: 0.000\n",
      "    step: 100; loss: 22.912; l2dist: 2.352\n",
      "    step: 200; loss: 21.406; l2dist: 2.336\n",
      "    step: 300; loss: 19.048; l2dist: 2.354\n",
      "    step: 400; loss: 18.736; l2dist: 2.353\n",
      "    step: 500; loss: 17.466; l2dist: 2.380\n",
      "    step: 600; loss: 14.552; l2dist: 2.395\n",
      "    step: 700; loss: 14.737; l2dist: 2.397\n",
      "    step: 800; loss: 12.209; l2dist: 2.396\n",
      "    step: 900; loss: 11.614; l2dist: 2.398\n",
      "binary step: 5; num successful adv: 59/100\n",
      "binary step: 5; num successful adv so far: 100/100\n",
      "    step: 0; loss: 76.632; l2dist: 0.000\n",
      "    step: 100; loss: 24.103; l2dist: 2.240\n",
      "    step: 200; loss: 23.804; l2dist: 2.238\n",
      "    step: 300; loss: 17.945; l2dist: 2.244\n",
      "    step: 400; loss: 15.481; l2dist: 2.257\n",
      "    step: 500; loss: 11.249; l2dist: 2.299\n",
      "    step: 600; loss: 9.759; l2dist: 2.324\n",
      "    step: 700; loss: 9.071; l2dist: 2.299\n",
      "    step: 800; loss: 9.055; l2dist: 2.285\n",
      "    step: 900; loss: 9.023; l2dist: 2.311\n",
      "binary step: 6; num successful adv: 36/100\n",
      "binary step: 6; num successful adv so far: 100/100\n",
      "    step: 0; loss: 61.882; l2dist: 0.000\n",
      "    step: 100; loss: 19.054; l2dist: 2.222\n",
      "    step: 200; loss: 17.819; l2dist: 2.236\n",
      "    step: 300; loss: 15.449; l2dist: 2.266\n",
      "    step: 400; loss: 15.717; l2dist: 2.224\n",
      "    step: 500; loss: 15.133; l2dist: 2.267\n",
      "    step: 600; loss: 12.533; l2dist: 2.267\n",
      "    step: 700; loss: 13.979; l2dist: 2.255\n",
      "    step: 800; loss: 10.068; l2dist: 2.258\n",
      "    step: 900; loss: 10.118; l2dist: 2.269\n",
      "binary step: 7; num successful adv: 42/100\n",
      "binary step: 7; num successful adv so far: 100/100\n",
      "    step: 0; loss: 59.055; l2dist: 0.000\n",
      "    step: 100; loss: 18.407; l2dist: 2.212\n",
      "    step: 200; loss: 17.104; l2dist: 2.202\n",
      "    step: 300; loss: 15.065; l2dist: 2.240\n",
      "    step: 400; loss: 15.223; l2dist: 2.244\n",
      "    step: 500; loss: 13.442; l2dist: 2.231\n",
      "    step: 600; loss: 13.877; l2dist: 2.248\n",
      "    step: 700; loss: 12.815; l2dist: 2.234\n",
      "    step: 800; loss: 12.956; l2dist: 2.242\n",
      "    step: 900; loss: 12.763; l2dist: 2.240\n",
      "binary step: 8; num successful adv: 40/100\n",
      "binary step: 8; num successful adv so far: 100/100\n",
      "    step: 0; loss: 60.889; l2dist: 0.000\n",
      "    step: 100; loss: 18.896; l2dist: 2.183\n",
      "    step: 200; loss: 17.636; l2dist: 2.210\n",
      "    step: 300; loss: 15.287; l2dist: 2.237\n",
      "    step: 400; loss: 15.369; l2dist: 2.231\n",
      "    step: 500; loss: 14.871; l2dist: 2.264\n",
      "    step: 600; loss: 12.441; l2dist: 2.261\n",
      "    step: 700; loss: 13.796; l2dist: 2.233\n",
      "    step: 800; loss: 9.654; l2dist: 2.215\n",
      "    step: 900; loss: 9.824; l2dist: 2.215\n",
      "binary step: 9; num successful adv: 70/100\n",
      "binary step: 9; num successful adv so far: 100/100\n"
     ]
    }
   ],
   "source": [
    "from lib.dknn_attack_exp import DKNNExpAttack\n",
    "attack = DKNNExpAttack(dknn)\n",
    "\n",
    "def attack_batch(x, y, batch_size):\n",
    "    x_adv = torch.zeros_like(x)\n",
    "    total_num = x.size(0)\n",
    "    num_batches = int(np.ceil(total_num / batch_size))\n",
    "    for i in range(num_batches):\n",
    "        begin = i * batch_size\n",
    "        end = (i + 1) * batch_size\n",
    "        x_adv[begin:end] = attack(\n",
    "            x[begin:end], y[begin:end],\n",
    "            guide_layer=layers[0], m=10, init_mode=1, \n",
    "            binary_search_steps=10, max_iterations=1000, learning_rate=1e-1,\n",
    "            initial_const=1e-1, random_start=False,\n",
    "            thres_steps=200, check_adv_steps=200, verbose=True,\n",
    "            max_linf=None)\n",
    "    return x_adv\n",
    "\n",
    "num = 100\n",
    "x_adv = attack_batch(x_test[ind][:num].cuda(), y_test[ind][:num], 100)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {
    "scrolled": true
   },
   "outputs": [],
   "source": [
    "def attack_batch(attack, x, y, init_mode, init_mode_k, batch_size):\n",
    "    x_adv = torch.zeros_like(x)\n",
    "    total_num = x.size(0)\n",
    "    num_batches = total_num // batch_size\n",
    "    for i in range(num_batches):\n",
    "        begin = i * batch_size\n",
    "        end = (i + 1) * batch_size\n",
    "        x_adv[begin:end] = attack(\n",
    "            x[begin:end], y[begin:end], 2, guide_layer=layers, m=150,\n",
    "            init_mode=init_mode, init_mode_k=init_mode_k,\n",
    "            binary_search_steps=10, max_iterations=1000, learning_rate=1e-1,\n",
    "            initial_const=1e-1, max_linf=None, random_start=True,\n",
    "            thres_steps=200, check_adv_steps=200, verbose=False)\n",
    "    return x_adv\n",
    "\n",
    "num = 100\n",
    "\n",
    "def full_eval(dknn):\n",
    "    with torch.no_grad():\n",
    "        y_pred = dknn.classify(x_test)\n",
    "        ind = np.where(y_pred.argmax(1) == y_test.numpy())[0]\n",
    "    print((y_pred.argmax(1) == y_test.numpy()).sum() / y_test.size(0))\n",
    "    \n",
    "    dist_all = np.zeros(num) + 1e9\n",
    "    attack = DKNNAttackV2(dknn)\n",
    "    \n",
    "    x_adv = attack_batch(\n",
    "        attack, x_test[ind][:num].cuda(), y_test[ind][:num], 1, 1, 100)\n",
    "    with torch.no_grad():\n",
    "        y_pred = dknn.classify(x_adv)\n",
    "        ind_adv = y_pred.argmax(1) != y_test[ind][:num].numpy()\n",
    "        dist = (x_adv.cpu() - x_test[ind][:num]).view(\n",
    "            num, -1).norm(2, 1).numpy()\n",
    "    for i in range(num):\n",
    "        if ind_adv[i] and (dist[i] < dist_all[i]):\n",
    "            dist_all[i] = dist[i]\n",
    "            \n",
    "    for k in range(1, 6):\n",
    "        x_adv = attack_batch(\n",
    "            attack, x_test[ind][:num].cuda(), y_test[ind][:num], 2, k, 100)\n",
    "        with torch.no_grad():\n",
    "            y_pred = dknn.classify(x_adv)\n",
    "            ind_adv = y_pred.argmax(1) != y_test[ind][:num].numpy()\n",
    "            dist = (x_adv.cpu() - x_test[ind][:num]).view(\n",
    "                num, -1).norm(2, 1).numpy()\n",
    "        for i in range(num):\n",
    "            if ind_adv[i] and (dist[i] < dist_all[i]):\n",
    "                dist_all[i] = dist[i]\n",
    "                \n",
    "    adv_acc = (dist_all == 1e9).mean()\n",
    "    print('adv accuracy: %.4f, mean dist: %.4f' % (\n",
    "        adv_acc, dist_all[dist_all < 1e9].mean()))\n",
    "    return dist_all"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0.9877\n"
     ]
    }
   ],
   "source": [
    "start = time.time()\n",
    "dist = full_eval(dknn)\n",
    "print(time.time() - start)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "1.753950898051262"
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "dist.mean()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "4.7507405281066895\n"
     ]
    }
   ],
   "source": [
    "i = 0\n",
    "plt.imshow(x_adv[i].detach().cpu().numpy().reshape(28, 28), cmap='gray')\n",
    "plt.show()\n",
    "plt.imshow(x_test[i].detach().cpu().numpy().reshape(28, 28), cmap='gray')\n",
    "plt.show()\n",
    "print((x_adv[i].cpu() - x_test[i]).view(-1).norm().item())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 106,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0.02\n"
     ]
    }
   ],
   "source": [
    "with torch.no_grad():\n",
    "    y_pred = dknn.classify(x_adv)\n",
    "    ind_adv = np.where(y_pred.argmax(1) != y_test[ind][:num].numpy())[0]\n",
    "    print((y_pred.argmax(1) == y_test[ind][:num].numpy()).sum() / y_pred.shape[0])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 107,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "4.4058709144592285"
      ]
     },
     "execution_count": 107,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "(x_adv.cpu() - x_test[ind][:num]).view(num, -1).norm(2, 1)[ind_adv].mean().item()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "tensor(0.7600)\n"
     ]
    }
   ],
   "source": [
    "logits = net.compute_logits(x_adv.cuda())\n",
    "acc = (logits.argmax(1).cpu() == y_test[ind][:num]).float().mean()\n",
    "print(acc)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(array([  0.,   0.,   0.,   0.,   0., 100.,   0.,   0.,   0.,   0.]),\n",
       " array([0.5, 0.6, 0.7, 0.8, 0.9, 1. , 1.1, 1.2, 1.3, 1.4, 1.5]),\n",
       " <a list of 10 Patch objects>)"
      ]
     },
     "execution_count": 18,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "cred = dknn.credibility(y_pred)\n",
    "plt.hist(cred)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "image/png": 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A967O5c/rCPvrkpaa2VfMbIakb0naVUMfn2Nmc7IPTmRmcyStU+ctRb1L0sbs642Snq2xl8/olGW885YZV83vXe3Ln7t72/9Iulvjn8j/t6R/rqOHnL7+StLb2Z936+5N0g6NH9YNa/yI6DuS/kLSHkkHs8crO6i3f5P0jqT9Gg/Wopp6+zuN/2q4X9K+7M/ddb93ib7a8r5xuiwQBGfQAUEQdiAIwg4EQdiBIAg7EARhB4Ig7EAQ/wtI4MpQzZe9ZgAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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TST8t3syLHmvCtt6AiaKs9XbCXDcewAiuGw9kjrADmSDsQCYIO5AJwg5kgrADmSDsQCYIO5AJwg5kgrADmSDsQCYIO5AJwg5kgrADmej0ks1fSNo26utZxX3dqFvH1q3jkhhbq+oc28KyQkfns3/ryc0GuvXadN06tm4dl8TYWtWpsfEyHsgEYQcy0XTY+xt+/ki3jq1bxyUxtlZ1ZGyN/s0OoHOaPrID6BDCDmSikbCb2XIz+28z22xm9zQxhjJm9omZfWBm7zW9Pl2xht6QmW0Ydd9MM3vZzDYVt2OusdfQ2O4zs8+KffeemV3R0NgWmNnrZrbRzD40s58X9ze674JxdWS/dfxvdjPrkfQ/kv5O0nZJ70i6yd0/6uhASpjZJ5KWunvjJ2CY2d9IGpb0uLv/RXHfP0va6e4PFL8oZ7j73V0ytvskDTe9jHexWtG80cuMS7pG0j+owX0XjOsGdWC/NXFkv0jSZnff4u6HJD0t6eoGxtH13P1NSTu/cffVktYUn6/RyH+WjisZW1dw9x3uvr74fI+k48uMN7rvgnF1RBNhny/pz6O+3q7uWu/dJb1kZu+a2cqmBzOGOceX2SpuZzc8nm9KLuPdSd9YZrxr9l0ry59X1UTYx1qappv6fxe7+19J+rGknxUvVzE+41rGu1PGWGa8K7S6/HlVTYR9u6QFo74+U9JgA+MYk7sPFrdDkp5V9y1F/fnxFXSL26GGx/OVblrGe6xlxtUF+67J5c+bCPs7ks4xs++ZWa+kn0ha28A4vsXMphZvnMjMpkr6kbpvKeq1km4uPr9Z0nMNjuVrumUZ77JlxtXwvmt8+XN37/iHpCs08o78/0r6pybGUDKu70v6r+Ljw6bHJun3GnlZd1gjr4hulXSGpFclbSpuZ3bR2J7QyNLe72skWPMaGtsPNfKn4fuS3is+rmh63wXj6sh+43RZIBOcQQdkgrADmSDsQCYIO5AJwg5kgrADmSDsQCb+H2tQmof9Uj93AAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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ubvy1G6+vTr1uTYT9XUnLzWyZmc2Q9A1J2xro43PMbG7xwYnMbK6kVeq+pai3SVpbfL9W0usN9vIZ3bKMd9ky42r4tWt8+XN37/iXpEc1+on8/0j6myZ6KOnrDyX9qvja13RvkrZq9LDukkaPiJ6RdJOkXZIOFLeLuqi3f9Ho0t4faDRY/Q319mWNvjX8QNLu4uvRpl+7RF8ded04XRYIgjPogCAIOxAEYQeCIOxAEIQdCIKwA0EQdiCI/wOxOryitsOdFAAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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rn5VtP3eFvgbyvPFxWSAJPkEHJEHYgSQIO5AEYQeSIOxAEoQdSIKwA0n8P/HmdmLlsQEVAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "for i in range(5):\n",
    "    plt.imshow(x_adv[i].cpu().detach().numpy().squeeze(), cmap='gray')\n",
    "    plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "    step: 0; loss: 7807.614\n",
      "    step: 50; loss: 1300.460\n",
      "    step: 100; loss: 740.567\n",
      "    step: 150; loss: 579.734\n",
      "    step: 200; loss: 498.007\n",
      "    step: 250; loss: 458.374\n",
      "    step: 300; loss: 445.230\n",
      "    step: 350; loss: 428.613\n",
      "    step: 400; loss: 425.181\n",
      "    step: 450; loss: 416.420\n",
      "number of successful adv: 80/100\n"
     ]
    }
   ],
   "source": [
    "attack = DKNN_PGD(dknn)\n",
    "\n",
    "def attack_batch(x, y, batch_size):\n",
    "    x_adv = torch.zeros_like(x)\n",
    "    total_num = x.size(0)\n",
    "    num_batches = total_num // batch_size\n",
    "    for i in range(num_batches):\n",
    "        begin = i * batch_size\n",
    "        end = (i + 1) * batch_size\n",
    "        x_adv[begin:end] = attack(\n",
    "            x[begin:end], y[begin:end],\n",
    "            guide_layer=layers[0], m=24, epsilon=0.01,\n",
    "            max_epsilon=0.3, max_iterations=500, num_restart=1,\n",
    "            rand_start=True, thres_steps=100, check_adv_steps=100,\n",
    "            verbose=True)\n",
    "    return x_adv\n",
    "\n",
    "num = 100\n",
    "x_adv = attack_batch(x_test[ind][:num].cuda(), y_test[ind][:num], 100)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## CW L2 Attack\n",
    "\n",
    "without DkNN"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "    step: 0; loss: 13.405; l2dist: 0.000\n",
      "    step: 100; loss: 10.804; l2dist: 1.309\n",
      "    step: 200; loss: 10.790; l2dist: 1.320\n",
      "    step: 300; loss: 10.706; l2dist: 1.327\n",
      "    step: 400; loss: 10.543; l2dist: 1.378\n",
      "    step: 500; loss: 10.419; l2dist: 1.434\n",
      "    step: 600; loss: 10.330; l2dist: 1.472\n",
      "    step: 700; loss: 10.271; l2dist: 1.492\n",
      "    step: 800; loss: 10.225; l2dist: 1.507\n",
      "    step: 900; loss: 10.189; l2dist: 1.519\n",
      "binary step: 0; number of successful adv: 18/100\n",
      "    step: 0; loss: 111.046; l2dist: 0.324\n",
      "    step: 100; loss: 41.892; l2dist: 3.591\n",
      "    step: 200; loss: 32.390; l2dist: 3.909\n",
      "    step: 300; loss: 21.221; l2dist: 3.721\n",
      "    step: 400; loss: 16.908; l2dist: 3.518\n",
      "    step: 500; loss: 14.763; l2dist: 3.400\n",
      "    step: 600; loss: 13.892; l2dist: 3.331\n",
      "    step: 700; loss: 13.365; l2dist: 3.281\n",
      "    step: 800; loss: 13.029; l2dist: 3.245\n",
      "    step: 900; loss: 12.853; l2dist: 3.222\n",
      "binary step: 1; number of successful adv: 97/100\n",
      "    step: 0; loss: 201.791; l2dist: 0.321\n",
      "    step: 100; loss: 50.556; l2dist: 3.838\n",
      "    step: 200; loss: 33.668; l2dist: 3.861\n",
      "    step: 300; loss: 21.572; l2dist: 3.707\n",
      "    step: 400; loss: 16.804; l2dist: 3.532\n",
      "    step: 500; loss: 14.430; l2dist: 3.405\n",
      "    step: 600; loss: 13.517; l2dist: 3.339\n",
      "    step: 700; loss: 13.134; l2dist: 3.308\n",
      "    step: 800; loss: 12.783; l2dist: 3.260\n",
      "    step: 900; loss: 12.587; l2dist: 3.239\n",
      "binary step: 2; number of successful adv: 100/100\n",
      "    step: 0; loss: 119.541; l2dist: 0.325\n",
      "    step: 100; loss: 37.933; l2dist: 3.471\n",
      "    step: 200; loss: 28.409; l2dist: 3.526\n",
      "    step: 300; loss: 21.222; l2dist: 3.471\n",
      "    step: 400; loss: 17.560; l2dist: 3.439\n",
      "    step: 500; loss: 15.098; l2dist: 3.396\n",
      "    step: 600; loss: 13.775; l2dist: 3.312\n",
      "    step: 700; loss: 13.146; l2dist: 3.266\n",
      "    step: 800; loss: 12.790; l2dist: 3.257\n",
      "    step: 900; loss: 12.614; l2dist: 3.249\n",
      "binary step: 3; number of successful adv: 100/100\n",
      "    step: 0; loss: 88.786; l2dist: 0.323\n",
      "    step: 100; loss: 32.492; l2dist: 3.273\n",
      "    step: 200; loss: 26.443; l2dist: 3.387\n",
      "    step: 300; loss: 20.771; l2dist: 3.392\n",
      "    step: 400; loss: 17.567; l2dist: 3.388\n",
      "    step: 500; loss: 15.544; l2dist: 3.367\n",
      "    step: 600; loss: 14.213; l2dist: 3.316\n",
      "    step: 700; loss: 13.263; l2dist: 3.270\n",
      "    step: 800; loss: 12.882; l2dist: 3.238\n",
      "    step: 900; loss: 12.661; l2dist: 3.218\n",
      "binary step: 4; number of successful adv: 100/100\n",
      "    step: 0; loss: 69.050; l2dist: 0.322\n",
      "    step: 100; loss: 28.858; l2dist: 3.083\n",
      "    step: 200; loss: 25.065; l2dist: 3.208\n",
      "    step: 300; loss: 20.596; l2dist: 3.235\n",
      "    step: 400; loss: 17.934; l2dist: 3.267\n",
      "    step: 500; loss: 16.185; l2dist: 3.307\n",
      "    step: 600; loss: 14.754; l2dist: 3.306\n",
      "    step: 700; loss: 13.884; l2dist: 3.281\n",
      "    step: 800; loss: 13.036; l2dist: 3.251\n",
      "    step: 900; loss: 12.671; l2dist: 3.222\n",
      "binary step: 5; number of successful adv: 100/100\n",
      "    step: 0; loss: 61.887; l2dist: 0.323\n",
      "    step: 100; loss: 27.285; l2dist: 2.980\n",
      "    step: 200; loss: 24.364; l2dist: 3.130\n",
      "    step: 300; loss: 20.420; l2dist: 3.168\n",
      "    step: 400; loss: 17.883; l2dist: 3.212\n",
      "    step: 500; loss: 16.216; l2dist: 3.269\n",
      "    step: 600; loss: 15.136; l2dist: 3.286\n",
      "    step: 700; loss: 14.158; l2dist: 3.300\n",
      "    step: 800; loss: 13.361; l2dist: 3.287\n",
      "    step: 900; loss: 12.928; l2dist: 3.266\n",
      "binary step: 6; number of successful adv: 100/100\n",
      "    step: 0; loss: 60.544; l2dist: 0.322\n",
      "    step: 100; loss: 26.717; l2dist: 2.964\n",
      "    step: 200; loss: 24.158; l2dist: 3.102\n",
      "    step: 300; loss: 20.385; l2dist: 3.145\n",
      "    step: 400; loss: 17.967; l2dist: 3.186\n",
      "    step: 500; loss: 16.332; l2dist: 3.237\n",
      "    step: 600; loss: 15.356; l2dist: 3.251\n",
      "    step: 700; loss: 14.281; l2dist: 3.268\n",
      "    step: 800; loss: 13.545; l2dist: 3.260\n",
      "    step: 900; loss: 13.056; l2dist: 3.247\n",
      "binary step: 7; number of successful adv: 100/100\n",
      "    step: 0; loss: 59.336; l2dist: 0.322\n",
      "    step: 100; loss: 26.360; l2dist: 2.961\n",
      "    step: 200; loss: 24.040; l2dist: 3.101\n",
      "    step: 300; loss: 20.290; l2dist: 3.153\n",
      "    step: 400; loss: 17.576; l2dist: 3.222\n",
      "    step: 500; loss: 15.896; l2dist: 3.254\n",
      "    step: 600; loss: 14.845; l2dist: 3.255\n",
      "    step: 700; loss: 14.194; l2dist: 3.261\n",
      "    step: 800; loss: 13.645; l2dist: 3.243\n",
      "    step: 900; loss: 12.958; l2dist: 3.226\n",
      "binary step: 8; number of successful adv: 100/100\n",
      "    step: 0; loss: 60.299; l2dist: 0.322\n",
      "    step: 100; loss: 26.585; l2dist: 2.960\n",
      "    step: 200; loss: 24.215; l2dist: 3.093\n",
      "    step: 300; loss: 20.469; l2dist: 3.146\n",
      "    step: 400; loss: 17.914; l2dist: 3.198\n",
      "    step: 500; loss: 16.189; l2dist: 3.243\n",
      "    step: 600; loss: 15.083; l2dist: 3.255\n",
      "    step: 700; loss: 14.236; l2dist: 3.261\n",
      "    step: 800; loss: 13.342; l2dist: 3.261\n",
      "    step: 900; loss: 12.885; l2dist: 3.235\n",
      "binary step: 9; number of successful adv: 100/100\n",
      "    step: 0; loss: 40.921; l2dist: 6.239\n",
      "    step: 100; loss: 21.351; l2dist: 4.215\n",
      "    step: 200; loss: 18.259; l2dist: 3.814\n",
      "    step: 300; loss: 10.785; l2dist: 2.356\n",
      "    step: 400; loss: 10.331; l2dist: 2.220\n",
      "    step: 500; loss: 10.111; l2dist: 2.185\n",
      "    step: 600; loss: 9.973; l2dist: 2.169\n",
      "    step: 700; loss: 9.882; l2dist: 2.166\n",
      "    step: 800; loss: 9.824; l2dist: 2.166\n",
      "    step: 900; loss: 9.761; l2dist: 2.157\n",
      "binary step: 0; number of successful adv: 100/100\n",
      "    step: 0; loss: 47.400; l2dist: 6.240\n",
      "    step: 100; loss: 23.619; l2dist: 4.709\n",
      "    step: 200; loss: 20.454; l2dist: 4.377\n",
      "    step: 300; loss: 14.267; l2dist: 3.517\n",
      "    step: 400; loss: 13.228; l2dist: 3.334\n",
      "    step: 500; loss: 12.667; l2dist: 3.244\n",
      "    step: 600; loss: 12.339; l2dist: 3.195\n",
      "    step: 700; loss: 12.087; l2dist: 3.158\n",
      "    step: 800; loss: 11.879; l2dist: 3.126\n",
      "    step: 900; loss: 11.772; l2dist: 3.109\n",
      "binary step: 1; number of successful adv: 100/100\n",
      "    step: 0; loss: 54.092; l2dist: 6.241\n",
      "    step: 100; loss: 23.885; l2dist: 4.733\n",
      "    step: 200; loss: 20.634; l2dist: 4.398\n",
      "    step: 300; loss: 14.319; l2dist: 3.552\n",
      "    step: 400; loss: 13.430; l2dist: 3.410\n",
      "    step: 500; loss: 13.053; l2dist: 3.360\n",
      "    step: 600; loss: 12.716; l2dist: 3.319\n",
      "    step: 700; loss: 12.446; l2dist: 3.284\n",
      "    step: 800; loss: 12.332; l2dist: 3.264\n",
      "    step: 900; loss: 12.081; l2dist: 3.226\n",
      "binary step: 2; number of successful adv: 100/100\n",
      "    step: 0; loss: 48.394; l2dist: 6.245\n",
      "    step: 100; loss: 23.476; l2dist: 4.660\n",
      "    step: 200; loss: 20.291; l2dist: 4.343\n",
      "    step: 300; loss: 13.941; l2dist: 3.484\n",
      "    step: 400; loss: 13.064; l2dist: 3.350\n",
      "    step: 500; loss: 12.599; l2dist: 3.286\n",
      "    step: 600; loss: 12.339; l2dist: 3.251\n",
      "    step: 700; loss: 12.147; l2dist: 3.224\n",
      "    step: 800; loss: 11.978; l2dist: 3.213\n",
      "    step: 900; loss: 11.906; l2dist: 3.184\n",
      "binary step: 3; number of successful adv: 100/100\n",
      "    step: 0; loss: 45.846; l2dist: 6.244\n",
      "    step: 100; loss: 23.325; l2dist: 4.627\n",
      "    step: 200; loss: 20.133; l2dist: 4.311\n",
      "    step: 300; loss: 13.759; l2dist: 3.441\n",
      "    step: 400; loss: 12.879; l2dist: 3.314\n",
      "    step: 500; loss: 12.491; l2dist: 3.270\n",
      "    step: 600; loss: 12.216; l2dist: 3.239\n",
      "    step: 700; loss: 12.087; l2dist: 3.226\n",
      "    step: 800; loss: 12.080; l2dist: 3.225\n",
      "    step: 900; loss: 11.842; l2dist: 3.189\n",
      "binary step: 4; number of successful adv: 100/100\n",
      "    step: 0; loss: 44.825; l2dist: 6.242\n",
      "    step: 100; loss: 23.222; l2dist: 4.621\n",
      "    step: 200; loss: 20.036; l2dist: 4.294\n",
      "    step: 300; loss: 13.712; l2dist: 3.421\n",
      "    step: 400; loss: 12.910; l2dist: 3.309\n",
      "    step: 500; loss: 12.519; l2dist: 3.256\n",
      "    step: 600; loss: 12.281; l2dist: 3.223\n",
      "    step: 700; loss: 12.057; l2dist: 3.196\n",
      "    step: 800; loss: 11.933; l2dist: 3.183\n",
      "    step: 900; loss: 11.836; l2dist: 3.171\n",
      "binary step: 5; number of successful adv: 100/100\n",
      "    step: 0; loss: 44.489; l2dist: 6.242\n",
      "    step: 100; loss: 23.203; l2dist: 4.611\n",
      "    step: 200; loss: 20.044; l2dist: 4.293\n",
      "    step: 300; loss: 13.684; l2dist: 3.423\n",
      "    step: 400; loss: 12.850; l2dist: 3.294\n",
      "    step: 500; loss: 12.486; l2dist: 3.245\n",
      "    step: 600; loss: 12.221; l2dist: 3.213\n",
      "    step: 700; loss: 12.031; l2dist: 3.189\n",
      "    step: 800; loss: 11.890; l2dist: 3.176\n",
      "    step: 900; loss: 11.813; l2dist: 3.163\n",
      "binary step: 6; number of successful adv: 100/100\n",
      "    step: 0; loss: 44.347; l2dist: 6.245\n",
      "    step: 100; loss: 23.169; l2dist: 4.613\n",
      "    step: 200; loss: 20.064; l2dist: 4.298\n",
      "    step: 300; loss: 13.623; l2dist: 3.410\n",
      "    step: 400; loss: 12.853; l2dist: 3.296\n",
      "    step: 500; loss: 12.445; l2dist: 3.237\n",
      "    step: 600; loss: 12.178; l2dist: 3.207\n",
      "    step: 700; loss: 11.985; l2dist: 3.183\n",
      "    step: 800; loss: 11.844; l2dist: 3.165\n",
      "    step: 900; loss: 11.778; l2dist: 3.151\n",
      "binary step: 7; number of successful adv: 100/100\n",
      "    step: 0; loss: 44.436; l2dist: 6.243\n",
      "    step: 100; loss: 23.160; l2dist: 4.604\n",
      "    step: 200; loss: 20.039; l2dist: 4.291\n",
      "    step: 300; loss: 13.612; l2dist: 3.408\n",
      "    step: 400; loss: 12.808; l2dist: 3.285\n",
      "    step: 500; loss: 12.405; l2dist: 3.234\n",
      "    step: 600; loss: 12.140; l2dist: 3.199\n",
      "    step: 700; loss: 11.949; l2dist: 3.175\n",
      "    step: 800; loss: 11.822; l2dist: 3.160\n",
      "    step: 900; loss: 11.748; l2dist: 3.150\n",
      "binary step: 8; number of successful adv: 100/100\n",
      "    step: 0; loss: 44.515; l2dist: 6.247\n",
      "    step: 100; loss: 23.214; l2dist: 4.613\n",
      "    step: 200; loss: 20.142; l2dist: 4.300\n",
      "    step: 300; loss: 13.644; l2dist: 3.411\n",
      "    step: 400; loss: 12.854; l2dist: 3.291\n",
      "    step: 500; loss: 12.482; l2dist: 3.248\n",
      "    step: 600; loss: 12.189; l2dist: 3.204\n",
      "    step: 700; loss: 12.027; l2dist: 3.183\n",
      "    step: 800; loss: 11.873; l2dist: 3.167\n",
      "    step: 900; loss: 11.777; l2dist: 3.154\n",
      "binary step: 9; number of successful adv: 100/100\n"
     ]
    }
   ],
   "source": [
    "from lib.cwl2_attack import CWL2Attack\n",
    "\n",
    "attack = CWL2Attack(net, x_train=x_train, y_train=y_train)\n",
    "\n",
    "def attack_batch(x, y, batch_size):\n",
    "    x_adv = torch.zeros_like(x)\n",
    "    total_num = x.size(0)\n",
    "    num_batches = total_num // batch_size\n",
    "    for i in range(num_batches):\n",
    "        begin = i * batch_size\n",
    "        end = (i + 1) * batch_size\n",
    "        x_adv[begin:end] = attack(\n",
    "            x[begin:end], y[begin:end], targeted=False,\n",
    "            init_mode=2, binary_search_steps=10, max_iterations=1000,\n",
    "            confidence=0, learning_rate=1e-2, initial_const=1e1, \n",
    "            abort_early=False, rand_start_std=0.1, check_adv_steps=100)\n",
    "    return x_adv\n",
    "\n",
    "num = 100\n",
    "x_adv = attack_batch(x_test[ind][:num].cuda(), y_test[ind][:num].cuda(), 100)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0.0\n",
      "2.9907801151275635 3.2651774883270264\n"
     ]
    }
   ],
   "source": [
    "y_pred = net(x_adv).argmax(1).cpu()\n",
    "print((y_pred == y_test[ind][:num]).numpy().sum() / y_pred.size(0))\n",
    "dist = (x_adv.cpu() - x_test[ind][:num]).view(num, -1).norm(2, 1)\n",
    "print(dist.mean().item(), dist.median().item())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "y_pred = dknn.classify(x_adv)\n",
    "(y_pred.argmax(1) == y_test[:10000].numpy()).sum() / len(y_pred)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 90,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(array([139.,  46.,  23.,  19.,   3.,   3.,   4.,   2.,   2.,   3.]),\n",
       " array([0.0165    , 0.08763333, 0.15876667, 0.2299    , 0.30103333,\n",
       "        0.37216667, 0.4433    , 0.51443333, 0.58556667, 0.6567    ,\n",
       "        0.72783333]),\n",
       " <a list of 10 Patch objects>)"
      ]
     },
     "execution_count": 90,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "cred = dknn.credibility(y_pred)\n",
    "suc_ind = y_pred.argmax(1) != y_test[:10000].numpy()\n",
    "plt.hist(cred[suc_ind])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 87,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.image.AxesImage at 0x7f5e5ddbbac8>"
      ]
     },
     "execution_count": 87,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAP8AAAD8CAYAAAC4nHJkAAAABHNCSVQICAgIfAhkiAAAAAlwSFlzAAALEgAACxIB0t1+/AAAADl0RVh0U29mdHdhcmUAbWF0cGxvdGxpYiB2ZXJzaW9uIDMuMC4zLCBodHRwOi8vbWF0cGxvdGxpYi5vcmcvnQurowAAEuNJREFUeJzt3W9sVXWaB/Dv09oiUP6UQJkiXRjRrKtoGNOQTdANm5WJ6CQ4MUPgxQQTMgzJmEgyL9YQk/HNJmazM7O+2IzpCBk0Mw4kg0iM2cWQNe4kK1r/ZJRFF0SgCBYofwtKae+zL3qYdKDneS733HPOZZ/vJzFt79Nz78/b++Xc9jm/309UFUQUT1PZAyCicjD8REEx/ERBMfxEQTH8REEx/ERBMfxEQTH8REEx/ERB3VLkg4lIw15OKCJmPc8rIb3H9vAqzfq7mX8mqlrV4DOFX0QeBvA8gGYAL6rqc1nur0wtLS1mfWhoKLV2yy320zg8PGzWveObm5vNujU270U8MjJi1pua7DeHlUrFrN+svJ+J58qVK3UaSX5qftsvIs0A/g3AcgB3A1gtInfXa2BElK8sv/MvBnBAVQ+q6hCA3wNYUZ9hEVHesoT/NgB9Y74+mtz2F0RknYj0ikhvhsciojrL8ovNeL9MXvdXDlXtAdADNPYf/IiiyXLmPwqga8zXcwEcyzYcIipKlvC/B+BOEfm2iLQCWAVgZ32GRUR5q/ltv6oOi8iTAP4Do62+zaq6t24jG0dra2tqzWp3AdnaZR6vlTdp0iSz7rXLvHadVfdaeR5vbLfeeqtZ/+abbzI9fl4mTJhg1i9fvlzQSMqTqZmpqm8AeKNOYyGiAvHyXqKgGH6ioBh+oqAYfqKgGH6ioBh+oqCkyHnHIqLWVEmvX96osvaMs/bKZ8+enVrr7+83j6V8eFPE85zyW+18fp75iYJi+ImCYviJgmL4iYJi+ImCYviJgiq01dfU1KRWC8SbVmu1xLypq15rpcylu8vU1dVl1vv6+sx6lpWLp02bZh577ty53B7bk/W+vfav9Xr1XmvWisrDw8OoVCps9RFROoafKCiGnygohp8oKIafKCiGnygohp8oqEK36AbsHqY3DdLqxXu9UW/5bG/arLVs+M28zLPXr25razPrg4ODZt163mbNmmUe6/XaBwYGzLpl6tSpZj3rzsp5LlmedTn2q3jmJwqK4ScKiuEnCorhJwqK4ScKiuEnCorhJwoqU59fRA4BuABgBMCwqnZb36+q5rx6qycMAF9//XXNx166dMmsT58+3axb4+7o6DCP9a4DOHHihFnPk7eGgve8eqw5+1mXau/s7DTrEydOTK2dOnXKPNb7mc2dO9esHz582KxbvLUArPn8N3J9QT0u8vl7VbWfSSJqOHzbTxRU1vArgF0i8r6IrKvHgIioGFnf9i9R1WMi0gHgTRH5VFXfHvsNyT8K/IeBqMFkOvOr6rHk4wkArwJYPM739Khqt/fHQCIqVs3hF5HJIjLl6ucAvgvgk3oNjIjyleVt/2wArybTbG8B8DtV/fe6jIqIctdQW3R7c6StecxWTxcAzp8/bw8ug9tvv92sf/XVV2a9ubnZrHv98Pb29tSa18/2njdv7fw8eXspzJkzx6xbz+uRI0fMY7Num14mbtFNRCaGnygohp8oKIafKCiGnygohp8oqMKX7rZ4bUdrWq23BbfH2y56+fLlqbWVK1eax3rLW3stzh07dpj1AwcOpNa8ltSFCxfMurfkeaVSMeve9FTLzJkzzbrXrrOW5543b15NY7rKax17U4KtKebec261fm8kBzzzEwXF8BMFxfATBcXwEwXF8BMFxfATBcXwEwVV+JReq+5t0Z21l2/xpnDu378/teZNLfV6wl6vfMaMGWb96NGjqbUvvvjCPNZb/tpbVtwbu3X8/fffbx7rjf3ixYtm3boOYPv27eaxvb29Zj3rVGfr+gfvGgErJ8PDw6hUKpzSS0TpGH6ioBh+oqAYfqKgGH6ioBh+oqAYfqKgCp3PLyJmP91bqtnqKVvLelfDm/e+du3a1Np9991nHuv1hL3tnr1+uLW9+IMPPmge67njjjsyHW9dRzIwMGAe682595ZMt/T19Zn13bt313zfgL8kurXdvKde17vwzE8UFMNPFBTDTxQUw08UFMNPFBTDTxQUw08UlNvnF5HNAL4H4ISqLkxumwFgK4D5AA4BWKmqZ7z7UlUMDQ2l1idPnmweb6117pkyZYpZ99av37VrV2rtrbfeMo/11mE/e/asWffWvre2ol69erV57KZNm8y6d42Bt+dAa2tras1ahwDwf96vv/66WV+0aFFqzds2Pau2tjaznqXPXy/VnPl/A+Dha257GsBuVb0TwO7kayK6ibjhV9W3AZy+5uYVALYkn28B8Fidx0VEOav1d/7ZqnocAJKPHfUbEhEVIfdr+0VkHYB1eT8OEd2YWs/8/SLSCQDJx9RVGlW1R1W7VbW7xsciohzUGv6dANYkn68B8Fp9hkNERXHDLyKvAPhvAH8tIkdFZC2A5wAsE5H9AJYlXxPRTaTQdfubmprU6vt68/m9Ofc3K2/PgDz/v9vb2836mTPu5Ru5efTRR8261+d/9913U2vLli0zj/X2BPCub/B+poODgzXft7WuBdftJyIXw08UFMNPFBTDTxQUw08UFMNPFFThS3dbrb4sLa0y22VZeWObNWuWWT958mTNj+218qyfFwBzirbHm8K9fv36mu8bAF544YXUmrdtusdbKt7bZjvLfVtTuG+kdc8zP1FQDD9RUAw/UVAMP1FQDD9RUAw/UVAMP1FQDTWl1xtLlp5ymbwpmsPDwwWNpHgLFy5MrW3YsME81toWHbCnxQL+cu1lsl4TWV8PqsopvUSUjuEnCorhJwqK4ScKiuEnCorhJwqK4ScKqtD5/KpqznOeNm2aeXyWPr/Xa/eWDbeO97ZbzruPP2fOnNTasWPHcn3szs5Os37XXXel1rw+fl9fn1lftWqVWS/TzbC+BM/8REEx/ERBMfxEQTH8REEx/ERBMfxEQTH8REG5fX4R2QzgewBOqOrC5LZnAfwIwNUF4zeq6htVPaDRL/f6+BMmTEiteeuke2vEnzt3zqxfuXLFrGexYMECs/7555+b9Tx7+RMnTjTrx48fN+uPP/54zY99+PBhs/7OO+/UfN95a4Q+vqeaM/9vADw8zu2/VNVFyX9VBZ+IGocbflV9G8DpAsZCRAXK8jv/kyLyJxHZLCLtdRsRERWi1vD/CsACAIsAHAfw87RvFJF1ItIrIr01PhYR5aCm8Ktqv6qOqGoFwK8BLDa+t0dVu1W1u9ZBElH91RR+ERk7lev7AD6pz3CIqCjVtPpeAbAUwEwROQrgZwCWisgiAArgEIAf5zhGIsqBG35VXT3OzZtqeTARQVNT+psNb168NUd6+vTp5rFnz561B1cir49fpo6ODrN++rTdCJo5c2bNj/3yyy+b9UqlUvN9l81au8K75sTa++JGrkfhFX5EQTH8REEx/ERBMfxEQTH8REEx/ERBFb50t7WMdUtLi3m8NeX3ZphCWYb2dnvahdcaOn/+vFlfunSpWb/33ntTa1u3bjWP7enpMetZeFO8L168aNabm5vN+sjIiFm3prZbU9cBf/p6tXjmJwqK4ScKiuEnCorhJwqK4ScKiuEnCorhJwpKVLW4BxMxH8zbRjvvra7peitWrDDrO3bsMOuDg4OptXnz5pnHetOFPdZ1I1OmTMn02JMmTTLrly5dMutTp05NrXnXVlhT2y9fvoxKpWLvN5/gmZ8oKIafKCiGnygohp8oKIafKCiGnygohp8oqELn8zc1NZn9UasnDNjbRXvLfltLhgPZloHO877z5s1r37Jli1k/c+aMWd+7d29qzeulW8tbA9m2Vffm44vYrfKsc+q914ylXmtX8MxPFBTDTxQUw08UFMNPFBTDTxQUw08UFMNPFJQ7n19EugC8BOBbACoAelT1eRGZAWArgPkADgFYqapm01dE1Jqz783Xt+Zne33bqOv6e1uXf/bZZ2bdW2Nh//79Zv2pp55KrZ06dco8NuvW5Vavvq2tzTzWey1615V4109Y8/2zrrGhqnWbzz8M4Keq+jcA/hbAT0TkbgBPA9itqncC2J18TUQ3CTf8qnpcVT9IPr8AYB+A2wCsAHD18q8tAB7La5BEVH839Du/iMwH8B0AewDMVtXjwOg/EAA66j04IspP1df2i0gbgD8A2KCq571rn8cctw7AutqGR0R5qerMLyItGA3+b1V1e3Jzv4h0JvVOACfGO1ZVe1S1W1W76zFgIqoPN/wyeorfBGCfqv5iTGkngDXJ52sAvFb/4RFRXqp5278EwA8BfCwiHyW3bQTwHIBtIrIWwBEAP/DuSETcqZIWa0qv18rzHtdaShmwp3C2traax3pLMedpxowZZt2bNuttF/3iiy+a9T179ph1i9dm7Oiw/8w0MDCQWrtw4UJNY6pWltaytTR31vseyw2/qv4RQFpy/qEuoyCiwvEKP6KgGH6ioBh+oqAYfqKgGH6ioBh+oqAaaotuqk1nZ2dqbdu2beax99xzj1lfv369Wffu3zJnzhyz7vXivXpXV1dqbWhoyDy2v7/frGftxVvXMHjXN1jXnKhqXaf0EtH/Qww/UVAMP1FQDD9RUAw/UVAMP1FQDD9RUIVu0Q3YPUxvueRa7zfrfXvK3qL7iSeeSK098MADme77yy+/zHS8xdui21snwdPX15da89Yp8Hh9fG+dBGvpb+/1Uq9rc3jmJwqK4ScKiuEnCorhJwqK4ScKiuEnCorhJwqq8D5/ln67tQ13nn18T959/IceesisP/PMMzXft7VVNADMnz/frB84cMCsW/PivWsz8lxrwpvP7/XpBwcHzfq5c+dueExF45mfKCiGnygohp8oKIafKCiGnygohp8oKIafKCi3zy8iXQBeAvAtABUAPar6vIg8C+BHAE4m37pRVd/IMhhv/rbV9x0ZGcny0K681iEAgJaWFrO+ZMkSs+71yy0HDx40659++qlZ99a3t36m1px2wL6uoxrW83rlyhXzWO859V5vEydONOvW//ukSZPMY71rM6pVzatmGMBPVfUDEZkC4H0ReTOp/VJV/6UuIyGiQrnhV9XjAI4nn18QkX0Abst7YESUrxv6nV9E5gP4DoA9yU1PisifRGSziLSnHLNORHpFpDfTSImorqoOv4i0AfgDgA2qeh7ArwAsALAIo+8Mfj7ecarao6rdqtpdh/ESUZ1UFX4RacFo8H+rqtsBQFX7VXVEVSsAfg1gcX7DJKJ6c8MvIgJgE4B9qvqLMbeP3Rr2+wA+qf/wiCgv1fy1fwmAHwL4WEQ+Sm7bCGC1iCwCoAAOAfhx1sF40yyt1k3W5bOzLP3tteq8tpK33bPX8rLaaR9++KF57COPPGLWveW1PdZzM3peSWdtRV0N63n32soDAwNmffLkyWbdW9rb4rWO69V2ruav/X8EMN5PKVNPn4jKxSv8iIJi+ImCYviJgmL4iYJi+ImCYviJgpI8l0e+7sFE1OpZe33fLLz+p9drt65B8K4h8K4DyHoNgnX/Xr/Z63d79YsXL5p1q9fubZOdtc9vPW/etRN5PjZg/8yzLgWvqlUFiWd+oqAYfqKgGH6ioBh+oqAYfqKgGH6ioBh+oqCK7vOfBHB4zE0zAZwqbAA3plHH1qjjAji2WtVzbPNUdVY131ho+K97cJHeRl3br1HH1qjjAji2WpU1Nr7tJwqK4ScKquzw95T8+JZGHVujjgvg2GpVythK/Z2fiMpT9pmfiEpSSvhF5GER+UxEDojI02WMIY2IHBKRj0Xko7K3GEu2QTshIp+MuW2GiLwpIvuTj+Nuk1bS2J4VkS+T5+4jEbHXBc9vbF0i8p8isk9E9orIU8ntpT53xrhKed4Kf9svIs0A/hfAMgBHAbwHYLWq/k+hA0khIocAdKtq6T1hEfk7AIMAXlLVhclt/wzgtKo+l/zD2a6q/9ggY3sWwGDZOzcnG8p0jt1ZGsBjAJ5Aic+dMa6VKOF5K+PMvxjAAVU9qKpDAH4PYEUJ42h4qvo2gGt3zVgBYEvy+RaMvngKlzK2hqCqx1X1g+TzCwCu7ixd6nNnjKsUZYT/NgB9Y74+isba8lsB7BKR90VkXdmDGcfsZNv0q9und5Q8nmu5OzcX6ZqdpRvmuatlx+t6KyP84y0x1EgthyWqej+A5QB+kry9pepUtXNzUcbZWboh1Lrjdb2VEf6jALrGfD0XwLESxjEuVT2WfDwB4FU03u7D/Vc3SU0+nih5PH/WSDs3j7ezNBrguWukHa/LCP97AO4UkW+LSCuAVQB2ljCO64jI5OQPMRCRyQC+i8bbfXgngDXJ52sAvFbiWP5Co+zcnLazNEp+7hptx+tSLvJJWhn/CqAZwGZV/afCBzEOEbkdo2d7YHQT09+VOTYReQXAUozO+uoH8DMAOwBsA/BXAI4A+IGqFv6Ht5SxLcXoW9c/79x89Xfsgsf2AID/AvAxgKtL4W7E6O/XpT13xrhWo4TnjVf4EQXFK/yIgmL4iYJi+ImCYviJgmL4iYJi+ImCYviJgmL4iYL6PwgGkRfT0MgzAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.imshow(x_adv[0].cpu().detach().numpy().squeeze(), cmap='gray')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "tensor(1.3970, grad_fn=<MeanBackward1>)"
      ]
     },
     "execution_count": 28,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "(x_adv.cpu() - x_test[:10000]).view(10000, -1).norm(2, 1).mean()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 90,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "tensor(2.6813, grad_fn=<MeanBackward0>)"
      ]
     },
     "execution_count": 90,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "(x_adv.cpu() - x_test[:100]).view(100, -1).norm(2, 1).mean()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "x_adv = pickle.load(open('x_adv/x_adv_adv_cifar10_exp0.h5.p', 'rb'))\n",
    "# x_adv = pickle.load(open('x_adv/x_ba_cifar10_adv2_0.2_0.001.p', 'rb'))\n",
    "# x_adv = pickle.load(open('x_adv/x_adv_cifar10_resnet_exp2.h5.p', 'rb'))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "metadata": {},
   "outputs": [],
   "source": [
    "torchvision.utils.save_image(x_adv[:15], 'mnist_cw_basic.png', nrow=15, padding=0)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {
    "collapsed": true,
    "jupyter": {
     "outputs_hidden": true
    }
   },
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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2XNJHJR2ooY43MLPB4osYmdmgpPer91YfPiBpT3F7j6RHa6zlN/TKys1lK0ur5teu11a8ruUkn2Io40uS+iSNuPvnu17EAszsBs0d7aW5RUy/XWdtZvagpFs1d9XXaUmfk/SIpO9J2iTpRUkfcfeuf/FWUtutmnvr+uuVmy9/xu5ybX8k6SeSnpY0W2y+W3Ofr2t77RJ17VYNrxtn+AFBcYYfEBThB4Ii/EBQhB8IivADQRF+ICjCDwRF+IGg/h+twy1raQ3XEwAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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HA0cz+s7g6vGOc/dl7t7n7n3dxOvkiUjrVBV+M+tmNPg/dffbAdx9g7uPuHsZ+DEwv3nNFJFGS4bfRpfFvR5Y4+7fH3P7rDF3Ox14qvHNE5FmsdRUWDP7PPAr4ElGu/oALgfOZvQtvwMDwNcrfxwsNMNm+rF2Yp1NbpJUd1zwPJWmT4+PTU0nntwd1suD8TbXFm0fnpj22smS24enugqDadqp5dAnqlW+ki2+uap17Kv5a/+DjN+V3bY+fRGpn0b4iWRK4RfJlMIvkimFXyRTCr9IphR+kUztMUt3162OqavlrVvrO3edXfHt2yS7ufbUvvhOoSu/SKYUfpFMKfwimVL4RTKl8ItkSuEXyZTCL5Kp5Hz+hp7MbBPw6zE37Qu82bIG7J5ObVuntgvUtlo1sm2/5e77VXPHlob/Iyc3W+3ufW1rQKBT29ap7QK1rVbtapve9otkSuEXyVS7w7+szeePdGrbOrVdoLbVqi1ta+vv/CLSPu2+8otIm7Ql/Ga20MyeM7MXzezSdrShiJkNmNmTZva4ma1uc1uWm9lGM3tqzG0zzex+M3uh8nHcbdLa1LYrzez1ynP3uJl9uU1tm2Nm/2lma8zsaTP7k8rtbX3ugna15Xlr+dt+M+sCngcWAGuBR4Cz3f2ZljakgJkNAH3u3vY+YTP7XeA94EZ3P6py23eBze5+VeWFc293/7MOaduVwHvt3rm5sqHMrLE7SwOLga/RxucuaNeZtOF5a8eVfz7woru/7O47gVuARW1oR8dz9weAzR+6eRGwovL5CkZ/eFquoG0dwd3Xu/tjlc+3Art2lm7rcxe0qy3aEf7ZwGtjvl5LZ2357cB9ZvaomfW3uzHjOGDXzkiVj/u3uT0flty5uZU+tLN0xzx3tex43WjtCP94u/90UpfD8e5+DPAl4ILK21upTlU7N7fKODtLd4Rad7xutHaEfy0wZ8zXnwTWtaEd43L3dZWPG4E76Lzdhzfs2iS18nFjm9vz/zpp5+bxdpamA567Ttrxuh3hfwQ41MwOMrPJwFnAXW1ox0eYWW/lDzGYWS9wEp23+/BdwJLK50uAO9vYlg/olJ2bi3aWps3PXafteN2WQT6VrowfAF3Acnf/65Y3Yhxm9tuMXu1hdGXjm9rZNjO7GTiB0VlfG4ArgH8FbgXmAq8CZ7h7y//wVtC2E9jNnZub1LainaVX0cbnrpE7XjekPRrhJ5InjfATyZTCL5IphV8kUwq/SKYUfpFMKfwimVL4RTKl8Itk6v8AQA5RtkY56pAAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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f9hwoYDr12pxdN86v0NN2r3sw9/nTwj4/EQUx/EROMfxETjH8RE4x/EROMfxETjH8RE4VvFxXRWTsJZszRk858fkKgSW8kYlvnWpnp71taGmzhGO35gOQrP37XUJ9+sD3JDTHvBrrHST9utMUul4/iXLOHdEfj/xETjH8RE4x/EROMfxETjH8RE4x/EROMfxETgX7/CIyDcBTACYByANoVdVHReR+AJ8DcHaR9GWqai80H9pX1u4pJ+rlJ+jjA/bYpKEhsG+j143w1xXqpSOT4Hd4d/yc/32Pba93kD8V2N742vKnTtnbllForYTQz0NIop/VwLkVyNvfk0IVcpJPD4B7VHWLiIwE8JqIrI9qy1X1wZKMhIgqKhh+VW0H0B59fFxEdgCYUu6BEVF5ndfrRRGZAeByAJuim+4SkTdEZKWINMZs0yIibSLS1o3AabBEVDEFh19ERgD4LoC7VfUYgMcBXARgLvpeGTw00Haq2qqqzaranENgPT0iqpiCwi8iOfQF/2lVfR4AVPWgqvaqah7AEwDmlW+YRFRqwfBL35+aVwDYoaoP97t9cr+73QxgW+mHR0TlUshf+68GcDuArSLyenTbMgBLRGQuAAWwG8AdSQej3V1JH6Js0rz4tJz7DrW8qvl7kkTiryu0JHxw/0aLtEStvJBC/tr/Ywy8Snuinj4RpYtn+BE5xfATOcXwEznF8BM5xfATOcXwEzlVXVN3U8UN1T5+uQWnax8EeOQncorhJ3KK4SdyiuEncorhJ3KK4SdyiuEnckoSL219PjsTOQTgV/1uGgfgvYoN4PxU69iqdVwAx1asUo7tI6o6vpA7VjT8H9q5SJuqNqc2AEO1jq1axwVwbMVKa2x82U/kFMNP5FTa4W9Nef+Wah1btY4L4NiKlcrYUn3PT0TpSfvIT0QpSSX8IrJQRN4WkXdE5L40xhBHRHaLyFYReV1E2lIey0oR6RCRbf1uGyMi60VkZ/T/gMukpTS2+0VkX/TcvS4iN6Q0tmki8rKI7BCR7SLyl9HtqT53xrhSed4q/rJfRLIAfg7gOgB7AWwGsERV36zoQGKIyG4Azaqaek9YRK4FcALAU6o6J7rtXwAcVtUHol+cjar6t1UytvsBnEh75eZoQZnJ/VeWBnATgD9Bis+dMa5bkcLzlsaRfx6Ad1R1l6p2AXgOwOIUxlH1VHUjgMPn3LwYwKro41Xo++GpuJixVQVVbVfVLdHHxwGcXVk61efOGFcq0gj/FAB7+n2+F9W15LcCWCcir4lIS9qDGcDEaNn0s8unT0h5POcKrtxcSeesLF01z10xK16XWhrhH2j1n2pqOVytqr8NYBGAL0Qvb6kwBa3cXCkDrCxdFYpd8brU0gj/XgDT+n0+FcD+FMYxIFXdH/3fAWA1qm/14YNnF0mN/u9IeTy/UU0rNw+0sjSq4LmrphWv0wj/ZgAzRaRJRGoB3AZgTQrj+BARGR79IQYiMhzA9ai+1YfXAFgafbwUwAspjuUDqmXl5riVpZHyc1dtK16ncpJP1Mp4BEAWwEpV/UrFBzEAEfko+o72QN/Mxs+kOTYReRbAfPRd9XUQwJcB/CeA7wCYDuBdALeoasX/8BYztvnoe+n6m5Wbz77HrvDYPgngFQBbAeSjm5eh7/11as+dMa4lSOF54xl+RE7xDD8ipxh+IqcYfiKnGH4ipxh+IqcYfiKnGH4ipxh+Iqf+D9k7ktMvpbS2AAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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0XKPvDG4bbzt3X+vuve7e260pDWgygEaoKfxm1q3R4N/l7vdLkrvvdvcRd69I+o6k+C9HANpKMvxmZpK+K+kFd799zP0LxjzsKknPNb55AJqllr/2XyjpU5K2mNkz1fvWSLrWzJZrdIXo7ZKub0oL0VRTH54d1r+04LKwfsbjxdOGo73V8tf+n0karzOXPn1gEuMKPyBThB/IFOEHMkX4gUwRfiBThB/IlLnHSzQ30myb6xfYypbtD8jNZt+kg76vpnHWnPmBTBF+IFOEH8gU4QcyRfiBTBF+IFOEH8hUS/v5zWyPpFfH3HW6pL0ta8DJade2tWu7JNo2UY1s21nu/u5aHtjS8J+wc7N+d+8trQGBdm1bu7ZLom0TVVbbeNsPZIrwA5kqO/xrS95/pF3b1q7tkmjbRJXStlI/8wMoT9lnfgAlKSX8Znapmb1oZi+b2U1ltKGImW03sy1m9oyZ9ZfclnVmNmBmz425b66ZbTSzl6r/j7tMWkltu8XMXq8eu2fM7A9KatsiM3vUzF4ws+fN7LPV+0s9dkG7SjluLX/bb2adkrZKukTSDklPSLrW3X/V0oYUMLPtknrdvfQ+YTP7qKTDku5093Or931V0j53v7X6i3OOu/9Fm7TtFkmHy165ubqgzIKxK0tLulLSp1XisQvatUolHLcyzvwrJL3s7tvc/bikH0i6ooR2tD13f0zSvnfcfYWk9dXb6zX64mm5gra1BXff5e5PVW8fkvTmytKlHrugXaUoI/wLJf1mzNc71F5LfrukH5vZk2bWV3ZjxjG/umz6m8unzyu5Pe+UXLm5ld6xsnTbHLuJrHjdaGWEf7wphtqpy+FCd/9dSZ+QdEP17S1qU9PKza0yzsrSbWGiK143Whnh3yFp0Zivz5S0s4R2jMvdd1b/H5C0Qe23+vDuNxdJrf4/UHJ73tJOKzePt7K02uDYtdOK12WE/wlJy8zsbDPrkXSNpAdLaMcJzGxG9Q8xMrMZkj6u9lt9+EFJq6u3V0t6oMS2vE27rNxctLK0Sj527bbidSkX+VS7Mr4hqVPSOnf/25Y3YhxmtkSjZ3tpdBHTu8tsm5ndI+kijY762i3pZkn/KuleSe+R9Jqkq9295X94K2jbRRp96/rWys1vfsZucds+IumnkrZIqlTvXqPRz9elHbugXdeqhOPGFX5AprjCD8gU4QcyRfiBTBF+IFOEH8gU4QcyRfiBTBF+IFP/B4u3KQ2zmglOAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "for i in range(20):\n",
    "    plt.imshow(x_adv[i].view(28, 28).cpu().detach().numpy())\n",
    "    plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Using TensorFlow backend.\n"
     ]
    }
   ],
   "source": [
    "# Subsamples dataset and reduces to two classes (1 and 7)\n",
    "# Code adapted from https://github.com/yangarbiter/adversarial-nonparametrics\n",
    "\n",
    "num_samples = 2200\n",
    "num_val = 300 # can pick anything > 0, does not really get used here\n",
    "\n",
    "from keras.datasets import mnist\n",
    "(X, y), (_, _) = mnist.load_data()\n",
    "np.random.seed(2019)\n",
    "idx1 = np.random.choice(\n",
    "    np.where(y == 1)[0], num_samples // 2, replace=False)\n",
    "idx2 = np.random.choice(\n",
    "    np.where(y == 7)[0], num_samples // 2, replace=False)\n",
    "y[idx1] = 0\n",
    "y[idx2] = 1\n",
    "X = np.vstack((X[idx1], X[idx2])).astype(np.float32) / 255.\n",
    "y = np.concatenate((y[idx1], y[idx2]))\n",
    "\n",
    "idxs = np.arange(num_samples)\n",
    "np.random.shuffle(idxs)\n",
    "x_train_sub = torch.tensor(X[idxs[:-200]])\n",
    "x_test_sub = torch.tensor(X[idxs[-200:]])\n",
    "y_train_sub = torch.tensor(y[idxs[:-200]])\n",
    "y_test_sub = torch.tensor(y[idxs[-200:]])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [],
   "source": [
    "net_knn = KNNModel()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [],
   "source": [
    "layers = ['identity']\n",
    "knn = DKNNL2(net_knn, x_train_sub, y_train_sub, \n",
    "             x_test_sub, y_test_sub, layers, \n",
    "             k=1, num_classes=2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0.99\n"
     ]
    }
   ],
   "source": [
    "with torch.no_grad():\n",
    "    y_pred = knn.classify(x_test_sub)\n",
    "    ind = np.where(y_pred.argmax(1) == y_test_sub.numpy())[0]\n",
    "    print((y_pred.argmax(1) == y_test_sub.numpy()).sum() / y_test_sub.size(0))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 56,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "    step: 0; loss: 0.454; l2dist: 0.000\n",
      "tensor(2., device='cuda:0')\n",
      "    step: 100; loss: 0.451; l2dist: 0.053\n",
      "tensor(2., device='cuda:0')\n",
      "    step: 200; loss: 0.451; l2dist: 0.053\n",
      "tensor(2., device='cuda:0')\n",
      "    step: 300; loss: 0.451; l2dist: 0.053\n",
      "tensor(2., device='cuda:0')\n",
      "    step: 400; loss: 0.451; l2dist: 0.053\n",
      "tensor(2., device='cuda:0')\n",
      "    step: 500; loss: 0.451; l2dist: 0.053\n",
      "tensor(2., device='cuda:0')\n",
      "    step: 600; loss: 0.451; l2dist: 0.053\n",
      "tensor(2., device='cuda:0')\n",
      "    step: 700; loss: 0.451; l2dist: 0.053\n",
      "tensor(2., device='cuda:0')\n",
      "    step: 800; loss: 0.451; l2dist: 0.053\n",
      "tensor(2., device='cuda:0')\n",
      "    step: 900; loss: 0.451; l2dist: 0.053\n",
      "tensor(2., device='cuda:0')\n",
      "tensor(2., device='cuda:0')\n",
      "binary step: 0; number of successful adv: 2/200\n",
      "    step: 0; loss: 4.519; l2dist: 0.000\n",
      "tensor(2., device='cuda:0')\n",
      "    step: 100; loss: 4.229; l2dist: 0.483\n",
      "tensor(3., device='cuda:0')\n",
      "    step: 200; loss: 4.229; l2dist: 0.484\n",
      "tensor(3., device='cuda:0')\n",
      "    step: 300; loss: 4.124; l2dist: 0.577\n",
      "tensor(3., device='cuda:0')\n",
      "    step: 400; loss: 4.112; l2dist: 0.587\n",
      "tensor(3., device='cuda:0')\n",
      "    step: 500; loss: 4.110; l2dist: 0.589\n",
      "tensor(3., device='cuda:0')\n",
      "    step: 600; loss: 4.109; l2dist: 0.590\n",
      "tensor(3., device='cuda:0')\n",
      "    step: 700; loss: 4.109; l2dist: 0.590\n",
      "tensor(3., device='cuda:0')\n",
      "    step: 800; loss: 4.109; l2dist: 0.590\n",
      "tensor(3., device='cuda:0')\n",
      "    step: 900; loss: 4.108; l2dist: 0.590\n",
      "tensor(3., device='cuda:0')\n",
      "tensor(3., device='cuda:0')\n",
      "binary step: 1; number of successful adv: 3/200\n",
      "    step: 0; loss: 45.074; l2dist: 0.000\n",
      "tensor(2., device='cuda:0')\n",
      "    step: 100; loss: 23.548; l2dist: 3.334\n",
      "tensor(150., device='cuda:0')\n",
      "    step: 200; loss: 22.551; l2dist: 3.233\n",
      "tensor(143., device='cuda:0')\n",
      "    step: 300; loss: 22.542; l2dist: 3.233\n",
      "tensor(143., device='cuda:0')\n",
      "    step: 400; loss: 22.541; l2dist: 3.233\n",
      "tensor(143., device='cuda:0')\n",
      "    step: 500; loss: 22.540; l2dist: 3.233\n",
      "tensor(143., device='cuda:0')\n",
      "    step: 600; loss: 22.540; l2dist: 3.233\n",
      "tensor(143., device='cuda:0')\n",
      "    step: 700; loss: 22.540; l2dist: 3.233\n",
      "tensor(143., device='cuda:0')\n",
      "    step: 800; loss: 22.540; l2dist: 3.233\n",
      "tensor(143., device='cuda:0')\n",
      "    step: 900; loss: 22.540; l2dist: 3.233\n",
      "tensor(143., device='cuda:0')\n",
      "tensor(143., device='cuda:0')\n",
      "binary step: 2; number of successful adv: 159/200\n",
      "    step: 0; loss: 137.673; l2dist: 0.000\n",
      "tensor(2., device='cuda:0')\n",
      "    step: 100; loss: 24.829; l2dist: 3.064\n",
      "tensor(64., device='cuda:0')\n",
      "    step: 200; loss: 22.707; l2dist: 3.297\n",
      "tensor(83., device='cuda:0')\n",
      "    step: 300; loss: 22.623; l2dist: 3.282\n",
      "tensor(82., device='cuda:0')\n",
      "    step: 400; loss: 22.621; l2dist: 3.283\n",
      "tensor(82., device='cuda:0')\n",
      "    step: 500; loss: 22.621; l2dist: 3.283\n",
      "tensor(82., device='cuda:0')\n",
      "    step: 600; loss: 22.620; l2dist: 3.283\n",
      "tensor(82., device='cuda:0')\n",
      "    step: 700; loss: 22.620; l2dist: 3.283\n",
      "tensor(82., device='cuda:0')\n",
      "    step: 800; loss: 22.620; l2dist: 3.283\n",
      "tensor(82., device='cuda:0')\n",
      "    step: 900; loss: 22.620; l2dist: 3.283\n",
      "tensor(82., device='cuda:0')\n",
      "tensor(82., device='cuda:0')\n",
      "binary step: 3; number of successful adv: 200/200\n",
      "    step: 0; loss: 89.472; l2dist: 0.000\n",
      "tensor(2., device='cuda:0')\n",
      "    step: 100; loss: 25.279; l2dist: 3.561\n",
      "tensor(104., device='cuda:0')\n",
      "    step: 200; loss: 23.782; l2dist: 3.424\n",
      "tensor(106., device='cuda:0')\n",
      "    step: 300; loss: 23.770; l2dist: 3.423\n",
      "tensor(106., device='cuda:0')\n",
      "    step: 400; loss: 23.768; l2dist: 3.423\n",
      "tensor(106., device='cuda:0')\n",
      "    step: 500; loss: 23.768; l2dist: 3.423\n",
      "tensor(106., device='cuda:0')\n",
      "    step: 600; loss: 23.768; l2dist: 3.423\n",
      "tensor(106., device='cuda:0')\n",
      "    step: 700; loss: 23.768; l2dist: 3.423\n",
      "tensor(106., device='cuda:0')\n",
      "    step: 800; loss: 23.768; l2dist: 3.423\n",
      "tensor(106., device='cuda:0')\n",
      "    step: 900; loss: 23.768; l2dist: 3.423\n",
      "tensor(106., device='cuda:0')\n",
      "tensor(106., device='cuda:0')\n",
      "binary step: 4; number of successful adv: 200/200\n",
      "    step: 0; loss: 63.769; l2dist: 0.000\n",
      "tensor(2., device='cuda:0')\n",
      "    step: 100; loss: 24.480; l2dist: 3.374\n",
      "tensor(109., device='cuda:0')\n",
      "    step: 200; loss: 23.155; l2dist: 3.325\n",
      "tensor(119., device='cuda:0')\n",
      "    step: 300; loss: 23.144; l2dist: 3.327\n",
      "tensor(120., device='cuda:0')\n",
      "    step: 400; loss: 23.142; l2dist: 3.327\n",
      "tensor(120., device='cuda:0')\n",
      "    step: 500; loss: 23.142; l2dist: 3.327\n",
      "tensor(120., device='cuda:0')\n",
      "    step: 600; loss: 23.142; l2dist: 3.327\n",
      "tensor(120., device='cuda:0')\n",
      "    step: 700; loss: 23.142; l2dist: 3.327\n",
      "tensor(120., device='cuda:0')\n",
      "    step: 800; loss: 23.142; l2dist: 3.327\n",
      "tensor(120., device='cuda:0')\n",
      "    step: 900; loss: 23.142; l2dist: 3.327\n",
      "tensor(120., device='cuda:0')\n",
      "tensor(120., device='cuda:0')\n",
      "binary step: 5; number of successful adv: 200/200\n",
      "    step: 0; loss: 50.559; l2dist: 0.000\n",
      "tensor(2., device='cuda:0')\n",
      "    step: 100; loss: 23.481; l2dist: 3.178\n",
      "tensor(113., device='cuda:0')\n",
      "    step: 200; loss: 22.225; l2dist: 3.188\n",
      "tensor(119., device='cuda:0')\n",
      "    step: 300; loss: 22.214; l2dist: 3.188\n",
      "tensor(119., device='cuda:0')\n",
      "    step: 400; loss: 22.212; l2dist: 3.189\n",
      "tensor(120., device='cuda:0')\n",
      "    step: 500; loss: 22.212; l2dist: 3.189\n",
      "tensor(121., device='cuda:0')\n",
      "    step: 600; loss: 22.212; l2dist: 3.189\n",
      "tensor(121., device='cuda:0')\n",
      "    step: 700; loss: 22.212; l2dist: 3.189\n",
      "tensor(121., device='cuda:0')\n",
      "    step: 800; loss: 22.212; l2dist: 3.189\n",
      "tensor(121., device='cuda:0')\n",
      "    step: 900; loss: 22.212; l2dist: 3.189\n",
      "tensor(121., device='cuda:0')\n",
      "tensor(121., device='cuda:0')\n",
      "binary step: 6; number of successful adv: 200/200\n",
      "    step: 0; loss: 43.985; l2dist: 0.000\n",
      "tensor(2., device='cuda:0')\n",
      "    step: 100; loss: 22.621; l2dist: 3.044\n",
      "tensor(113., device='cuda:0')\n",
      "    step: 200; loss: 21.433; l2dist: 3.069\n",
      "tensor(129., device='cuda:0')\n",
      "    step: 300; loss: 21.422; l2dist: 3.069\n",
      "tensor(129., device='cuda:0')\n",
      "    step: 400; loss: 21.421; l2dist: 3.070\n",
      "tensor(129., device='cuda:0')\n",
      "    step: 500; loss: 21.420; l2dist: 3.070\n",
      "tensor(129., device='cuda:0')\n",
      "    step: 600; loss: 21.420; l2dist: 3.070\n",
      "tensor(129., device='cuda:0')\n",
      "    step: 700; loss: 21.420; l2dist: 3.070\n",
      "tensor(129., device='cuda:0')\n",
      "    step: 800; loss: 21.420; l2dist: 3.070\n",
      "tensor(130., device='cuda:0')\n",
      "    step: 900; loss: 21.420; l2dist: 3.070\n",
      "tensor(130., device='cuda:0')\n",
      "tensor(130., device='cuda:0')\n",
      "binary step: 7; number of successful adv: 200/200\n",
      "    step: 0; loss: 40.633; l2dist: 0.000\n",
      "tensor(2., device='cuda:0')\n",
      "    step: 100; loss: 22.046; l2dist: 2.970\n",
      "tensor(104., device='cuda:0')\n",
      "    step: 200; loss: 20.859; l2dist: 2.983\n",
      "tensor(139., device='cuda:0')\n",
      "    step: 300; loss: 20.846; l2dist: 2.983\n",
      "tensor(143., device='cuda:0')\n",
      "    step: 400; loss: 20.845; l2dist: 2.983\n",
      "tensor(143., device='cuda:0')\n",
      "    step: 500; loss: 20.845; l2dist: 2.984\n",
      "tensor(143., device='cuda:0')\n",
      "    step: 600; loss: 20.845; l2dist: 2.984\n",
      "tensor(143., device='cuda:0')\n",
      "    step: 700; loss: 20.845; l2dist: 2.984\n",
      "tensor(143., device='cuda:0')\n",
      "    step: 800; loss: 20.845; l2dist: 2.984\n",
      "tensor(143., device='cuda:0')\n",
      "    step: 900; loss: 20.845; l2dist: 2.984\n",
      "tensor(142., device='cuda:0')\n",
      "tensor(142., device='cuda:0')\n",
      "binary step: 8; number of successful adv: 200/200\n",
      "    step: 0; loss: 38.943; l2dist: 0.000\n",
      "tensor(2., device='cuda:0')\n",
      "    step: 100; loss: 21.720; l2dist: 2.931\n",
      "tensor(86., device='cuda:0')\n",
      "    step: 200; loss: 20.507; l2dist: 2.930\n",
      "tensor(105., device='cuda:0')\n",
      "    step: 300; loss: 20.493; l2dist: 2.931\n",
      "tensor(109., device='cuda:0')\n",
      "    step: 400; loss: 20.492; l2dist: 2.931\n",
      "tensor(111., device='cuda:0')\n",
      "    step: 500; loss: 20.492; l2dist: 2.931\n",
      "tensor(110., device='cuda:0')\n",
      "    step: 600; loss: 20.491; l2dist: 2.931\n",
      "tensor(110., device='cuda:0')\n",
      "    step: 700; loss: 20.491; l2dist: 2.931\n",
      "tensor(110., device='cuda:0')\n",
      "    step: 800; loss: 20.491; l2dist: 2.931\n",
      "tensor(110., device='cuda:0')\n",
      "    step: 900; loss: 20.491; l2dist: 2.931\n",
      "tensor(110., device='cuda:0')\n",
      "tensor(110., device='cuda:0')\n",
      "binary step: 9; number of successful adv: 200/200\n",
      "    step: 0; loss: 38.568; l2dist: 0.000\n",
      "tensor(2., device='cuda:0')\n",
      "    step: 100; loss: 21.648; l2dist: 2.925\n",
      "tensor(88., device='cuda:0')\n",
      "    step: 200; loss: 20.419; l2dist: 2.918\n",
      "tensor(96., device='cuda:0')\n",
      "    step: 300; loss: 20.406; l2dist: 2.918\n",
      "tensor(104., device='cuda:0')\n",
      "    step: 400; loss: 20.404; l2dist: 2.919\n",
      "tensor(111., device='cuda:0')\n",
      "    step: 500; loss: 20.404; l2dist: 2.919\n",
      "tensor(110., device='cuda:0')\n",
      "    step: 600; loss: 20.404; l2dist: 2.919\n",
      "tensor(110., device='cuda:0')\n",
      "    step: 700; loss: 20.404; l2dist: 2.919\n",
      "tensor(110., device='cuda:0')\n",
      "    step: 800; loss: 20.404; l2dist: 2.919\n",
      "tensor(110., device='cuda:0')\n",
      "    step: 900; loss: 20.404; l2dist: 2.919\n",
      "tensor(109., device='cuda:0')\n",
      "tensor(109., device='cuda:0')\n",
      "binary step: 10; number of successful adv: 200/200\n",
      "    step: 0; loss: 38.358; l2dist: 0.000\n",
      "tensor(2., device='cuda:0')\n",
      "    step: 100; loss: 21.613; l2dist: 2.921\n",
      "tensor(76., device='cuda:0')\n",
      "    step: 200; loss: 20.370; l2dist: 2.911\n",
      "tensor(98., device='cuda:0')\n",
      "    step: 300; loss: 20.357; l2dist: 2.911\n",
      "tensor(105., device='cuda:0')\n",
      "    step: 400; loss: 20.355; l2dist: 2.912\n",
      "tensor(110., device='cuda:0')\n",
      "    step: 500; loss: 20.355; l2dist: 2.912\n",
      "tensor(111., device='cuda:0')\n",
      "    step: 600; loss: 20.355; l2dist: 2.912\n",
      "tensor(106., device='cuda:0')\n",
      "    step: 700; loss: 20.355; l2dist: 2.912\n",
      "tensor(107., device='cuda:0')\n",
      "    step: 800; loss: 20.355; l2dist: 2.912\n",
      "tensor(107., device='cuda:0')\n",
      "    step: 900; loss: 20.355; l2dist: 2.912\n",
      "tensor(106., device='cuda:0')\n",
      "tensor(108., device='cuda:0')\n",
      "binary step: 11; number of successful adv: 200/200\n",
      "    step: 0; loss: 38.345; l2dist: 0.000\n",
      "tensor(2., device='cuda:0')\n",
      "    step: 100; loss: 21.610; l2dist: 2.921\n",
      "tensor(77., device='cuda:0')\n",
      "    step: 200; loss: 20.367; l2dist: 2.911\n",
      "tensor(92., device='cuda:0')\n",
      "    step: 300; loss: 20.353; l2dist: 2.911\n",
      "tensor(95., device='cuda:0')\n",
      "    step: 400; loss: 20.352; l2dist: 2.911\n",
      "tensor(96., device='cuda:0')\n",
      "    step: 500; loss: 20.352; l2dist: 2.911\n",
      "tensor(98., device='cuda:0')\n",
      "    step: 600; loss: 20.352; l2dist: 2.911\n",
      "tensor(98., device='cuda:0')\n",
      "    step: 700; loss: 20.352; l2dist: 2.911\n",
      "tensor(98., device='cuda:0')\n",
      "    step: 800; loss: 20.352; l2dist: 2.911\n",
      "tensor(97., device='cuda:0')\n",
      "    step: 900; loss: 20.352; l2dist: 2.911\n",
      "tensor(97., device='cuda:0')\n",
      "tensor(98., device='cuda:0')\n",
      "binary step: 12; number of successful adv: 200/200\n",
      "    step: 0; loss: 38.330; l2dist: 0.000\n",
      "tensor(2., device='cuda:0')\n",
      "    step: 100; loss: 21.607; l2dist: 2.920\n",
      "tensor(77., device='cuda:0')\n",
      "    step: 200; loss: 20.364; l2dist: 2.910\n",
      "tensor(93., device='cuda:0')\n",
      "    step: 300; loss: 20.351; l2dist: 2.910\n",
      "tensor(92., device='cuda:0')\n",
      "    step: 400; loss: 20.349; l2dist: 2.911\n",
      "tensor(107., device='cuda:0')\n",
      "    step: 500; loss: 20.349; l2dist: 2.911\n",
      "tensor(109., device='cuda:0')\n",
      "    step: 600; loss: 20.349; l2dist: 2.911\n",
      "tensor(109., device='cuda:0')\n",
      "    step: 700; loss: 20.349; l2dist: 2.911\n",
      "tensor(107., device='cuda:0')\n",
      "    step: 800; loss: 20.349; l2dist: 2.911\n",
      "tensor(109., device='cuda:0')\n",
      "    step: 900; loss: 20.349; l2dist: 2.911\n",
      "tensor(107., device='cuda:0')\n",
      "tensor(109., device='cuda:0')\n",
      "binary step: 13; number of successful adv: 200/200\n",
      "    step: 0; loss: 38.384; l2dist: 0.000\n",
      "tensor(2., device='cuda:0')\n",
      "    step: 100; loss: 21.618; l2dist: 2.922\n",
      "tensor(77., device='cuda:0')\n",
      "    step: 200; loss: 20.377; l2dist: 2.912\n",
      "tensor(117., device='cuda:0')\n",
      "    step: 300; loss: 20.364; l2dist: 2.912\n",
      "tensor(147., device='cuda:0')\n",
      "    step: 400; loss: 20.362; l2dist: 2.913\n",
      "tensor(180., device='cuda:0')\n",
      "    step: 500; loss: 20.362; l2dist: 2.913\n",
      "tensor(190., device='cuda:0')\n",
      "    step: 600; loss: 20.362; l2dist: 2.913\n",
      "tensor(196., device='cuda:0')\n",
      "    step: 700; loss: 20.362; l2dist: 2.913\n",
      "tensor(197., device='cuda:0')\n",
      "    step: 800; loss: 20.362; l2dist: 2.913\n",
      "tensor(199., device='cuda:0')\n",
      "    step: 900; loss: 20.362; l2dist: 2.913\n",
      "tensor(196., device='cuda:0')\n",
      "tensor(200., device='cuda:0')\n",
      "binary step: 14; number of successful adv: 200/200\n"
     ]
    }
   ],
   "source": [
    "# Attack for L2 DkNN\n",
    "\n",
    "# attack = DKNNL2Attack()\n",
    "# attack = DKNNLinfAttack()\n",
    "from lib.dknn_attack_exp import DKNNExpAttack\n",
    "attack = DKNNExpAttack()\n",
    "\n",
    "def attack_batch(x, y, batch_size):\n",
    "    x_adv = torch.zeros_like(x)\n",
    "    total_num = x.size(0)\n",
    "    num_batches = total_num // batch_size\n",
    "    for i in range(num_batches):\n",
    "        begin = i * batch_size\n",
    "        end = (i + 1) * batch_size\n",
    "        x_adv[begin:end] = attack(\n",
    "            knn, x[begin:end], y[begin:end],\n",
    "            guide_layer=layers[0], m=1, binary_search_steps=15,\n",
    "            max_iterations=1000, learning_rate=1e-1, guide_mode=1,\n",
    "            initial_const=1e-2, abort_early=True, random_start=False)\n",
    "    return x_adv\n",
    "\n",
    "num = 200\n",
    "x_adv = attack_batch(x_test_sub[:num].cuda(), y_test_sub[:num], 200)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 57,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0.0\n"
     ]
    }
   ],
   "source": [
    "with torch.no_grad():\n",
    "    y_pred = knn.classify(x_adv)\n",
    "    ind = np.where(y_pred.argmax(1) == y_test_sub[:num].numpy())[0]\n",
    "    print(len(ind) / x_adv.size(0))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 57,
   "metadata": {
    "collapsed": true,
    "jupyter": {
     "outputs_hidden": true
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
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       "          39.263206 , 39.646885 , 40.205055 , 41.575085 , 42.94713  ,\n",
       "          43.37243  , 43.766434 , 43.78415  , 43.83922  , 44.964108 ,\n",
       "          45.238632 , 45.84772  , 46.177193 , 46.570705 , 47.616356 ,\n",
       "          48.474632 , 48.639538 , 49.064854 , 49.434353 , 49.726173 ,\n",
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       "          56.536583 , 56.65898  , 57.060356 , 57.667496 , 57.79631  ],\n",
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       "          57.91779  , 57.96455  , 57.97461  , 58.210304 , 58.25958  ,\n",
       "          58.440247 , 58.79622  , 59.020744 , 59.247807 , 59.380466 ,\n",
       "          59.597523 , 59.599342 , 59.69076  , 59.79572  , 59.87937  ,\n",
       "          60.026512 , 60.225082 , 60.260002 , 60.5457   , 60.626175 ,\n",
       "          60.75285  , 61.154114 , 61.19094  , 61.242798 , 61.63345  ,\n",
       "          61.740517 , 61.76284  , 61.8308   , 61.89235  , 62.10685  ],\n",
       "         [19.91514  , 25.985928 , 28.36915  , 32.733856 , 33.126873 ,\n",
       "          33.25579  , 37.9234   , 37.961308 , 38.183407 , 38.437225 ,\n",
       "          38.50931  , 38.757324 , 39.287598 , 39.44349  , 39.554108 ,\n",
       "          39.659176 , 40.03895  , 40.076508 , 40.114357 , 40.255165 ,\n",
       "          40.44038  , 40.627373 , 41.172073 , 41.175865 , 42.084347 ,\n",
       "          42.240665 , 42.422882 , 42.87503  , 43.97762  , 44.01418  ,\n",
       "          44.025864 , 44.11194  , 44.130047 , 44.303757 , 44.396103 ,\n",
       "          44.429054 , 44.59611  , 44.817177 , 44.853745 , 45.0766   ,\n",
       "          45.336643 , 45.38301  , 45.388237 , 45.480927 , 46.123707 ,\n",
       "          46.268707 , 46.340454 , 46.37232  , 46.60743  , 46.81138  ],\n",
       "         [19.958614 , 20.081585 , 20.793495 , 20.808872 , 21.196247 ,\n",
       "          22.615177 , 23.157032 , 23.400276 , 23.633005 , 23.663021 ,\n",
       "          24.052567 , 24.317398 , 25.757893 , 26.278433 , 26.337791 ,\n",
       "          26.999996 , 27.223454 , 27.290287 , 27.474844 , 27.968103 ,\n",
       "          27.989834 , 28.311232 , 28.721504 , 28.885843 , 29.009598 ,\n",
       "          29.64973  , 30.298805 , 30.35692  , 30.594921 , 30.931267 ,\n",
       "          30.934963 , 31.59043  , 31.98537  , 32.36953  , 32.400967 ,\n",
       "          32.59935  , 32.92143  , 33.108437 , 33.580482 , 33.639244 ,\n",
       "          33.98914  , 34.166504 , 34.270817 , 34.536285 , 34.63981  ,\n",
       "          35.15277  , 35.233036 , 35.402843 , 35.772842 , 36.595306 ]],\n",
       "        dtype=float32),\n",
       "  array([[1751, 1516,  650, 1494, 1364,  804,  395,  484,  655, 1037, 1788,\n",
       "          1175, 1711, 1619, 1987, 1833,  175,  557,  418, 1013,  410,  644,\n",
       "          1359, 1151,  589, 1646,  922,  456, 1944,   26, 1848,  896,   19,\n",
       "          1111, 1000, 1083,  697, 1935,   91, 1636, 1748, 1798,  513, 1391,\n",
       "          1697, 1746,  601, 1617,  108,   74],\n",
       "         [1926,  727, 1207,  831,  144, 1333,  959, 1145, 1916,  929, 1764,\n",
       "           406, 1572,  220,  288, 1816, 1177, 1101,  547,  165, 1462,  605,\n",
       "           378,  821,  565, 1062, 1473, 1773,   77, 1203,  835,  236, 1369,\n",
       "          1797, 1978, 1336,  665,   61,   49,  214,  808, 1296,  404, 1244,\n",
       "          1385,  567,  572, 1645, 1847, 1139],\n",
       "         [1931, 1836, 1269,   73,  362,  995, 1624,   98,  169, 1640,  162,\n",
       "          1754, 1629, 1444, 1774,  499,  916,  936, 1695, 1603, 1674, 1122,\n",
       "            97,  909, 1214, 1382, 1571, 1059,  223, 1478,  806, 1760, 1591,\n",
       "          1555, 1660, 1260, 1281, 1747,  754,  530,  289,  376,  187,  780,\n",
       "          1158, 1487, 1222,  344, 1010, 1610],\n",
       "         [ 619, 1881,  950, 1002, 1285, 1659, 1731, 1682, 1932, 1532, 1720,\n",
       "           563,  138, 1658,   71, 1520,  613, 1637, 1380, 1676, 1220,  371,\n",
       "          1182,  241, 1398, 1338,  651, 1314,  379,  859, 1616, 1968, 1270,\n",
       "           742, 1199,    5, 1267, 1102,  423, 1490,  137,  969,  991,  680,\n",
       "          1531, 1129, 1242, 1598,  735, 1325],\n",
       "         [1547, 1092,  218, 1752, 1938, 1004, 1549,  127,  312, 1021, 1357,\n",
       "            53, 1905,  653, 1913,  348,  486,  457, 1814, 1411, 1464,  199,\n",
       "          1627,  667,  668, 1217, 1468,  117, 1884,  156,   39, 1898, 1652,\n",
       "          1903, 1409,   90, 1890,  129, 1596,  675, 1433,  222,  544, 1866,\n",
       "          1909,  292, 1219,  809,  528, 1793],\n",
       "         [ 368,  601, 1573,  323,  468, 1052,  537, 1872,  277,  668, 1991,\n",
       "           926,  887,  518,  348, 1163,  117,   53, 1302, 1097,  431, 1696,\n",
       "            95,  309,  854,   63,  385,  127,  341, 1262,  723,  699, 1755,\n",
       "          1884,  457, 1620,  177,  648, 1818,  787, 1225, 1071, 1434,  544,\n",
       "           487, 1547, 1889,  190, 1003, 1358],\n",
       "         [1817, 1475,  346,  654, 1297, 1576, 1405,  310,  453,  258, 1312,\n",
       "          1632,  932, 1882, 1246,  790, 1726,  329,  244,  260,  426, 1243,\n",
       "          1706,  354,  524,  811, 1519,  209, 1113,  212,  903,  303,  367,\n",
       "          1744,  488,  753,  939,   38,  512, 1362, 1489, 1825, 1623, 1408,\n",
       "          1032, 1437,  905, 1667, 1244, 1524],\n",
       "         [ 825,  968,  803, 1045,   95,  350,  886,  801,  444, 1034,  854,\n",
       "          1239,  723,  207,  317,  543, 1694, 1679,  947, 1493, 1317, 1672,\n",
       "           964, 1617, 1302, 1595,  937,   39, 1998,  545, 1181,  158, 1809,\n",
       "           380, 1300, 1125,  799, 1470,  938,  422, 1375,  254, 1097, 1339,\n",
       "           225,  896, 1903,  105, 1004,  271],\n",
       "         [ 275, 1331, 1136, 1301, 1334,  419,  136,  402, 1757,  851, 1527,\n",
       "          1567, 1689,  176,  902,  566, 1091,  148,  985, 1386,  500, 1251,\n",
       "          1812, 1876,  987,   23,  933,  199, 1542,  364, 1999, 1426,  473,\n",
       "           217,  990,  594,  756,  843,   12, 1482,  873,  962,  415, 1474,\n",
       "           155,  580,  392, 1038,  455, 1979],\n",
       "         [ 840, 1802, 1770,  764,  357, 1545, 1372, 1612,  910, 1050, 1579,\n",
       "          1460,  313, 1230,   87, 1483,  114,  989, 1562,  118, 1288, 1985,\n",
       "           216,  414,  556,  521,  733, 1824, 1446, 1961,  807, 1785, 1653,\n",
       "          1870,  412, 1955, 1974,  179, 1227,  862,  299, 1976,  112,  578,\n",
       "           335,  194, 1614,   42,  180, 1026]]))]"
      ]
     },
     "execution_count": 57,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# knn.get_neighbors(x_test_sub[:10], k=50)\n",
    "# for idx in knn.get_neighbors(x_test_sub[:10], k=50)[0][1]:\n",
    "#     print(y_train_sub[idx])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "tensor([1, 0, 0], dtype=torch.uint8)"
      ]
     },
     "execution_count": 34,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "y_test_sub[:3]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "tensor(0, dtype=torch.uint8)"
      ]
     },
     "execution_count": 37,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "y_train_sub[1931]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 58,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAPsAAAD4CAYAAAAq5pAIAAAABHNCSVQICAgIfAhkiAAAAAlwSFlzAAALEgAACxIB0t1+/AAAADh0RVh0U29mdHdhcmUAbWF0cGxvdGxpYiB2ZXJzaW9uMy4xLjAsIGh0dHA6Ly9tYXRwbG90bGliLm9yZy+17YcXAAANf0lEQVR4nO3dXaxV9ZnH8d8PBSVAImpgzhxgWusb4yTCSIixdaJp2gg3WLWTctEwxsypEcc26YUvk1gvCZm28UYSGklh0rGpaY1Em04Ra5wxBkGhiGDB0TOUlxymmogkIgM8c3EWk1M8+78Pe+03eL6f5GTvvZ793+vJDj/W2nuttf+OCAG48E3qdQMAuoOwA0kQdiAJwg4kQdiBJC7u5sps89U/0GER4fGW19qy277D9h9sv2f7kTqvBaCz3OpxdtsXSdor6WuSDkjaKml5ROwujGHLDnRYJ7bsiyW9FxHvR8QJST+XtKzG6wHooDphH5T0xzGPD1TL/oztIdvbbG+rsS4ANdX5gm68XYXP7aZHxFpJayV244FeqrNlPyBp7pjHcyQdqtcOgE6pE/atkq6x/UXbUyR9S9LG9rQFoN1a3o2PiJO2H5T075IukrQuIt5pW2cA2qrlQ28trYzP7EDHdeSkGgDnD8IOJEHYgSQIO5AEYQeSIOxAEoQdSIKwA0kQdiAJwg4kQdiBJAg7kARhB5Ig7EAShB1IgrADSRB2IAnCDiRB2IEkCDuQBGEHkiDsQBKEHUiCsANJEHYgCcIOJEHYgSQIO5AEYQeSIOxAEi3Pzy5JtoclfSLplKSTEbGoHU0BaL9aYa/cHhF/asPrAOggduOBJOqGPST91vabtofGe4LtIdvbbG+ruS4ANTgiWh9s/2VEHLI9S9ImSf8UEa8Wnt/6ygBMSER4vOW1tuwRcai6PSLpOUmL67wegM5pOey2p9mecea+pK9L2tWuxgC0V51v42dLes72mdf5t4j4TVu66oBJk8r/r02bNq1YP3bsWMPanDlzimOvuOKKYn3evHnF+lVXXVWs33333Q1rTz75ZHHs66+/XqwfOnSoWK/zMRDd1XLYI+J9STe2sRcAHcShNyAJwg4kQdiBJAg7kARhB5KodQbdOa+s5hl0M2bMaFh76qmnimNfeeWVYn1wcLCVltri9OnTxfrIyEjLr33xxeUDLrfeemuxvnDhwmL9s88+K9aHhsY9i1qS9MYbbxTHojUdOYMOwPmDsANJEHYgCcIOJEHYgSQIO5AEYQeSaMcPTnbN/PnzG9buuuuu4th9+/YV6/v37y/Wd+7c2bA2a9as4tjNmzcX682Os586dapYr2P79u3Feuk9l6R169YV66tWrWpYW7p0aXHs8ePHi3WcG7bsQBKEHUiCsANJEHYgCcIOJEHYgSQIO5DEeXU9e8n1119frC9ZsqRYX7NmTbF+oR7zvfnmm4v1LVu2FOsnTpwo1kvX0z/66KPFsatXry7Wm52fkBXXswPJEXYgCcIOJEHYgSQIO5AEYQeSIOxAEufV9ewl7777bq16Vs1+V77ueRil3+ufMmVKcWw1HTjapOmW3fY620ds7xqz7HLbm2zvq25ndrZNAHVNZDf+p5LuOGvZI5I2R8Q1kjZXjwH0saZhj4hXJX101uJlktZX99dLurPNfQFos1Y/s8+OiMOSFBGHbTf8ETbbQ5IaT/gFoCs6/gVdRKyVtFbq7IUwAMpaPfQ2YntAkqrbI+1rCUAntBr2jZJWVPdXSHq+Pe0A6JSmu/G2n5F0m6QrbR+Q9ANJqyT9wvZ9kvZL+mYnm0TrbrzxxmL9tddeq/X6J0+eLNa7+XsJKGsa9ohY3qD01Tb3AqCDOF0WSIKwA0kQdiAJwg4kQdiBJC6YS1wxvqlTpxbrdQ+NjYyMFOuzZ8/u2LpxbtiyA0kQdiAJwg4kQdiBJAg7kARhB5Ig7EASHGe/AFx77bUNa53+Ce3BwcFi/eOPP25YGx4eLo49depUKy2hAbbsQBKEHUiCsANJEHYgCcIOJEHYgSQIO5AEx9kvAHv37u3Zug8ePFisT58+vWFtYGCgOLbZlM1cD39u2LIDSRB2IAnCDiRB2IEkCDuQBGEHkiDsQBIcZ0ctzY6Fl1x66aXF+qRJ5W0R17ufm6ZbdtvrbB+xvWvMsidsH7S9o/pb2tk2AdQ1kd34n0q6Y5zlP46IBdXfr9vbFoB2axr2iHhV0kdd6AVAB9X5gu5B2zur3fyZjZ5ke8j2NtvbaqwLQE2thn2NpC9JWiDpsKQfNnpiRKyNiEURsajFdQFog5bCHhEjEXEqIk5L+omkxe1tC0C7tRR222OvTfyGpF2NngugPzQ9zm77GUm3SbrS9gFJP5B0m+0FkkLSsKTvdLBH9LEPPvigWF+wYEHD2uTJk4tjm12v3uwYf+k4/enTp2ut+3zUNOwRsXycxU93oBcAHcTpskAShB1IgrADSRB2IAnCDiTBJa7ngdtvv71Yv+WWWxrWXnzxxeLYZj8F3cwNN9xQrJcOr02dOrU49qGHHirWmx16u+666xrW7r///uLYCxFbdiAJwg4kQdiBJAg7kARhB5Ig7EAShB1Iwt28lM/2hXfdYB+47LLLGtbuvffe4tgZM2bUWvfDDz9crH/66acNazt37iyOPXHiRLH+wgsvFOsbNmxoWDt69Ghx7PksIsY9AYEtO5AEYQeSIOxAEoQdSIKwA0kQdiAJwg4kwXH2C1yza77vueeeYn1gYKBYX716dbH+4YcfNqytWLGiOPbll18u1pv9HHRWHGcHkiPsQBKEHUiCsANJEHYgCcIOJEHYgST43fgLXLPzKJ599tlifeXKlcX6JZdcUqyXrht/6aWXimPRXk237Lbn2v6d7T2237H93Wr55bY32d5X3c7sfLsAWjWR3fiTkr4fEfMl3Sxppe2/lvSIpM0RcY2kzdVjAH2qadgj4nBEvFXd/0TSHkmDkpZJWl89bb2kOzvVJID6zukzu+0vSFooaYuk2RFxWBr9D8H2rAZjhiQN1WsTQF0TDrvt6ZJ+Kel7EXG02QUWZ0TEWklrq9fgQhigRyZ06M32ZI0G/WcR8atq8Yjtgao+IOlIZ1oE0A5Nt+we3YQ/LWlPRPxoTGmjpBWSVlW3z3ekQ/TU7t27a43funVrmzpBXRPZjf+ypG9Letv2jmrZYxoN+S9s3ydpv6RvdqZFAO3QNOwR8Z+SGn1A/2p72wHQKZwuCyRB2IEkCDuQBGEHkiDsQBJc4oqiBx54oNb4xx9/vE2doC627EAShB1IgrADSRB2IAnCDiRB2IEkCDuQBMfZk7v66quL9Xnz5hXrx48fL9aHh4fPtSV0CFt2IAnCDiRB2IEkCDuQBGEHkiDsQBKEHUiC4+zJ3XTTTcX6/Pnzi/Xt27e3sx10EFt2IAnCDiRB2IEkCDuQBGEHkiDsQBKEHUjCEVF+gj1X0gZJfyHptKS1EfGk7Sck/aOk/6me+lhE/LrJa5VXhq5bsmRJsT579uxiff369cV6s39faL+IGHfW5YmcVHNS0vcj4i3bMyS9aXtTVftxRPxLu5oE0DkTmZ/9sKTD1f1PbO+RNNjpxgC01zl9Zrf9BUkLJW2pFj1oe6ftdbZnNhgzZHub7W21OgVQy4TDbnu6pF9K+l5EHJW0RtKXJC3Q6Jb/h+ONi4i1EbEoIha1oV8ALZpQ2G1P1mjQfxYRv5KkiBiJiFMRcVrSTyQt7lybAOpqGnbblvS0pD0R8aMxywfGPO0bkna1vz0A7TKRQ29fkfQfkt7W6KE3SXpM0nKN7sKHpGFJ36m+zCu9FsdhgA5rdOitadjbibADndco7JxBByRB2IEkCDuQBGEHkiDsQBKEHUiCsANJEHYgCcIOJEHYgSQIO5AEYQeSIOxAEoQdSKLbUzb/SdJ/j3l8ZbWsH/Vrb/3al0RvrWpnb3/VqNDV69k/t3J7W7/+Nl2/9tavfUn01qpu9cZuPJAEYQeS6HXY1/Z4/SX92lu/9iXRW6u60ltPP7MD6J5eb9kBdAlhB5LoSdht32H7D7bfs/1IL3poxPaw7bdt7+j1/HTVHHpHbO8as+xy25ts76tux51jr0e9PWH7YPXe7bC9tEe9zbX9O9t7bL9j+7vV8p6+d4W+uvK+df0zu+2LJO2V9DVJByRtlbQ8InZ3tZEGbA9LWhQRPT8Bw/bfSTomaUNE/E21bLWkjyJiVfUf5cyIeLhPentC0rFeT+NdzVY0MHaacUl3SvoH9fC9K/T19+rC+9aLLftiSe9FxPsRcULSzyUt60EffS8iXpX00VmLl0laX91fr9F/LF3XoLe+EBGHI+Kt6v4nks5MM97T967QV1f0IuyDkv445vEB9dd87yHpt7bftD3U62bGMfvMNFvV7awe93O2ptN4d9NZ04z3zXvXyvTndfUi7ONNTdNPx/++HBF/K2mJpJXV7iomZkLTeHfLONOM94VWpz+vqxdhPyBp7pjHcyQd6kEf44qIQ9XtEUnPqf+moh45M4NudXukx/38v36axnu8acbVB+9dL6c/70XYt0q6xvYXbU+R9C1JG3vQx+fYnlZ9cSLb0yR9Xf03FfVGSSuq+yskPd/DXv5Mv0zj3WiacfX4vev59OcR0fU/SUs1+o38f0n651700KCvqyT9vvp7p9e9SXpGo7t1/6vRPaL7JF0habOkfdXt5X3U279qdGrvnRoN1kCPevuKRj8a7pS0o/pb2uv3rtBXV943TpcFkuAMOiAJwg4kQdiBJAg7kARhB5Ig7EAShB1I4v8AN54yjLL2Aa8AAAAASUVORK5CYII=\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAPsAAAD4CAYAAAAq5pAIAAAABHNCSVQICAgIfAhkiAAAAAlwSFlzAAALEgAACxIB0t1+/AAAADh0RVh0U29mdHdhcmUAbWF0cGxvdGxpYiB2ZXJzaW9uMy4xLjAsIGh0dHA6Ly9tYXRwbG90bGliLm9yZy+17YcXAAAM+UlEQVR4nO3db6xU9Z3H8c9Ht0gCmKBElrU3SwVd3WwUFkI2oTHVxkaNBvrATTFRVs3ePsBNm/TBqvtgfWg229ZVI8ltJIVNta5piZg03SI2cTeujRfDyj8LaFhKucISH2BFQb3ffXAPmyve+c1lzsycge/7ldzMzPnOmfPNCR/OmTl/fo4IAbjwXdR0AwD6g7ADSRB2IAnCDiRB2IEk/qifC7PNT/9Aj0WEp5pea8tu+1bbv7V9wPZDdT4LQG+50+Psti+WtE/SLZIOS3pD0pqI2FOYhy070GO92LKvkHQgIt6NiNOSfippVY3PA9BDdcJ+paTfTXp9uJr2ObaHbY/aHq2xLAA11fmBbqpdhS/spkfEiKQRid14oEl1tuyHJQ1Nev1lSUfqtQOgV+qE/Q1JV9v+iu0Zkr4laUt32gLQbR3vxkfEp7YflPTvki6WtCEidnetMwBd1fGht44Wxnd2oOd6clINgPMHYQeSIOxAEoQdSIKwA0kQdiAJwg4kQdiBJAg7kARhB5Ig7EAShB1IgrADSRB2IAnCDiRB2IEkCDuQBGEHkiDsQBKEHUiCsANJEHYgCcIOJEHYgSQIO5AEYQeSIOxAEoQdSIKwA0kQdiCJjsdnlyTbByV9IOkzSZ9GxPJuNAWg+2qFvXJTRBzvwucA6CF244Ek6oY9JP3K9nbbw1O9wfaw7VHbozWXBaAGR0TnM9t/EhFHbF8haaukv4uIVwvv73xhAKYlIjzV9Fpb9og4Uj0ek7RZ0oo6nwegdzoOu+1ZtueceS7pG5J2dasxAN1V59f4+ZI22z7zOc9GxC+70lUPXHRR+f+1WbNmFeuXX355y9qNN95YnHfZsmXF+tDQULF+1VVXFes33HBDy9quXeX/f1977bVi/YknnijW9+3bV6x/8sknxTr6p+OwR8S7klr/KwMwUDj0BiRB2IEkCDuQBGEHkiDsQBK1zqA754XVPINuzpw5LWtPP/10cd6FCxcW6ytXruykpa4YHx8v1o8ePdqnTr7o0ksvLdbfeeedYv3xxx9vWXv22WeL854+fbpYx9R6cgYdgPMHYQeSIOxAEoQdSIKwA0kQdiAJwg4k0Y0bTvbNdddd17K2evXq4rztLmE9dOhQsb5ly5aWtZMnTxbnfemll4r1U6dOFeujo83d0WvRokXF+vbt24v1DRs2tKyVzpuQpCeffLJYx7lhyw4kQdiBJAg7kARhB5Ig7EAShB1IgrADSZxX17OXXHvttcX6bbfdVqyvX7++WP/444/PuacMnn/++WL9rrvuallrd736kiVLivW33367WM+K69mB5Ag7kARhB5Ig7EAShB1IgrADSRB2IInz6nr2knbHXDkmO3hmzJhRrM+cObNPneTQdstue4PtY7Z3TZp2me2ttvdXj3N72yaAuqazG/9jSbeeNe0hSdsi4mpJ26rXAAZY27BHxKuS3j9r8ipJG6vnGyWV7wkFoHGdfmefHxFjkhQRY7avaPVG28OShjtcDoAu6fkPdBExImlE6u2FMADKOj30dtT2AkmqHo91ryUAvdBp2LdIWls9Xyvpxe60A6BXpnPo7TlJ/yXpz2wftv2ApMck3WJ7v6RbqtcABljb7+wRsaZF6etd7gVAD3G6LJAEYQeSIOxAEoQdSIKwA0lcMJe44vzTz9uYgy07kAZhB5Ig7EAShB1IgrADSRB2IAnCDiTBcXY05uDBg8X6jh07+tNIEmzZgSQIO5AEYQeSIOxAEoQdSIKwA0kQdiAJjrOjMQsWLCjWFy9eXKwfOHCgm+1c8NiyA0kQdiAJwg4kQdiBJAg7kARhB5Ig7EASHGdHY2bOnFmsz549u0+d5DCd8dk32D5me9ekaY/a/r3tHdXf7b1tE0Bd09mN/7GkW6eY/sOIWFL9/aK7bQHotrZhj4hXJb3fh14A9FCdH+getP1WtZs/t9WbbA/bHrU9WmNZAGrqNOzrJS2StETSmKTvt3pjRIxExPKIWN7hsgB0QUdhj4ijEfFZRIxL+pGkFd1tC0C3dRR225OvTfympF2t3gtgMLQ9zm77OUlfkzTP9mFJ/yjpa7aXSApJByV9u4c9Iqm5c1v+FCRJGhoaKtavv/76lrXdu3cX5213T/vzUduwR8SaKSY/04NeAPQQp8sCSRB2IAnCDiRB2IEkCDuQhCOifwuz+7ewC8h9991XrN99990ta3v27Ol2O59z//33F+uzZs3q+LNPnDhRrNsu1sfGxlrWli5dWpz3o48+KtYHWURMuWLYsgNJEHYgCcIOJEHYgSQIO5AEYQeSIOxAEtxK+jywefPmYv2aa65pWVu3bl1x3kG+XfN7771XrD/11FPF+qZNm1rWzufj6J1iyw4kQdiBJAg7kARhB5Ig7EAShB1IgrADSXA9+wVu3rx5xfpNN91UrN9xxx3F+j333FOsnzp1qmXtzjvvLM77yiuvFOvj4+PFelZczw4kR9iBJAg7kARhB5Ig7EAShB1IgrADSXA9+wXu+PHjxfoLL7xQrLe7v3o7pXu/v/zyy7U+G+em7Zbd9pDtX9vea3u37e9U0y+zvdX2/uqxPJg2gEZNZzf+U0nfi4jrJP2VpHW2/1zSQ5K2RcTVkrZVrwEMqLZhj4ixiHizev6BpL2SrpS0StLG6m0bJa3uVZMA6jun7+y2F0paKuk3kuZHxJg08R+C7StazDMsabhemwDqmnbYbc+W9DNJ342IE+0G1TsjIkYkjVSfwYUwQEOmdejN9pc0EfSfRMTPq8lHbS+o6gskHetNiwC6oe2W3ROb8Gck7Y2IH0wqbZG0VtJj1eOLPekQjZo/f36t+U+ePNmlTlDXdHbjV0q6R9JO2zuqaY9oIuT/ZvsBSYck3dWbFgF0Q9uwR8R/Smr1Bf3r3W0HQK9wuiyQBGEHkiDsQBKEHUiCsANJcIkrim6++eZa8z/88MNd6gR1sWUHkiDsQBKEHUiCsANJEHYgCcIOJEHYgSQYsjm5xYsXF+s7d+6s9fmlIaM//PDDWp+NqTFkM5AcYQeSIOxAEoQdSIKwA0kQdiAJwg4kwfXsyS1btqxYv+SSS4r1119/vVjnWPrgYMsOJEHYgSQIO5AEYQeSIOxAEoQdSIKwA0lMZ3z2IUmbJP2xpHFJIxHxL7YflfS3kv63eusjEfGLXjWK3jhx4kSxfujQoWL93nvv7WY76KHpnFTzqaTvRcSbtudI2m57a1X7YUT8c+/aA9At0xmffUzSWPX8A9t7JV3Z68YAdNc5fWe3vVDSUkm/qSY9aPst2xtsz20xz7DtUdujtToFUMu0w257tqSfSfpuRJyQtF7SIklLNLHl//5U80XESEQsj4jlXegXQIemFXbbX9JE0H8SET+XpIg4GhGfRcS4pB9JWtG7NgHU1Tbsti3pGUl7I+IHk6YvmPS2b0ra1f32AHRL21tJ2/6qpP+QtFMTh94k6RFJazSxCx+SDkr6dvVjXumzuJU00GOtbiXNfeOBCwz3jQeSI+xAEoQdSIKwA0kQdiAJwg4kQdiBJAg7kARhB5Ig7EAShB1IgrADSRB2IAnCDiTR7yGbj0v6n0mv51XTBtGg9jaofUn01qlu9vanrQp9vZ79Cwu3Rwf13nSD2tug9iXRW6f61Ru78UAShB1IoumwjzS8/JJB7W1Q+5LorVN96a3R7+wA+qfpLTuAPiHsQBKNhN32rbZ/a/uA7Yea6KEV2wdt77S9o+nx6aox9I7Z3jVp2mW2t9reXz1OOcZeQ709avv31brbYfv2hnobsv1r23tt77b9nWp6o+uu0Fdf1lvfv7PbvljSPkm3SDos6Q1JayJiT18bacH2QUnLI6LxEzBs3yjpD5I2RcRfVNP+SdL7EfFY9R/l3Ij4+wHp7VFJf2h6GO9qtKIFk4cZl7Ra0t+owXVX6Ouv1Yf11sSWfYWkAxHxbkSclvRTSasa6GPgRcSrkt4/a/IqSRur5xs18Y+l71r0NhAiYiwi3qyefyDpzDDjja67Ql990UTYr5T0u0mvD2uwxnsPSb+yvd32cNPNTGH+mWG2qscrGu7nbG2H8e6ns4YZH5h118nw53U1EfaphqYZpON/KyPiLyXdJmldtbuK6ZnWMN79MsUw4wOh0+HP62oi7IclDU16/WVJRxroY0oRcaR6PCZpswZvKOqjZ0bQrR6PNdzP/xukYbynGmZcA7Dumhz+vImwvyHpattfsT1D0rckbWmgjy+wPav64US2Z0n6hgZvKOotktZWz9dKerHBXj5nUIbxbjXMuBped40Pfx4Rff+TdLsmfpF/R9I/NNFDi76ukvTf1d/upnuT9Jwmdus+0cQe0QOSLpe0TdL+6vGyAertXzUxtPdbmgjWgoZ6+6omvhq+JWlH9Xd70+uu0Fdf1hunywJJcAYdkARhB5Ig7EAShB1IgrADSRB2IAnCDiTxfz7UAdNKIvJSAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "i = 8\n",
    "plt.imshow(x_adv.cpu().detach().numpy()[i], cmap='gray')\n",
    "plt.show()\n",
    "plt.imshow(x_test_sub.cpu().detach().numpy()[i], cmap='gray')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 58,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "tensor(2.9113, grad_fn=<MeanBackward0>)"
      ]
     },
     "execution_count": 58,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "(x_test_sub - x_adv.cpu()).view(num, -1).norm(2, 1).mean()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "metadata": {
    "collapsed": true,
    "jupyter": {
     "outputs_hidden": true
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "torch.return_types.min(\n",
       "values=tensor([-0.4831, -0.4931, -0.4981, -0.4964, -0.4972, -0.4926, -0.4997, -0.4477,\n",
       "        -0.4990, -0.4980, -0.4247, -0.4960, -0.4990, -0.4995, -0.4997, -0.4995,\n",
       "        -0.4964, -0.4994, -0.4999, -0.4997, -0.4971, -0.4974, -0.4231, -0.4872,\n",
       "        -0.4995, -0.4903, -0.5000, -0.4987, -0.4945, -0.4995, -0.4998, -0.4999,\n",
       "        -0.4984, -0.4952, -0.5000, -0.4998, -0.4983, -0.4993, -0.4940, -0.4998,\n",
       "        -0.4991, -0.4993, -0.4885, -0.4588, -0.4945, -0.4957, -0.4997, -0.4866,\n",
       "        -0.4411, -0.4933, -0.4999, -0.4949, -0.5000, -0.4998, -0.4995, -0.3135,\n",
       "        -0.4698, -0.4998, -0.5000, -0.4997, -0.4981, -0.4992, -0.4000, -0.4967,\n",
       "        -0.4991, -0.4999, -0.4980, -0.4997, -0.4710, -0.4937, -0.4997, -0.4975,\n",
       "        -0.4815, -0.4997, -0.4986, -0.4990, -0.4982, -0.4090, -0.4999, -0.4967,\n",
       "        -0.4668, -0.4962, -0.4983, -0.4996, -0.4919, -0.4906, -0.4990, -0.4984,\n",
       "        -0.4946, -0.4972, -0.4996, -0.4914, -0.4876, -0.4997, -0.4990, -0.4942,\n",
       "        -0.4982, -0.4999, -0.4978, -0.4985, -0.4988, -0.3804, -0.4939, -0.4961,\n",
       "        -0.5000, -0.4982, -0.4967, -0.4993, -0.4906, -0.4922, -0.4979, -0.4982,\n",
       "        -0.4431, -0.4999, -0.4974, -0.4815, -0.4883, -0.4995, -0.4736, -0.4999,\n",
       "        -0.4956, -0.4996, -0.4922, -0.5000, -0.4964, -0.4986, -0.4896, -0.4886,\n",
       "        -0.4993, -0.4994, -0.4966, -0.4852, -0.4995, -0.4995, -0.4980, -0.4922,\n",
       "        -0.4330, -0.4915, -0.4974, -0.4551, -0.4961, -0.4999, -0.4979, -0.4995,\n",
       "        -0.4988, -0.4960, -0.4998, -0.4969, -0.4992, -0.4985, -0.4607, -0.4831,\n",
       "        -0.4998, -0.3739, -0.4868, -0.4999, -0.4644, -0.4956, -0.4963, -0.4890,\n",
       "        -0.4996, -0.4898, -0.4982, -0.4979, -0.4980, -0.4997, -0.4948, -0.4829,\n",
       "        -0.4999, -0.3412, -0.5000, -0.4993, -0.4945, -0.4853, -0.4910, -0.4459,\n",
       "        -0.4933, -0.4951, -0.4499, -0.4881, -0.4767, -0.4995, -0.4996, -0.4006,\n",
       "        -0.4842, -0.4999, -0.4928, -0.4997, -0.5000, -0.4986, -0.4947, -0.4964,\n",
       "        -0.4912, -0.4985, -0.4996, -0.4034, -0.4996, -0.4997, -0.4982, -0.4920],\n",
       "       grad_fn=<MinBackward0>),\n",
       "indices=tensor([267, 352, 215, 212, 320, 239, 211, 546, 238, 625, 574, 713, 713, 462,\n",
       "        268, 202, 294, 269, 407, 268, 657, 209, 378, 211, 373, 654, 601, 352,\n",
       "        349, 211, 545, 182, 269, 435, 351, 239, 435, 516, 292, 208, 292, 628,\n",
       "        158, 295, 345, 518, 297, 600, 156, 294, 518, 462, 207, 297, 188, 213,\n",
       "        379, 323, 658, 380, 652, 232, 298, 462, 598, 685, 182, 352, 570, 435,\n",
       "        406, 487, 658, 462, 266, 184, 294, 375, 232, 183, 574, 241, 491, 434,\n",
       "        602, 435, 407, 234, 235, 211, 209, 686, 183, 490, 348, 684, 324, 407,\n",
       "        379, 243, 464, 628, 491, 185, 546, 713, 156, 324, 296, 243, 206, 606,\n",
       "        574, 269, 658, 156, 239, 212, 293, 155, 236, 262, 293, 457, 434, 324,\n",
       "        350, 435, 265, 434, 435, 155, 408, 352, 348, 213, 295, 571, 297, 243,\n",
       "        683, 295, 380, 206, 379, 655, 547, 684, 323, 462, 546, 687, 239, 185,\n",
       "        435, 490, 574, 629, 519, 655, 461, 153, 682, 435, 714, 686, 156, 490,\n",
       "        548, 679, 520, 518, 349, 433, 209, 156, 518, 680, 267, 323, 492, 210,\n",
       "        379, 518, 267, 464, 295, 462, 379, 435, 352, 407, 183, 211, 352, 574,\n",
       "        489, 241, 461, 518]))"
      ]
     },
     "execution_count": 44,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "(x_test_sub - x_adv.cpu()).view(num, -1).min(1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [],
   "source": [
    "net.compute_logits(x_test[0:1], recompute_train_rep=True, requires_grad=False)\n",
    "logits = torch.zeros((x_test.size(0), 10))\n",
    "\n",
    "for i in range(x_test.size(0) // 100):\n",
    "    logits[i * 100:(i + 1) * 100] = net.compute_logits(x_test[i * 100:(i + 1) * 100])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Acc: 0.9863\n"
     ]
    }
   ],
   "source": [
    "ind = np.where((logits.argmax(1) == y_test).numpy())[0]\n",
    "acc = (logits.argmax(1) == y_test).float().mean()\n",
    "print('Acc: %.4f' % acc)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "    step: 0; loss: 99.682; l2dist: 0.000\n",
      "    step: 100; loss: 46.878; l2dist: 1.176\n",
      "    step: 200; loss: 39.503; l2dist: 1.154\n",
      "    step: 300; loss: 39.618; l2dist: 1.202\n",
      "    step: 400; loss: 39.449; l2dist: 1.161\n",
      "    step: 500; loss: 39.968; l2dist: 1.254\n",
      "    step: 600; loss: 39.540; l2dist: 1.173\n",
      "    step: 700; loss: 39.788; l2dist: 1.215\n",
      "    step: 800; loss: 39.506; l2dist: 1.174\n",
      "    step: 900; loss: 39.391; l2dist: 1.156\n",
      "binary step: 0; number of successful adv: 4/10\n",
      "    step: 0; loss: 618.832; l2dist: 0.000\n",
      "    step: 100; loss: 260.767; l2dist: 2.274\n",
      "    step: 200; loss: 256.815; l2dist: 1.813\n",
      "    step: 300; loss: 255.130; l2dist: 1.670\n",
      "    step: 400; loss: 254.723; l2dist: 1.558\n",
      "    step: 500; loss: 254.830; l2dist: 1.611\n",
      "    step: 600; loss: 254.621; l2dist: 1.578\n",
      "    step: 700; loss: 254.493; l2dist: 1.577\n",
      "    step: 800; loss: 254.820; l2dist: 1.609\n",
      "    step: 900; loss: 255.064; l2dist: 1.654\n",
      "binary step: 1; number of successful adv: 5/10\n",
      "    step: 0; loss: 5055.853; l2dist: 0.000\n",
      "    step: 100; loss: 1615.679; l2dist: 3.172\n",
      "    step: 200; loss: 1612.208; l2dist: 2.825\n",
      "    step: 300; loss: 1609.636; l2dist: 2.545\n",
      "    step: 400; loss: 1608.305; l2dist: 2.402\n",
      "    step: 500; loss: 1606.565; l2dist: 2.150\n",
      "    step: 600; loss: 1605.773; l2dist: 2.009\n",
      "    step: 700; loss: 1608.834; l2dist: 2.447\n",
      "    step: 800; loss: 1607.736; l2dist: 2.342\n",
      "    step: 900; loss: 1605.544; l2dist: 2.002\n",
      "binary step: 2; number of successful adv: 6/10\n",
      "    step: 0; loss: 40513.301; l2dist: 0.000\n",
      "    step: 100; loss: 16065.270; l2dist: 3.858\n",
      "    step: 200; loss: 16057.067; l2dist: 3.584\n",
      "    step: 300; loss: 16049.965; l2dist: 3.338\n",
      "    step: 400; loss: 16046.580; l2dist: 3.134\n",
      "    step: 500; loss: 16044.347; l2dist: 2.935\n",
      "    step: 600; loss: 16043.174; l2dist: 2.861\n",
      "    step: 700; loss: 16041.300; l2dist: 2.624\n",
      "    step: 800; loss: 16041.005; l2dist: 2.630\n",
      "    step: 900; loss: 16039.964; l2dist: 2.496\n",
      "binary step: 3; number of successful adv: 6/10\n",
      "    step: 0; loss: 399616.219; l2dist: 0.000\n",
      "    step: 100; loss: 40013.445; l2dist: 5.280\n",
      "    step: 200; loss: 40010.586; l2dist: 5.161\n",
      "    step: 300; loss: 40009.137; l2dist: 5.054\n",
      "    step: 400; loss: 40007.664; l2dist: 4.908\n",
      "    step: 500; loss: 40006.375; l2dist: 4.812\n",
      "    step: 600; loss: 40004.867; l2dist: 4.670\n",
      "    step: 700; loss: 40003.367; l2dist: 4.548\n",
      "    step: 800; loss: 40001.090; l2dist: 4.350\n",
      "    step: 900; loss: 39999.945; l2dist: 4.265\n",
      "binary step: 4; number of successful adv: 8/10\n",
      "    step: 0; loss: 2106370.500; l2dist: 0.000\n",
      "    step: 100; loss: 99945.828; l2dist: 6.229\n",
      "    step: 200; loss: 99944.789; l2dist: 6.143\n",
      "    step: 300; loss: 99943.867; l2dist: 6.068\n",
      "    step: 400; loss: 99942.930; l2dist: 5.998\n",
      "    step: 500; loss: 99941.586; l2dist: 5.892\n",
      "    step: 600; loss: 99940.555; l2dist: 5.776\n",
      "    step: 700; loss: 99940.570; l2dist: 5.812\n",
      "    step: 800; loss: 99937.898; l2dist: 5.592\n",
      "    step: 900; loss: 99939.281; l2dist: 5.642\n",
      "binary step: 5; number of successful adv: 9/10\n",
      "    step: 0; loss: 10594471.000; l2dist: 0.000\n",
      "    step: 100; loss: 45.802; l2dist: 6.699\n",
      "    step: 200; loss: 45.496; l2dist: 6.676\n",
      "    step: 300; loss: 45.043; l2dist: 6.642\n",
      "    step: 400; loss: 44.420; l2dist: 6.595\n",
      "    step: 500; loss: 43.668; l2dist: 6.539\n",
      "    step: 600; loss: 42.510; l2dist: 6.451\n",
      "    step: 700; loss: 41.264; l2dist: 6.356\n",
      "    step: 800; loss: 39.678; l2dist: 6.232\n",
      "    step: 900; loss: 37.476; l2dist: 6.043\n",
      "binary step: 6; number of successful adv: 10/10\n",
      "    step: 0; loss: 5856205.500; l2dist: 0.000\n",
      "    step: 100; loss: 47.005; l2dist: 6.792\n",
      "    step: 200; loss: 46.697; l2dist: 6.769\n",
      "    step: 300; loss: 46.272; l2dist: 6.736\n",
      "    step: 400; loss: 45.688; l2dist: 6.691\n",
      "    step: 500; loss: 44.896; l2dist: 6.629\n",
      "    step: 600; loss: 43.928; l2dist: 6.553\n",
      "    step: 700; loss: 42.544; l2dist: 6.442\n",
      "    step: 800; loss: 40.829; l2dist: 6.298\n",
      "    step: 900; loss: 38.442; l2dist: 6.091\n",
      "binary step: 7; number of successful adv: 10/10\n",
      "    step: 0; loss: 3487072.000; l2dist: 0.000\n",
      "    step: 100; loss: 44.517; l2dist: 6.629\n",
      "    step: 200; loss: 44.127; l2dist: 6.599\n",
      "    step: 300; loss: 43.570; l2dist: 6.556\n",
      "    step: 400; loss: 42.754; l2dist: 6.492\n",
      "    step: 500; loss: 41.800; l2dist: 6.417\n",
      "    step: 600; loss: 40.719; l2dist: 6.329\n",
      "    step: 700; loss: 39.251; l2dist: 6.203\n",
      "    step: 800; loss: 37.304; l2dist: 6.028\n",
      "    step: 900; loss: 34.828; l2dist: 5.792\n",
      "binary step: 8; number of successful adv: 10/10\n",
      "    step: 0; loss: 3487072.000; l2dist: 0.000\n",
      "    step: 100; loss: 43.087; l2dist: 6.544\n",
      "    step: 200; loss: 42.630; l2dist: 6.510\n",
      "    step: 300; loss: 43.905; l2dist: 6.580\n",
      "    step: 400; loss: 43.092; l2dist: 6.515\n",
      "    step: 500; loss: 42.116; l2dist: 6.437\n",
      "    step: 600; loss: 40.960; l2dist: 6.342\n",
      "    step: 700; loss: 39.457; l2dist: 6.213\n",
      "    step: 800; loss: 37.458; l2dist: 6.032\n",
      "    step: 900; loss: 34.884; l2dist: 5.783\n",
      "binary step: 9; number of successful adv: 10/10\n",
      "    step: 0; loss: 99.854; l2dist: 0.000\n",
      "    step: 100; loss: 32.711; l2dist: 1.250\n",
      "    step: 200; loss: 32.749; l2dist: 1.299\n",
      "    step: 300; loss: 32.441; l2dist: 1.213\n",
      "    step: 400; loss: 32.528; l2dist: 1.264\n",
      "    step: 500; loss: 33.173; l2dist: 1.413\n",
      "    step: 600; loss: 32.484; l2dist: 1.271\n",
      "    step: 700; loss: 32.732; l2dist: 1.280\n",
      "    step: 800; loss: 32.546; l2dist: 1.266\n",
      "    step: 900; loss: 32.671; l2dist: 1.340\n",
      "binary step: 0; number of successful adv: 5/10\n",
      "    step: 0; loss: 524.521; l2dist: 0.000\n",
      "    step: 100; loss: 254.507; l2dist: 1.522\n",
      "    step: 200; loss: 254.201; l2dist: 1.460\n",
      "    step: 300; loss: 254.156; l2dist: 1.449\n",
      "    step: 400; loss: 254.188; l2dist: 1.478\n",
      "    step: 500; loss: 255.190; l2dist: 1.653\n",
      "    step: 600; loss: 255.015; l2dist: 1.626\n",
      "    step: 700; loss: 254.532; l2dist: 1.510\n",
      "    step: 800; loss: 254.569; l2dist: 1.506\n",
      "    step: 900; loss: 254.531; l2dist: 1.543\n",
      "binary step: 1; number of successful adv: 5/10\n",
      "    step: 0; loss: 5008.207; l2dist: 0.000\n",
      "    step: 100; loss: 2508.624; l2dist: 2.118\n",
      "    step: 200; loss: 2506.208; l2dist: 1.873\n",
      "    step: 300; loss: 2505.379; l2dist: 1.797\n",
      "    step: 400; loss: 2504.561; l2dist: 1.677\n",
      "    step: 500; loss: 2504.457; l2dist: 1.670\n",
      "    step: 600; loss: 2505.899; l2dist: 1.865\n",
      "    step: 700; loss: 2504.291; l2dist: 1.594\n",
      "    step: 800; loss: 2504.653; l2dist: 1.663\n",
      "    step: 900; loss: 2504.087; l2dist: 1.602\n",
      "binary step: 2; number of successful adv: 5/10\n",
      "    step: 0; loss: 49963.570; l2dist: 0.000\n",
      "    step: 100; loss: 25008.512; l2dist: 2.885\n",
      "    step: 200; loss: 25006.924; l2dist: 2.728\n",
      "    step: 300; loss: 25004.447; l2dist: 2.448\n",
      "    step: 400; loss: 25002.699; l2dist: 2.281\n",
      "    step: 500; loss: 25001.145; l2dist: 2.176\n",
      "    step: 600; loss: 24999.365; l2dist: 1.990\n",
      "    step: 700; loss: 24998.990; l2dist: 1.964\n",
      "    step: 800; loss: 24997.928; l2dist: 1.769\n",
      "    step: 900; loss: 24997.826; l2dist: 1.776\n",
      "binary step: 3; number of successful adv: 5/10\n",
      "    step: 0; loss: 499576.375; l2dist: 0.000\n",
      "    step: 100; loss: 89989.734; l2dist: 4.551\n",
      "    step: 200; loss: 89988.586; l2dist: 4.457\n",
      "    step: 300; loss: 89987.836; l2dist: 4.438\n",
      "    step: 400; loss: 89986.102; l2dist: 4.259\n",
      "    step: 500; loss: 89985.039; l2dist: 4.151\n",
      "    step: 600; loss: 89983.734; l2dist: 4.124\n",
      "    step: 700; loss: 89981.469; l2dist: 3.916\n",
      "    step: 800; loss: 89978.922; l2dist: 3.713\n",
      "    step: 900; loss: 89976.578; l2dist: 3.457\n",
      "binary step: 4; number of successful adv: 7/10\n",
      "    step: 0; loss: 3107207.250; l2dist: 0.000\n",
      "    step: 100; loss: 899640.938; l2dist: 4.957\n",
      "    step: 200; loss: 899636.625; l2dist: 4.878\n",
      "    step: 300; loss: 899636.000; l2dist: 4.857\n",
      "    step: 400; loss: 899634.688; l2dist: 4.733\n",
      "    step: 500; loss: 899634.062; l2dist: 4.764\n",
      "    step: 600; loss: 899632.625; l2dist: 4.672\n",
      "    step: 700; loss: 899630.688; l2dist: 4.447\n",
      "    step: 800; loss: 899629.562; l2dist: 4.360\n",
      "    step: 900; loss: 899628.750; l2dist: 4.268\n",
      "binary step: 5; number of successful adv: 7/10\n",
      "    step: 0; loss: 30037866.000; l2dist: 0.000\n",
      "    step: 100; loss: 3997725.000; l2dist: 6.098\n",
      "    step: 200; loss: 999865.750; l2dist: 6.294\n",
      "    step: 300; loss: 999864.875; l2dist: 6.246\n",
      "    step: 400; loss: 999864.188; l2dist: 6.193\n",
      "    step: 500; loss: 999863.688; l2dist: 6.135\n",
      "    step: 600; loss: 999862.938; l2dist: 6.067\n",
      "    step: 700; loss: 999862.750; l2dist: 6.068\n",
      "    step: 800; loss: 999862.750; l2dist: 6.193\n",
      "    step: 900; loss: 999860.188; l2dist: 5.969\n",
      "binary step: 6; number of successful adv: 9/10\n",
      "    step: 0; loss: 110949208.000; l2dist: 0.000\n",
      "    step: 100; loss: 9998271.000; l2dist: 6.500\n",
      "    step: 200; loss: 9998236.000; l2dist: 6.436\n",
      "    step: 300; loss: 9998234.000; l2dist: 6.368\n",
      "    step: 400; loss: 9998233.000; l2dist: 6.303\n",
      "    step: 500; loss: 9998233.000; l2dist: 6.217\n",
      "    step: 600; loss: 9998233.000; l2dist: 6.311\n",
      "    step: 700; loss: 9998235.000; l2dist: 6.435\n",
      "    step: 800; loss: 9998233.000; l2dist: 6.307\n",
      "    step: 900; loss: 9998233.000; l2dist: 6.211\n",
      "binary step: 7; number of successful adv: 9/10\n",
      "    step: 0; loss: 1005695936.000; l2dist: 0.000\n",
      "    step: 100; loss: 324737.062; l2dist: 6.661\n",
      "    step: 200; loss: 324736.688; l2dist: 6.635\n",
      "    step: 300; loss: 324736.156; l2dist: 6.593\n",
      "    step: 400; loss: 324735.375; l2dist: 6.528\n",
      "    step: 500; loss: 324734.281; l2dist: 6.448\n",
      "    step: 600; loss: 324732.781; l2dist: 6.324\n",
      "    step: 700; loss: 324731.562; l2dist: 6.197\n",
      "    step: 800; loss: 324734.344; l2dist: 6.346\n",
      "    step: 900; loss: 324732.938; l2dist: 6.266\n",
      "binary step: 8; number of successful adv: 10/10\n",
      "    step: 0; loss: 1007943552.000; l2dist: 0.000\n",
      "    step: 100; loss: 549524.000; l2dist: 6.729\n",
      "    step: 200; loss: 549523.375; l2dist: 6.689\n",
      "    step: 300; loss: 549522.938; l2dist: 6.645\n",
      "    step: 400; loss: 549522.062; l2dist: 6.588\n",
      "    step: 500; loss: 549520.938; l2dist: 6.496\n",
      "    step: 600; loss: 549522.562; l2dist: 6.587\n",
      "    step: 700; loss: 549521.438; l2dist: 6.489\n",
      "    step: 800; loss: 549520.375; l2dist: 6.384\n",
      "    step: 900; loss: 549518.500; l2dist: 6.256\n",
      "binary step: 9; number of successful adv: 10/10\n",
      "    step: 0; loss: 95.426; l2dist: 0.000\n",
      "    step: 100; loss: 67.240; l2dist: 0.636\n",
      "    step: 200; loss: 67.216; l2dist: 0.655\n",
      "    step: 300; loss: 67.239; l2dist: 0.667\n",
      "    step: 400; loss: 67.230; l2dist: 0.662\n",
      "    step: 500; loss: 67.214; l2dist: 0.626\n",
      "    step: 600; loss: 67.188; l2dist: 0.632\n",
      "    step: 700; loss: 67.193; l2dist: 0.631\n",
      "    step: 800; loss: 67.184; l2dist: 0.637\n",
      "    step: 900; loss: 67.185; l2dist: 0.636\n",
      "binary step: 0; number of successful adv: 1/10\n",
      "    step: 0; loss: 865.233; l2dist: 0.000\n",
      "    step: 100; loss: 318.329; l2dist: 1.908\n",
      "    step: 200; loss: 316.457; l2dist: 1.659\n",
      "    step: 300; loss: 258.559; l2dist: 1.913\n",
      "    step: 400; loss: 256.924; l2dist: 1.731\n",
      "    step: 500; loss: 256.284; l2dist: 1.615\n",
      "    step: 600; loss: 256.667; l2dist: 1.702\n",
      "    step: 700; loss: 257.737; l2dist: 1.860\n",
      "    step: 800; loss: 256.273; l2dist: 1.627\n",
      "    step: 900; loss: 256.615; l2dist: 1.692\n",
      "binary step: 1; number of successful adv: 5/10\n",
      "    step: 0; loss: 5092.121; l2dist: 0.000\n",
      "    step: 100; loss: 2816.720; l2dist: 2.321\n",
      "    step: 200; loss: 2814.524; l2dist: 2.041\n",
      "    step: 300; loss: 2506.951; l2dist: 2.063\n",
      "    step: 400; loss: 2506.480; l2dist: 2.014\n",
      "    step: 500; loss: 2506.261; l2dist: 1.974\n",
      "    step: 600; loss: 2505.869; l2dist: 1.881\n",
      "    step: 700; loss: 2504.971; l2dist: 1.807\n",
      "    step: 800; loss: 2505.100; l2dist: 1.801\n",
      "    step: 900; loss: 2506.271; l2dist: 1.984\n",
      "binary step: 2; number of successful adv: 5/10\n",
      "    step: 0; loss: 48973.695; l2dist: 0.000\n",
      "    step: 100; loss: 25011.865; l2dist: 3.316\n",
      "    step: 200; loss: 25009.809; l2dist: 3.135\n",
      "    step: 300; loss: 25007.986; l2dist: 2.995\n",
      "    step: 400; loss: 25005.969; l2dist: 2.817\n",
      "    step: 500; loss: 25003.734; l2dist: 2.583\n",
      "    step: 600; loss: 25001.795; l2dist: 2.381\n",
      "    step: 700; loss: 25001.520; l2dist: 2.368\n",
      "    step: 800; loss: 25000.566; l2dist: 2.282\n",
      "    step: 900; loss: 24998.414; l2dist: 1.988\n",
      "binary step: 3; number of successful adv: 5/10\n",
      "    step: 0; loss: 488595.750; l2dist: 0.000\n",
      "    step: 100; loss: 159969.219; l2dist: 4.187\n",
      "    step: 200; loss: 159967.562; l2dist: 4.065\n",
      "    step: 300; loss: 159965.906; l2dist: 3.932\n",
      "    step: 400; loss: 159963.734; l2dist: 3.759\n",
      "    step: 500; loss: 159961.844; l2dist: 3.592\n",
      "    step: 600; loss: 159959.734; l2dist: 3.395\n",
      "    step: 700; loss: 159957.891; l2dist: 3.222\n",
      "    step: 800; loss: 159956.703; l2dist: 3.100\n",
      "    step: 900; loss: 159955.109; l2dist: 2.941\n",
      "binary step: 4; number of successful adv: 6/10\n",
      "    step: 0; loss: 3961905.750; l2dist: 0.000\n",
      "    step: 100; loss: 899650.688; l2dist: 4.926\n",
      "    step: 200; loss: 899649.688; l2dist: 4.887\n",
      "    step: 300; loss: 899648.375; l2dist: 4.778\n",
      "    step: 400; loss: 899647.750; l2dist: 4.735\n",
      "    step: 500; loss: 899646.250; l2dist: 4.737\n",
      "    step: 600; loss: 899645.250; l2dist: 4.688\n",
      "    step: 700; loss: 899645.125; l2dist: 4.753\n",
      "    step: 800; loss: 899642.875; l2dist: 4.558\n",
      "    step: 900; loss: 899640.938; l2dist: 4.367\n",
      "binary step: 5; number of successful adv: 7/10\n",
      "    step: 0; loss: 29879874.000; l2dist: 0.000\n",
      "    step: 100; loss: 3997976.750; l2dist: 5.854\n",
      "    step: 200; loss: 3997952.000; l2dist: 5.866\n",
      "    step: 300; loss: 3997951.250; l2dist: 5.837\n",
      "    step: 400; loss: 3997950.750; l2dist: 5.809\n",
      "    step: 500; loss: 3997944.250; l2dist: 5.817\n",
      "    step: 600; loss: 3997934.250; l2dist: 5.803\n",
      "    step: 700; loss: 3997931.500; l2dist: 5.798\n",
      "    step: 800; loss: 3997929.500; l2dist: 5.743\n",
      "    step: 900; loss: 3997928.250; l2dist: 5.670\n",
      "binary step: 6; number of successful adv: 9/10\n",
      "    step: 0; loss: 108796104.000; l2dist: 0.000\n",
      "    step: 100; loss: 21093714.000; l2dist: 5.967\n",
      "    step: 200; loss: 21093670.000; l2dist: 5.988\n",
      "    step: 300; loss: 21093568.000; l2dist: 5.997\n",
      "    step: 400; loss: 21093536.000; l2dist: 6.023\n",
      "    step: 500; loss: 21093536.000; l2dist: 5.985\n",
      "    step: 600; loss: 21093536.000; l2dist: 5.980\n",
      "    step: 700; loss: 21093536.000; l2dist: 5.946\n",
      "    step: 800; loss: 21093534.000; l2dist: 5.931\n",
      "    step: 900; loss: 10000020.000; l2dist: 6.336\n",
      "binary step: 7; number of successful adv: 9/10\n",
      "    step: 0; loss: 983683136.000; l2dist: 0.000\n",
      "    step: 100; loss: 324709.000; l2dist: 6.645\n",
      "    step: 200; loss: 324693.125; l2dist: 6.632\n",
      "    step: 300; loss: 324690.750; l2dist: 6.587\n",
      "    step: 400; loss: 324689.625; l2dist: 6.533\n",
      "    step: 500; loss: 324688.688; l2dist: 6.490\n",
      "    step: 600; loss: 324687.031; l2dist: 6.382\n",
      "    step: 700; loss: 324687.406; l2dist: 6.417\n",
      "    step: 800; loss: 324686.438; l2dist: 6.338\n",
      "    step: 900; loss: 324687.125; l2dist: 6.365\n",
      "binary step: 8; number of successful adv: 10/10\n",
      "    step: 0; loss: 985881280.000; l2dist: 0.000\n",
      "    step: 100; loss: 745184.875; l2dist: 6.627\n",
      "    step: 200; loss: 549443.125; l2dist: 6.664\n",
      "    step: 300; loss: 549442.875; l2dist: 6.637\n",
      "    step: 400; loss: 549442.625; l2dist: 6.605\n",
      "    step: 500; loss: 549442.125; l2dist: 6.555\n",
      "    step: 600; loss: 549441.875; l2dist: 6.528\n",
      "    step: 700; loss: 549441.250; l2dist: 6.483\n",
      "    step: 800; loss: 549440.750; l2dist: 6.430\n",
      "    step: 900; loss: 549440.375; l2dist: 6.398\n",
      "binary step: 9; number of successful adv: 10/10\n",
      "    step: 0; loss: 99.983; l2dist: 0.000\n",
      "    step: 100; loss: 51.358; l2dist: 0.859\n",
      "    step: 200; loss: 51.137; l2dist: 0.846\n",
      "    step: 300; loss: 51.346; l2dist: 0.916\n",
      "    step: 400; loss: 51.426; l2dist: 0.877\n",
      "    step: 500; loss: 51.472; l2dist: 0.944\n",
      "    step: 600; loss: 51.020; l2dist: 0.837\n",
      "    step: 700; loss: 50.999; l2dist: 0.818\n",
      "    step: 800; loss: 51.357; l2dist: 0.924\n",
      "    step: 900; loss: 51.097; l2dist: 0.864\n",
      "binary step: 0; number of successful adv: 3/10\n",
      "    step: 0; loss: 714.893; l2dist: 0.000\n",
      "    step: 100; loss: 388.467; l2dist: 1.818\n",
      "    step: 200; loss: 387.156; l2dist: 1.682\n",
      "    step: 300; loss: 386.748; l2dist: 1.649\n",
      "    step: 400; loss: 363.525; l2dist: 1.569\n",
      "    step: 500; loss: 362.920; l2dist: 1.498\n",
      "    step: 600; loss: 363.220; l2dist: 1.535\n",
      "    step: 700; loss: 362.928; l2dist: 1.491\n",
      "    step: 800; loss: 362.976; l2dist: 1.461\n",
      "    step: 900; loss: 362.793; l2dist: 1.415\n",
      "binary step: 1; number of successful adv: 4/10\n",
      "    step: 0; loss: 6061.559; l2dist: 0.000\n",
      "    step: 100; loss: 1613.732; l2dist: 2.897\n",
      "    step: 200; loss: 1611.028; l2dist: 2.601\n",
      "    step: 300; loss: 1611.315; l2dist: 2.656\n",
      "    step: 400; loss: 1608.874; l2dist: 2.328\n",
      "    step: 500; loss: 1609.688; l2dist: 2.460\n",
      "    step: 600; loss: 1607.896; l2dist: 2.223\n",
      "    step: 700; loss: 1607.786; l2dist: 2.191\n",
      "    step: 800; loss: 1610.010; l2dist: 2.357\n",
      "    step: 900; loss: 1609.645; l2dist: 2.365\n",
      "binary step: 2; number of successful adv: 6/10\n",
      "    step: 0; loss: 41130.055; l2dist: 0.000\n",
      "    step: 100; loss: 4027.428; l2dist: 4.572\n",
      "    step: 200; loss: 4025.031; l2dist: 4.365\n",
      "    step: 300; loss: 4023.105; l2dist: 4.190\n",
      "    step: 400; loss: 4020.841; l2dist: 3.993\n",
      "    step: 500; loss: 4019.058; l2dist: 3.812\n",
      "    step: 600; loss: 4017.077; l2dist: 3.634\n",
      "    step: 700; loss: 4014.552; l2dist: 3.328\n",
      "    step: 800; loss: 4014.465; l2dist: 3.305\n",
      "    step: 900; loss: 4012.087; l2dist: 3.055\n",
      "binary step: 3; number of successful adv: 8/10\n",
      "    step: 0; loss: 211650.422; l2dist: 0.000\n",
      "    step: 100; loss: 10036.087; l2dist: 5.425\n",
      "    step: 200; loss: 10034.370; l2dist: 5.290\n",
      "    step: 300; loss: 10032.893; l2dist: 5.167\n",
      "    step: 400; loss: 10031.308; l2dist: 5.052\n",
      "    step: 500; loss: 10029.307; l2dist: 4.884\n",
      "    step: 600; loss: 10027.304; l2dist: 4.690\n",
      "    step: 700; loss: 10024.693; l2dist: 4.452\n",
      "    step: 800; loss: 10025.568; l2dist: 4.485\n",
      "    step: 900; loss: 10023.038; l2dist: 4.251\n",
      "binary step: 4; number of successful adv: 9/10\n",
      "    step: 0; loss: 1061832.250; l2dist: 0.000\n",
      "    step: 100; loss: 100040.945; l2dist: 5.741\n",
      "    step: 200; loss: 100038.320; l2dist: 5.590\n",
      "    step: 300; loss: 100037.219; l2dist: 5.518\n",
      "    step: 400; loss: 100035.805; l2dist: 5.439\n",
      "    step: 500; loss: 100034.648; l2dist: 5.351\n",
      "    step: 600; loss: 100033.445; l2dist: 5.249\n",
      "    step: 700; loss: 100032.211; l2dist: 5.154\n",
      "    step: 800; loss: 100030.719; l2dist: 5.025\n",
      "    step: 900; loss: 100028.914; l2dist: 4.850\n",
      "binary step: 5; number of successful adv: 9/10\n",
      "    step: 0; loss: 10036048.000; l2dist: 0.000\n",
      "    step: 100; loss: 34.754; l2dist: 5.818\n",
      "    step: 200; loss: 34.409; l2dist: 5.787\n",
      "    step: 300; loss: 34.075; l2dist: 5.759\n",
      "    step: 400; loss: 33.937; l2dist: 5.748\n",
      "    step: 500; loss: 33.678; l2dist: 5.729\n",
      "    step: 600; loss: 33.072; l2dist: 5.672\n",
      "    step: 700; loss: 32.738; l2dist: 5.642\n",
      "    step: 800; loss: 32.266; l2dist: 5.599\n",
      "    step: 900; loss: 30.819; l2dist: 5.454\n",
      "binary step: 6; number of successful adv: 10/10\n",
      "    step: 0; loss: 5524076.500; l2dist: 0.000\n",
      "    step: 100; loss: 550056.375; l2dist: 5.885\n",
      "    step: 200; loss: 550046.938; l2dist: 5.846\n",
      "    step: 300; loss: 550045.125; l2dist: 5.797\n",
      "    step: 400; loss: 550043.250; l2dist: 5.739\n",
      "    step: 500; loss: 550037.000; l2dist: 5.734\n",
      "    step: 600; loss: 550035.688; l2dist: 5.645\n",
      "    step: 700; loss: 550034.562; l2dist: 5.569\n",
      "    step: 800; loss: 550033.438; l2dist: 5.484\n",
      "    step: 900; loss: 550032.688; l2dist: 5.413\n",
      "binary step: 7; number of successful adv: 10/10\n",
      "    step: 0; loss: 7767631.000; l2dist: 0.000\n",
      "    step: 100; loss: 775061.875; l2dist: 5.887\n",
      "    step: 200; loss: 775046.250; l2dist: 5.878\n",
      "    step: 300; loss: 775045.188; l2dist: 5.837\n",
      "    step: 400; loss: 775044.062; l2dist: 5.789\n",
      "    step: 500; loss: 775036.250; l2dist: 5.783\n",
      "    step: 600; loss: 775035.312; l2dist: 5.712\n",
      "    step: 700; loss: 775034.125; l2dist: 5.629\n",
      "    step: 800; loss: 775033.188; l2dist: 5.549\n",
      "    step: 900; loss: 775032.625; l2dist: 5.498\n",
      "binary step: 8; number of successful adv: 10/10\n",
      "    step: 0; loss: 10017401.000; l2dist: 0.000\n",
      "    step: 100; loss: 34.772; l2dist: 5.816\n",
      "    step: 200; loss: 34.417; l2dist: 5.771\n",
      "    step: 300; loss: 34.079; l2dist: 5.759\n",
      "    step: 400; loss: 33.836; l2dist: 5.738\n",
      "    step: 500; loss: 33.437; l2dist: 5.703\n",
      "    step: 600; loss: 33.082; l2dist: 5.671\n",
      "    step: 700; loss: 32.631; l2dist: 5.628\n",
      "    step: 800; loss: 31.762; l2dist: 5.544\n",
      "    step: 900; loss: 30.816; l2dist: 5.462\n",
      "binary step: 9; number of successful adv: 10/10\n",
      "    step: 0; loss: 99.958; l2dist: 0.000\n",
      "    step: 100; loss: 21.215; l2dist: 1.724\n",
      "    step: 200; loss: 20.489; l2dist: 1.612\n",
      "    step: 300; loss: 20.633; l2dist: 1.658\n",
      "    step: 400; loss: 20.546; l2dist: 1.638\n",
      "    step: 500; loss: 20.391; l2dist: 1.591\n",
      "    step: 600; loss: 20.630; l2dist: 1.661\n",
      "    step: 700; loss: 20.741; l2dist: 1.645\n",
      "    step: 800; loss: 20.473; l2dist: 1.652\n",
      "    step: 900; loss: 20.449; l2dist: 1.636\n",
      "binary step: 0; number of successful adv: 6/10\n",
      "    step: 0; loss: 429.874; l2dist: 0.000\n",
      "    step: 100; loss: 166.108; l2dist: 2.090\n",
      "    step: 200; loss: 165.018; l2dist: 1.931\n",
      "    step: 300; loss: 165.453; l2dist: 2.011\n",
      "    step: 400; loss: 164.911; l2dist: 1.895\n",
      "    step: 500; loss: 164.927; l2dist: 1.937\n",
      "    step: 600; loss: 164.755; l2dist: 1.870\n",
      "    step: 700; loss: 164.936; l2dist: 1.918\n",
      "    step: 800; loss: 165.192; l2dist: 1.936\n",
      "    step: 900; loss: 165.260; l2dist: 1.960\n",
      "binary step: 1; number of successful adv: 6/10\n",
      "    step: 0; loss: 4013.891; l2dist: 0.000\n",
      "    step: 100; loss: 915.246; l2dist: 3.262\n",
      "    step: 200; loss: 910.479; l2dist: 2.685\n",
      "    step: 300; loss: 909.312; l2dist: 2.517\n",
      "    step: 400; loss: 906.450; l2dist: 2.112\n",
      "    step: 500; loss: 908.540; l2dist: 2.328\n",
      "    step: 600; loss: 907.857; l2dist: 2.279\n",
      "    step: 700; loss: 907.621; l2dist: 2.255\n",
      "    step: 800; loss: 906.713; l2dist: 2.133\n",
      "    step: 900; loss: 905.913; l2dist: 2.046\n",
      "binary step: 2; number of successful adv: 7/10\n",
      "    step: 0; loss: 30549.660; l2dist: 0.000\n",
      "    step: 100; loss: 1029.177; l2dist: 5.008\n",
      "    step: 200; loss: 1024.410; l2dist: 4.594\n",
      "    step: 300; loss: 1020.906; l2dist: 4.228\n",
      "    step: 400; loss: 1018.126; l2dist: 3.922\n",
      "    step: 500; loss: 1015.917; l2dist: 3.638\n",
      "    step: 600; loss: 1013.952; l2dist: 3.392\n",
      "    step: 700; loss: 1012.144; l2dist: 3.169\n",
      "    step: 800; loss: 1010.930; l2dist: 2.996\n",
      "    step: 900; loss: 1010.118; l2dist: 2.936\n",
      "binary step: 3; number of successful adv: 9/10\n",
      "    step: 0; loss: 111302.086; l2dist: 0.000\n",
      "    step: 100; loss: 31.825; l2dist: 5.537\n",
      "    step: 200; loss: 29.261; l2dist: 5.308\n",
      "    step: 300; loss: 26.852; l2dist: 5.092\n",
      "    step: 400; loss: 25.268; l2dist: 4.931\n",
      "    step: 500; loss: 22.199; l2dist: 4.609\n",
      "    step: 600; loss: 18.770; l2dist: 4.198\n",
      "    step: 700; loss: 20.877; l2dist: 4.358\n",
      "    step: 800; loss: 17.909; l2dist: 3.957\n",
      "    step: 900; loss: 15.635; l2dist: 3.594\n",
      "binary step: 4; number of successful adv: 10/10\n",
      "    step: 0; loss: 61701.453; l2dist: 0.000\n",
      "    step: 100; loss: 29.696; l2dist: 5.363\n",
      "    step: 200; loss: 26.765; l2dist: 5.084\n",
      "    step: 300; loss: 25.245; l2dist: 4.937\n",
      "    step: 400; loss: 21.953; l2dist: 4.588\n",
      "    step: 500; loss: 19.284; l2dist: 4.260\n",
      "    step: 600; loss: 15.987; l2dist: 3.820\n",
      "    step: 700; loss: 16.231; l2dist: 3.753\n",
      "    step: 800; loss: 13.735; l2dist: 3.340\n",
      "    step: 900; loss: 17.599; l2dist: 3.882\n",
      "binary step: 5; number of successful adv: 10/10\n",
      "    step: 0; loss: 36901.141; l2dist: 0.000\n",
      "    step: 100; loss: 3280.736; l2dist: 5.172\n",
      "    step: 200; loss: 3277.927; l2dist: 4.940\n",
      "    step: 300; loss: 3274.821; l2dist: 4.672\n",
      "    step: 400; loss: 3271.250; l2dist: 4.329\n",
      "    step: 500; loss: 3267.515; l2dist: 3.923\n",
      "    step: 600; loss: 3264.142; l2dist: 3.540\n",
      "    step: 700; loss: 3262.420; l2dist: 3.289\n",
      "    step: 800; loss: 3261.717; l2dist: 3.190\n",
      "    step: 900; loss: 3260.258; l2dist: 3.004\n",
      "binary step: 6; number of successful adv: 10/10\n",
      "    step: 0; loss: 46995.188; l2dist: 0.000\n",
      "    step: 100; loss: 42.346; l2dist: 5.277\n",
      "    step: 200; loss: 39.882; l2dist: 5.024\n",
      "    step: 300; loss: 37.734; l2dist: 4.859\n",
      "    step: 400; loss: 34.884; l2dist: 4.538\n",
      "    step: 500; loss: 19.169; l2dist: 4.262\n",
      "    step: 600; loss: 18.141; l2dist: 4.015\n",
      "    step: 700; loss: 15.440; l2dist: 3.551\n",
      "    step: 800; loss: 13.686; l2dist: 3.379\n",
      "    step: 900; loss: 12.214; l2dist: 3.128\n",
      "binary step: 7; number of successful adv: 10/10\n",
      "    step: 0; loss: 40795.188; l2dist: 0.000\n",
      "    step: 100; loss: 29.433; l2dist: 5.336\n",
      "    step: 200; loss: 26.560; l2dist: 5.056\n",
      "    step: 300; loss: 23.976; l2dist: 4.812\n",
      "    step: 400; loss: 20.734; l2dist: 4.431\n",
      "    step: 500; loss: 17.191; l2dist: 3.956\n",
      "    step: 600; loss: 13.925; l2dist: 3.475\n",
      "    step: 700; loss: 17.215; l2dist: 3.850\n",
      "    step: 800; loss: 13.946; l2dist: 3.286\n",
      "    step: 900; loss: 14.818; l2dist: 3.460\n",
      "binary step: 8; number of successful adv: 10/10\n",
      "    step: 0; loss: 40795.305; l2dist: 0.000\n",
      "    step: 100; loss: 38.768; l2dist: 5.068\n",
      "    step: 200; loss: 35.894; l2dist: 4.787\n",
      "    step: 300; loss: 32.676; l2dist: 4.422\n",
      "    step: 400; loss: 30.111; l2dist: 4.156\n",
      "    step: 500; loss: 19.505; l2dist: 4.245\n",
      "    step: 600; loss: 15.081; l2dist: 3.623\n",
      "    step: 700; loss: 14.582; l2dist: 3.468\n",
      "    step: 800; loss: 13.791; l2dist: 3.328\n",
      "    step: 900; loss: 11.677; l2dist: 2.958\n",
      "binary step: 9; number of successful adv: 10/10\n",
      "    step: 0; loss: 99.975; l2dist: 0.000\n",
      "    step: 100; loss: 66.479; l2dist: 0.817\n",
      "    step: 200; loss: 66.104; l2dist: 0.764\n",
      "    step: 300; loss: 66.036; l2dist: 0.761\n",
      "    step: 400; loss: 65.956; l2dist: 0.739\n",
      "    step: 500; loss: 66.383; l2dist: 0.802\n",
      "    step: 600; loss: 66.102; l2dist: 0.779\n",
      "    step: 700; loss: 66.302; l2dist: 0.806\n",
      "    step: 800; loss: 66.143; l2dist: 0.788\n",
      "    step: 900; loss: 66.397; l2dist: 0.824\n",
      "binary step: 0; number of successful adv: 2/10\n",
      "    step: 0; loss: 809.789; l2dist: 0.000\n",
      "    step: 100; loss: 642.511; l2dist: 0.914\n",
      "    step: 200; loss: 642.062; l2dist: 0.853\n",
      "    step: 300; loss: 641.968; l2dist: 0.866\n",
      "    step: 400; loss: 642.187; l2dist: 0.898\n",
      "    step: 500; loss: 641.994; l2dist: 0.832\n",
      "    step: 600; loss: 642.056; l2dist: 0.862\n",
      "    step: 700; loss: 641.919; l2dist: 0.855\n",
      "    step: 800; loss: 642.179; l2dist: 0.898\n",
      "    step: 900; loss: 641.930; l2dist: 0.867\n",
      "binary step: 1; number of successful adv: 2/10\n",
      "    step: 0; loss: 8002.906; l2dist: 0.000\n",
      "    step: 100; loss: 4906.471; l2dist: 1.734\n",
      "    step: 200; loss: 4905.255; l2dist: 1.621\n",
      "    step: 300; loss: 4904.299; l2dist: 1.520\n",
      "    step: 400; loss: 4903.538; l2dist: 1.424\n",
      "    step: 500; loss: 4902.919; l2dist: 1.340\n",
      "    step: 600; loss: 4903.121; l2dist: 1.384\n",
      "    step: 700; loss: 4904.941; l2dist: 1.541\n",
      "    step: 800; loss: 4904.557; l2dist: 1.518\n",
      "    step: 900; loss: 4904.324; l2dist: 1.502\n",
      "binary step: 2; number of successful adv: 3/10\n",
      "    step: 0; loss: 70535.406; l2dist: 0.000\n",
      "    step: 100; loss: 36001.430; l2dist: 2.661\n",
      "    step: 200; loss: 36000.324; l2dist: 2.602\n",
      "    step: 300; loss: 35999.336; l2dist: 2.526\n",
      "    step: 400; loss: 35998.223; l2dist: 2.434\n",
      "    step: 500; loss: 35997.199; l2dist: 2.353\n",
      "    step: 600; loss: 35995.883; l2dist: 2.232\n",
      "    step: 700; loss: 35994.773; l2dist: 2.099\n",
      "    step: 800; loss: 35993.812; l2dist: 1.996\n",
      "    step: 900; loss: 35993.078; l2dist: 1.913\n",
      "binary step: 3; number of successful adv: 4/10\n",
      "    step: 0; loss: 605672.312; l2dist: 0.000\n",
      "    step: 100; loss: 89973.516; l2dist: 4.940\n",
      "    step: 200; loss: 89972.117; l2dist: 4.860\n",
      "    step: 300; loss: 89969.078; l2dist: 4.845\n",
      "    step: 400; loss: 89967.828; l2dist: 4.764\n",
      "    step: 500; loss: 89966.516; l2dist: 4.666\n",
      "    step: 600; loss: 89965.008; l2dist: 4.561\n",
      "    step: 700; loss: 89963.000; l2dist: 4.431\n",
      "    step: 800; loss: 89961.086; l2dist: 4.274\n",
      "    step: 900; loss: 89967.586; l2dist: 4.346\n",
      "binary step: 4; number of successful adv: 7/10\n",
      "    step: 0; loss: 3167398.250; l2dist: 0.000\n",
      "    step: 100; loss: 616038.938; l2dist: 5.204\n",
      "    step: 200; loss: 616030.375; l2dist: 5.118\n",
      "    step: 300; loss: 616018.438; l2dist: 5.154\n",
      "    step: 400; loss: 616000.875; l2dist: 5.140\n",
      "    step: 500; loss: 615997.562; l2dist: 5.136\n",
      "    step: 600; loss: 615995.875; l2dist: 5.060\n",
      "    step: 700; loss: 615994.375; l2dist: 4.992\n",
      "    step: 800; loss: 615992.562; l2dist: 4.876\n",
      "    step: 900; loss: 615990.875; l2dist: 4.746\n",
      "binary step: 5; number of successful adv: 8/10\n",
      "    step: 0; loss: 20685742.000; l2dist: 0.000\n",
      "    step: 100; loss: 3995681.500; l2dist: 6.029\n",
      "    step: 200; loss: 3995662.750; l2dist: 5.997\n",
      "    step: 300; loss: 3995657.500; l2dist: 5.942\n",
      "    step: 400; loss: 3995650.750; l2dist: 5.900\n",
      "    step: 500; loss: 3995647.250; l2dist: 5.867\n",
      "    step: 600; loss: 3995646.000; l2dist: 5.795\n",
      "    step: 700; loss: 3995645.250; l2dist: 5.734\n",
      "    step: 800; loss: 3995645.250; l2dist: 5.710\n",
      "    step: 900; loss: 3995644.000; l2dist: 5.639\n",
      "binary step: 6; number of successful adv: 8/10\n",
      "    step: 0; loss: 200346000.000; l2dist: 0.000\n",
      "    step: 100; loss: 39956184.000; l2dist: 6.225\n",
      "    step: 200; loss: 39956152.000; l2dist: 6.199\n",
      "    step: 300; loss: 39956152.000; l2dist: 6.169\n",
      "    step: 400; loss: 39956140.000; l2dist: 6.143\n",
      "    step: 500; loss: 39956136.000; l2dist: 6.138\n",
      "    step: 600; loss: 39956132.000; l2dist: 6.085\n",
      "    step: 700; loss: 39956132.000; l2dist: 6.059\n",
      "    step: 800; loss: 39956132.000; l2dist: 6.019\n",
      "    step: 900; loss: 39956132.000; l2dist: 5.978\n",
      "binary step: 7; number of successful adv: 8/10\n",
      "    step: 0; loss: 1999412224.000; l2dist: 0.000\n",
      "    step: 100; loss: 399560928.000; l2dist: 6.303\n",
      "    step: 200; loss: 399560928.000; l2dist: 6.270\n",
      "    step: 300; loss: 399560928.000; l2dist: 6.247\n",
      "    step: 400; loss: 399560928.000; l2dist: 6.224\n",
      "    step: 500; loss: 399560896.000; l2dist: 6.219\n",
      "    step: 600; loss: 399560928.000; l2dist: 6.196\n",
      "    step: 700; loss: 399560928.000; l2dist: 6.166\n",
      "    step: 800; loss: 399560928.000; l2dist: 6.152\n",
      "    step: 900; loss: 399560864.000; l2dist: 6.131\n",
      "binary step: 8; number of successful adv: 8/10\n",
      "    step: 0; loss: 200216368.000; l2dist: 0.000\n",
      "    step: 100; loss: 39956148.000; l2dist: 6.130\n",
      "    step: 200; loss: 39956148.000; l2dist: 6.104\n",
      "    step: 300; loss: 39956148.000; l2dist: 6.081\n",
      "    step: 400; loss: 39956136.000; l2dist: 6.061\n",
      "    step: 500; loss: 39956132.000; l2dist: 6.047\n",
      "    step: 600; loss: 39956132.000; l2dist: 6.012\n",
      "    step: 700; loss: 39956132.000; l2dist: 5.984\n",
      "    step: 800; loss: 39956124.000; l2dist: 5.955\n",
      "    step: 900; loss: 39956132.000; l2dist: 5.903\n",
      "binary step: 9; number of successful adv: 8/10\n",
      "    step: 0; loss: 94.825; l2dist: 0.000\n",
      "    step: 100; loss: 37.850; l2dist: 0.866\n",
      "    step: 200; loss: 38.478; l2dist: 1.005\n",
      "    step: 300; loss: 38.035; l2dist: 0.943\n",
      "    step: 400; loss: 38.031; l2dist: 0.879\n",
      "    step: 500; loss: 37.935; l2dist: 0.876\n",
      "    step: 600; loss: 37.913; l2dist: 0.914\n",
      "    step: 700; loss: 37.885; l2dist: 0.908\n",
      "    step: 800; loss: 37.902; l2dist: 0.904\n",
      "    step: 900; loss: 38.092; l2dist: 0.962\n",
      "binary step: 0; number of successful adv: 4/10\n",
      "    step: 0; loss: 602.534; l2dist: 0.000\n",
      "    step: 100; loss: 363.137; l2dist: 1.160\n",
      "    step: 200; loss: 362.036; l2dist: 0.972\n",
      "    step: 300; loss: 361.706; l2dist: 0.900\n",
      "    step: 400; loss: 361.751; l2dist: 0.949\n",
      "    step: 500; loss: 361.782; l2dist: 0.920\n",
      "    step: 600; loss: 361.635; l2dist: 0.906\n",
      "    step: 700; loss: 361.769; l2dist: 0.953\n",
      "    step: 800; loss: 362.046; l2dist: 1.016\n",
      "    step: 900; loss: 361.749; l2dist: 0.936\n",
      "binary step: 1; number of successful adv: 4/10\n",
      "    step: 0; loss: 5852.480; l2dist: 0.000\n",
      "    step: 100; loss: 2507.936; l2dist: 2.102\n",
      "    step: 200; loss: 2505.087; l2dist: 1.740\n",
      "    step: 300; loss: 2503.558; l2dist: 1.558\n",
      "    step: 400; loss: 2502.931; l2dist: 1.474\n",
      "    step: 500; loss: 2502.446; l2dist: 1.381\n",
      "    step: 600; loss: 2501.976; l2dist: 1.264\n",
      "    step: 700; loss: 2502.005; l2dist: 1.307\n",
      "    step: 800; loss: 2502.347; l2dist: 1.373\n",
      "    step: 900; loss: 2502.005; l2dist: 1.291\n",
      "binary step: 2; number of successful adv: 5/10\n",
      "    step: 0; loss: 49233.938; l2dist: 0.000\n",
      "    step: 100; loss: 16002.649; l2dist: 3.331\n",
      "    step: 200; loss: 16001.018; l2dist: 3.164\n",
      "    step: 300; loss: 15999.403; l2dist: 2.983\n",
      "    step: 400; loss: 15997.492; l2dist: 2.752\n",
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      "    step: 600; loss: 15994.403; l2dist: 2.396\n",
      "    step: 700; loss: 15993.247; l2dist: 2.227\n",
      "    step: 800; loss: 15992.022; l2dist: 2.043\n",
      "    step: 900; loss: 15991.259; l2dist: 1.942\n",
      "binary step: 3; number of successful adv: 6/10\n",
      "    step: 0; loss: 395239.938; l2dist: 0.000\n",
      "    step: 100; loss: 89915.648; l2dist: 4.202\n",
      "    step: 200; loss: 89914.539; l2dist: 4.112\n",
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      "    step: 800; loss: 89906.055; l2dist: 3.260\n",
      "    step: 900; loss: 40014.477; l2dist: 3.517\n",
      "binary step: 4; number of successful adv: 8/10\n",
      "    step: 0; loss: 2058467.625; l2dist: 0.000\n",
      "    step: 100; loss: 210792.438; l2dist: 4.891\n",
      "    step: 200; loss: 210791.297; l2dist: 4.838\n",
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      "    step: 800; loss: 100008.250; l2dist: 4.773\n",
      "    step: 900; loss: 100006.578; l2dist: 4.616\n",
      "binary step: 5; number of successful adv: 9/10\n",
      "    step: 0; loss: 10340694.000; l2dist: 0.000\n",
      "    step: 100; loss: 2004430.875; l2dist: 5.294\n",
      "    step: 200; loss: 2004428.875; l2dist: 5.146\n",
      "    step: 300; loss: 2004428.125; l2dist: 5.045\n",
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      "    step: 800; loss: 999854.438; l2dist: 5.484\n",
      "    step: 900; loss: 999853.500; l2dist: 5.416\n",
      "binary step: 6; number of successful adv: 9/10\n",
      "    step: 0; loss: 97754168.000; l2dist: 0.000\n",
      "    step: 100; loss: 19983422.000; l2dist: 5.257\n",
      "    step: 200; loss: 19983420.000; l2dist: 5.226\n",
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      "    step: 800; loss: 19974066.000; l2dist: 5.855\n",
      "    step: 900; loss: 19974064.000; l2dist: 5.824\n",
      "binary step: 7; number of successful adv: 9/10\n",
      "    step: 0; loss: 974195392.000; l2dist: 0.000\n",
      "    step: 100; loss: 99981944.000; l2dist: 5.899\n",
      "    step: 200; loss: 99981928.000; l2dist: 5.892\n",
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      "    step: 400; loss: 99981912.000; l2dist: 5.898\n",
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      "    step: 600; loss: 99981936.000; l2dist: 5.921\n",
      "    step: 700; loss: 99981912.000; l2dist: 5.907\n",
      "    step: 800; loss: 99981936.000; l2dist: 5.888\n",
      "    step: 900; loss: 99981912.000; l2dist: 5.869\n",
      "binary step: 8; number of successful adv: 9/10\n",
      "    step: 0; loss: 97644296.000; l2dist: 0.000\n",
      "    step: 100; loss: 19984544.000; l2dist: 5.200\n",
      "    step: 200; loss: 19984542.000; l2dist: 5.167\n",
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      "    step: 800; loss: 19975174.000; l2dist: 5.699\n",
      "    step: 900; loss: 19975062.000; l2dist: 5.781\n",
      "binary step: 9; number of successful adv: 9/10\n",
      "    step: 0; loss: 97.515; l2dist: 0.000\n",
      "    step: 100; loss: 51.944; l2dist: 1.002\n",
      "    step: 200; loss: 51.615; l2dist: 0.983\n",
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      "    step: 800; loss: 51.055; l2dist: 0.875\n",
      "    step: 900; loss: 51.026; l2dist: 0.864\n",
      "binary step: 0; number of successful adv: 3/10\n",
      "    step: 0; loss: 705.374; l2dist: 0.000\n",
      "    step: 100; loss: 364.265; l2dist: 1.377\n",
      "    step: 200; loss: 363.281; l2dist: 1.234\n",
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      "    step: 800; loss: 362.651; l2dist: 1.120\n",
      "    step: 900; loss: 362.854; l2dist: 1.175\n",
      "binary step: 1; number of successful adv: 4/10\n",
      "    step: 0; loss: 5985.637; l2dist: 0.000\n",
      "    step: 100; loss: 2513.529; l2dist: 2.677\n",
      "    step: 200; loss: 2510.731; l2dist: 2.406\n",
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      "    step: 800; loss: 2503.974; l2dist: 1.607\n",
      "    step: 900; loss: 2504.137; l2dist: 1.589\n",
      "binary step: 2; number of successful adv: 5/10\n",
      "    step: 0; loss: 49949.059; l2dist: 0.000\n",
      "    step: 100; loss: 4025.897; l2dist: 4.623\n",
      "    step: 200; loss: 4023.788; l2dist: 4.503\n",
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      "    step: 800; loss: 4014.371; l2dist: 3.483\n",
      "    step: 900; loss: 4011.725; l2dist: 3.215\n",
      "binary step: 3; number of successful adv: 8/10\n",
      "    step: 0; loss: 214101.312; l2dist: 0.000\n",
      "    step: 100; loss: 40006.898; l2dist: 4.806\n",
      "    step: 200; loss: 40005.742; l2dist: 4.707\n",
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      "    step: 900; loss: 39995.848; l2dist: 3.800\n",
      "binary step: 4; number of successful adv: 8/10\n",
      "    step: 0; loss: 1984499.125; l2dist: 0.000\n",
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      "    step: 200; loss: 399805.125; l2dist: 4.830\n",
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      "    step: 900; loss: 399800.906; l2dist: 4.445\n",
      "binary step: 5; number of successful adv: 8/10\n",
      "    step: 0; loss: 19752914.000; l2dist: 0.000\n",
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      "    step: 200; loss: 999857.250; l2dist: 5.793\n",
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      "    step: 800; loss: 999855.125; l2dist: 5.685\n",
      "    step: 900; loss: 999854.625; l2dist: 5.673\n",
      "binary step: 6; number of successful adv: 9/10\n",
      "    step: 0; loss: 104179688.000; l2dist: 0.000\n",
      "    step: 100; loss: 9998535.000; l2dist: 6.088\n",
      "    step: 200; loss: 9998443.000; l2dist: 6.061\n",
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      "    step: 900; loss: 9998228.000; l2dist: 5.893\n",
      "binary step: 7; number of successful adv: 9/10\n",
      "    step: 0; loss: 990665984.000; l2dist: 0.000\n",
      "    step: 100; loss: 99981936.000; l2dist: 6.096\n",
      "    step: 200; loss: 99981936.000; l2dist: 6.075\n",
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      "    step: 900; loss: 99981936.000; l2dist: 5.950\n",
      "binary step: 8; number of successful adv: 9/10\n",
      "    step: 0; loss: 101957648.000; l2dist: 0.000\n",
      "    step: 100; loss: 9998236.000; l2dist: 6.029\n",
      "    step: 200; loss: 9998228.000; l2dist: 5.959\n",
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      "    step: 900; loss: 9998228.000; l2dist: 5.789\n",
      "binary step: 9; number of successful adv: 9/10\n",
      "    step: 0; loss: 99.589; l2dist: 0.000\n",
      "    step: 100; loss: 66.095; l2dist: 0.739\n",
      "    step: 200; loss: 65.848; l2dist: 0.710\n",
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      "    step: 800; loss: 66.002; l2dist: 0.758\n",
      "    step: 900; loss: 65.796; l2dist: 0.720\n",
      "binary step: 0; number of successful adv: 2/10\n",
      "    step: 0; loss: 808.180; l2dist: 0.000\n",
      "    step: 100; loss: 495.136; l2dist: 1.314\n",
      "    step: 200; loss: 494.285; l2dist: 1.244\n",
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      "    step: 800; loss: 493.137; l2dist: 1.077\n",
      "    step: 900; loss: 492.988; l2dist: 1.064\n",
      "binary step: 1; number of successful adv: 3/10\n",
      "    step: 0; loss: 7044.934; l2dist: 0.000\n",
      "    step: 100; loss: 2518.002; l2dist: 2.993\n",
      "    step: 200; loss: 2514.442; l2dist: 2.599\n",
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      "binary step: 2; number of successful adv: 5/10\n",
      "    step: 0; loss: 51028.469; l2dist: 0.000\n",
      "    step: 100; loss: 16027.893; l2dist: 3.995\n",
      "    step: 200; loss: 16025.377; l2dist: 3.834\n",
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      "    step: 800; loss: 16012.949; l2dist: 2.660\n",
      "    step: 900; loss: 16011.264; l2dist: 2.468\n",
      "binary step: 3; number of successful adv: 6/10\n",
      "    step: 0; loss: 405328.062; l2dist: 0.000\n",
      "    step: 100; loss: 90050.258; l2dist: 5.279\n",
      "    step: 200; loss: 90043.328; l2dist: 5.209\n",
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      "    step: 800; loss: 90026.891; l2dist: 4.288\n",
      "    step: 900; loss: 90023.969; l2dist: 4.061\n",
      "binary step: 4; number of successful adv: 7/10\n",
      "    step: 0; loss: 3052351.000; l2dist: 0.000\n",
      "    step: 100; loss: 400058.219; l2dist: 6.046\n",
      "    step: 200; loss: 400050.875; l2dist: 6.081\n",
      "    step: 300; loss: 400046.219; l2dist: 6.088\n",
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      "    step: 800; loss: 400039.000; l2dist: 5.726\n",
      "    step: 900; loss: 400037.312; l2dist: 5.612\n",
      "binary step: 5; number of successful adv: 8/10\n",
      "    step: 0; loss: 20542318.000; l2dist: 0.000\n",
      "    step: 100; loss: 4000045.250; l2dist: 6.127\n",
      "    step: 200; loss: 4000033.500; l2dist: 6.148\n",
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      "    step: 800; loss: 4000022.000; l2dist: 6.072\n",
      "    step: 900; loss: 4000020.750; l2dist: 6.017\n",
      "binary step: 6; number of successful adv: 8/10\n",
      "    step: 0; loss: 199933184.000; l2dist: 0.000\n",
      "    step: 100; loss: 39999868.000; l2dist: 6.068\n",
      "    step: 200; loss: 39999832.000; l2dist: 6.098\n",
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      "    step: 900; loss: 39999772.000; l2dist: 6.107\n",
      "binary step: 7; number of successful adv: 8/10\n",
      "    step: 0; loss: 1996087424.000; l2dist: 0.000\n",
      "    step: 100; loss: 399997664.000; l2dist: 6.125\n",
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      "    step: 800; loss: 399997216.000; l2dist: 6.196\n",
      "    step: 900; loss: 399997216.000; l2dist: 6.197\n",
      "binary step: 8; number of successful adv: 8/10\n",
      "    step: 0; loss: 199814976.000; l2dist: 0.000\n",
      "    step: 100; loss: 39999816.000; l2dist: 6.159\n",
      "    step: 200; loss: 39999788.000; l2dist: 6.202\n",
      "    step: 300; loss: 39999788.000; l2dist: 6.196\n",
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      "    step: 500; loss: 39999784.000; l2dist: 6.197\n",
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      "    step: 700; loss: 39999780.000; l2dist: 6.193\n",
      "    step: 800; loss: 39999772.000; l2dist: 6.190\n",
      "    step: 900; loss: 39999772.000; l2dist: 6.182\n",
      "binary step: 9; number of successful adv: 8/10\n",
      "    step: 0; loss: 99.972; l2dist: 0.000\n",
      "    step: 100; loss: 82.672; l2dist: 0.339\n",
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      "    step: 800; loss: 82.685; l2dist: 0.392\n",
      "    step: 900; loss: 82.686; l2dist: 0.385\n",
      "binary step: 0; number of successful adv: 1/10\n",
      "    step: 0; loss: 904.787; l2dist: 0.000\n",
      "    step: 100; loss: 366.467; l2dist: 1.650\n",
      "    step: 200; loss: 365.333; l2dist: 1.503\n",
      "    step: 300; loss: 365.122; l2dist: 1.484\n",
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      "    step: 800; loss: 365.029; l2dist: 1.506\n",
      "    step: 900; loss: 364.374; l2dist: 1.345\n",
      "binary step: 1; number of successful adv: 4/10\n",
      "    step: 0; loss: 6166.167; l2dist: 0.000\n",
      "    step: 100; loss: 1616.219; l2dist: 3.013\n",
      "    step: 200; loss: 1614.248; l2dist: 2.834\n",
      "    step: 300; loss: 1612.950; l2dist: 2.690\n",
      "    step: 400; loss: 1611.163; l2dist: 2.433\n",
      "    step: 500; loss: 1610.290; l2dist: 2.370\n",
      "    step: 600; loss: 1609.648; l2dist: 2.300\n",
      "    step: 700; loss: 1608.904; l2dist: 2.215\n",
      "    step: 800; loss: 1608.849; l2dist: 2.214\n",
      "    step: 900; loss: 1609.206; l2dist: 2.320\n",
      "binary step: 2; number of successful adv: 6/10\n",
      "    step: 0; loss: 41190.215; l2dist: 0.000\n",
      "    step: 100; loss: 9026.701; l2dist: 4.039\n",
      "    step: 200; loss: 9023.872; l2dist: 3.887\n",
      "    step: 300; loss: 9019.952; l2dist: 3.605\n",
      "    step: 400; loss: 9017.402; l2dist: 3.407\n",
      "    step: 500; loss: 9015.175; l2dist: 3.160\n",
      "    step: 600; loss: 9012.993; l2dist: 3.147\n",
      "    step: 700; loss: 9011.026; l2dist: 2.903\n",
      "    step: 800; loss: 9010.788; l2dist: 2.886\n",
      "    step: 900; loss: 9012.424; l2dist: 3.046\n",
      "binary step: 3; number of successful adv: 7/10\n",
      "    step: 0; loss: 306147.438; l2dist: 0.000\n",
      "    step: 100; loss: 10037.879; l2dist: 5.587\n",
      "    step: 200; loss: 10034.830; l2dist: 5.365\n",
      "    step: 300; loss: 10031.563; l2dist: 5.204\n",
      "    step: 400; loss: 10029.419; l2dist: 5.092\n",
      "    step: 500; loss: 10026.603; l2dist: 4.900\n",
      "    step: 600; loss: 10024.853; l2dist: 4.806\n",
      "    step: 700; loss: 10023.101; l2dist: 4.639\n",
      "    step: 800; loss: 10021.804; l2dist: 4.433\n",
      "    step: 900; loss: 10019.725; l2dist: 4.242\n",
      "binary step: 4; number of successful adv: 9/10\n",
      "    step: 0; loss: 1113455.875; l2dist: 0.000\n",
      "    step: 100; loss: 100022.930; l2dist: 5.725\n",
      "    step: 200; loss: 100020.500; l2dist: 5.662\n",
      "    step: 300; loss: 100019.078; l2dist: 5.572\n",
      "    step: 400; loss: 100017.164; l2dist: 5.436\n",
      "    step: 500; loss: 100015.867; l2dist: 5.336\n",
      "    step: 600; loss: 100014.719; l2dist: 5.271\n",
      "    step: 700; loss: 100013.281; l2dist: 5.143\n",
      "    step: 800; loss: 100010.398; l2dist: 5.019\n",
      "    step: 900; loss: 100009.875; l2dist: 4.981\n",
      "binary step: 5; number of successful adv: 9/10\n",
      "    step: 0; loss: 10065034.000; l2dist: 0.000\n",
      "    step: 100; loss: 999860.000; l2dist: 5.997\n",
      "    step: 200; loss: 999859.312; l2dist: 5.957\n",
      "    step: 300; loss: 999858.875; l2dist: 5.924\n",
      "    step: 400; loss: 999858.750; l2dist: 5.941\n",
      "    step: 500; loss: 999858.438; l2dist: 5.888\n",
      "    step: 600; loss: 999857.812; l2dist: 5.897\n",
      "    step: 700; loss: 999857.812; l2dist: 5.901\n",
      "    step: 800; loss: 999856.938; l2dist: 5.807\n",
      "    step: 900; loss: 999855.438; l2dist: 5.692\n",
      "binary step: 6; number of successful adv: 9/10\n",
      "    step: 0; loss: 100020072.000; l2dist: 0.000\n",
      "    step: 100; loss: 9998230.000; l2dist: 6.163\n",
      "    step: 200; loss: 9998230.000; l2dist: 6.093\n",
      "    step: 300; loss: 9998230.000; l2dist: 6.054\n",
      "    step: 400; loss: 9998231.000; l2dist: 6.026\n",
      "    step: 500; loss: 9998231.000; l2dist: 6.003\n",
      "    step: 600; loss: 9998229.000; l2dist: 5.980\n",
      "    step: 700; loss: 9998231.000; l2dist: 6.001\n",
      "    step: 800; loss: 9998230.000; l2dist: 6.092\n",
      "    step: 900; loss: 9998229.000; l2dist: 6.038\n",
      "binary step: 7; number of successful adv: 9/10\n",
      "    step: 0; loss: 999790016.000; l2dist: 0.000\n",
      "    step: 100; loss: 100099272.000; l2dist: 6.257\n",
      "    step: 200; loss: 99981896.000; l2dist: 6.298\n",
      "    step: 300; loss: 46.190; l2dist: 6.707\n",
      "    step: 400; loss: 46.115; l2dist: 6.701\n",
      "    step: 500; loss: 46.008; l2dist: 6.693\n",
      "    step: 600; loss: 45.847; l2dist: 6.680\n",
      "    step: 700; loss: 45.580; l2dist: 6.660\n",
      "    step: 800; loss: 45.328; l2dist: 6.640\n",
      "    step: 900; loss: 45.037; l2dist: 6.617\n",
      "binary step: 8; number of successful adv: 10/10\n",
      "    step: 0; loss: 999790016.000; l2dist: 0.000\n",
      "    step: 100; loss: 100098592.000; l2dist: 6.267\n",
      "    step: 200; loss: 99981904.000; l2dist: 6.286\n",
      "    step: 300; loss: 44.497; l2dist: 6.581\n",
      "    step: 400; loss: 46.385; l2dist: 6.723\n",
      "    step: 500; loss: 46.340; l2dist: 6.719\n",
      "    step: 600; loss: 46.274; l2dist: 6.713\n",
      "    step: 700; loss: 46.184; l2dist: 6.705\n",
      "    step: 800; loss: 46.067; l2dist: 6.695\n",
      "    step: 900; loss: 45.917; l2dist: 6.683\n",
      "binary step: 9; number of successful adv: 10/10\n"
     ]
    }
   ],
   "source": [
    "attack = CWL2_NCA_Attack()\n",
    "\n",
    "def attack_batch(x, y, batch_size):\n",
    "    x_adv = torch.zeros_like(x)\n",
    "    total_num = x.size(0)\n",
    "    num_batches = total_num // batch_size\n",
    "    for i in range(num_batches):\n",
    "        begin = i * batch_size\n",
    "        end = (i + 1) * batch_size\n",
    "        x_adv[begin:end] = attack(\n",
    "            net, x[begin:end], y[begin:end], targeted=False,\n",
    "            binary_search_steps=10, max_iterations=1000,\n",
    "            confidence=0, learning_rate=1e-1,\n",
    "            initial_const=1e2, abort_early=False)\n",
    "    return x_adv\n",
    "\n",
    "x_adv = attack_batch(x_test[ind][:100].cuda(), y_test[ind][:100].cuda(), 10)\n",
    "# x_adv = attack_batch(x_test[ind][:2].cuda(), y_test[ind][:2].cuda(), 2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0.06\n"
     ]
    }
   ],
   "source": [
    "logits_adv = net.compute_logits(x_adv)\n",
    "adv_ind = np.where(logits_adv.argmax(1).cpu() != y_test[ind][:100])[0]\n",
    "acc = (logits_adv.argmax(1).cpu() == y_test[ind][:100]).float().numpy().mean()\n",
    "print(acc)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "3.8527956008911133"
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "(x_adv.cpu() - x_test[ind][:100]).view(100, -1).norm(2, 1)[adv_ind].mean().item()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 57,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "tensor([3.9654, 3.8659, 3.1585, 3.0521, 2.9116, 3.3393, 2.9059, 1.8409, 2.2006,\n",
       "        0.7463, 3.4531, 3.1243, 0.8316, 3.7189, 3.6752, 0.7141, 7.3174, 2.4736,\n",
       "        2.6379, 0.7758, 2.9101, 3.1342, 2.2701, 1.7538, 4.3092, 3.3058, 2.4876,\n",
       "        3.0321, 3.2119, 2.9654, 2.8977, 3.0509, 1.7986, 3.6026, 4.0228, 2.4388,\n",
       "        3.4142, 2.0723, 3.5873, 3.0175, 2.1664, 3.5316, 1.2208, 1.7057, 0.4346,\n",
       "        2.6737, 2.9177, 2.0605, 4.4492, 2.4814, 2.9824, 2.9804, 0.7697, 3.8104,\n",
       "        2.9644, 5.1662, 3.4106, 1.8374, 1.8860, 3.6003, 0.8830, 0.4588, 0.8025,\n",
       "        3.0817, 0.4208, 2.6848, 2.7654, 2.5435, 3.8160, 6.4778, 4.8342, 3.9204,\n",
       "        0.1647, 3.3441, 2.3854, 3.0777, 1.4431, 1.2292, 4.3104, 2.7652, 0.0000,\n",
       "        2.8042, 3.1115, 4.7793, 3.3844, 3.4500, 4.5450, 3.6714, 3.1127, 4.6415,\n",
       "        0.5048, 2.6473, 3.1263, 2.6982, 2.6311, 2.5629, 2.4965, 1.4889, 3.0107,\n",
       "        4.4997], grad_fn=<NormBackward1>)"
      ]
     },
     "execution_count": 57,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "(x_adv.cpu() - x_test[ind][:100]).view(100, -1).norm(2, 1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 58,
   "metadata": {},
   "outputs": [],
   "source": [
    "x_nca = x_adv.clone()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 55,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.image.AxesImage at 0x7f1284837438>"
      ]
     },
     "execution_count": 55,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.imshow(x_adv.cpu().detach().numpy()[1].reshape(28, 28))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.image.AxesImage at 0x7f1284b14f28>"
      ]
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.imshow(x_test.cpu().detach().numpy()[1].reshape(28, 28))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [],
   "source": [
    "INFTY = 1e20\n",
    "\n",
    "\n",
    "class DKNN_PGD(object):\n",
    "    \"\"\"\n",
    "    \"\"\"\n",
    "\n",
    "    def __init__(self, dknn):\n",
    "        self.dknn = dknn\n",
    "        self.device = dknn.device\n",
    "        self.layers = dknn.layers\n",
    "        self.guide_reps = {}\n",
    "        self.thres = None\n",
    "        self.coeff = None\n",
    "\n",
    "    def __call__(self, x_orig, label, guide_layer, m, epsilon=0.1,\n",
    "                 max_epsilon=0.3, max_iterations=1000, num_restart=1,\n",
    "                 rand_start=True, thres_steps=100, check_adv_steps=100,\n",
    "                 verbose=True):\n",
    "        \"\"\"\n",
    "        x_orig is tensor (requires_grad=False)\n",
    "        \"\"\"\n",
    "\n",
    "        # make sure we run at least once\n",
    "        if num_restart < 1:\n",
    "            num_restart = 1\n",
    "\n",
    "        # if not using randomized start, no point in doing more than one start\n",
    "        if not rand_start:\n",
    "            num_restart = 1\n",
    "\n",
    "        label = label.cpu().numpy()\n",
    "        batch_size = x_orig.size(0)\n",
    "        min_, max_ = x_orig.min(), x_orig.max()\n",
    "        x_adv = x_orig.detach()\n",
    "        best_num_nn = np.zeros((batch_size, ))\n",
    "        \n",
    "        self.coeff = torch.zeros((x_orig.size(0), m))\n",
    "        self.coeff[:, :m // 2] += 1\n",
    "        self.coeff[:, m // 2:] -= 1\n",
    "        \n",
    "        for i in range(num_restart):\n",
    "\n",
    "            # initialize perturbation\n",
    "            delta = torch.zeros_like(x_adv)\n",
    "            if rand_start:\n",
    "                delta.uniform_(- max_epsilon * 0.1, max_epsilon * 0.1)\n",
    "            delta.requires_grad_()\n",
    "\n",
    "            for iteration in range(max_iterations):\n",
    "                x = torch.clamp(x_orig + delta, min_, max_)\n",
    "\n",
    "                # adaptively choose threshold and guide samples every\n",
    "                # <thres_steps> iterations\n",
    "                with torch.no_grad():\n",
    "                    if iteration % thres_steps == 0:\n",
    "                        thres = self.dknn.get_neighbors(x)[0][0][:, -1]\n",
    "                        self.thres = torch.tensor(thres).to(self.device).view(\n",
    "                            batch_size, 1)\n",
    "                        self.find_guide_samples(\n",
    "                            x, label, m=m, layer=guide_layer)\n",
    "\n",
    "                reps = self.dknn.get_activations(x, requires_grad=True)\n",
    "                loss = self.loss_function(reps)\n",
    "                loss.backward()\n",
    "                # perform update on delta\n",
    "                with torch.no_grad():\n",
    "#                     import pdb; pdb.set_trace()\n",
    "                    delta -= epsilon * delta.grad.detach().sign()\n",
    "#                     delta -= epsilon * delta.grad.detach()\n",
    "                    delta.clamp_(- max_epsilon, max_epsilon)\n",
    "\n",
    "                if (verbose and iteration % (np.ceil(max_iterations / 10)) == 0):\n",
    "                    print('    step: %d; loss: %.3f' %\n",
    "                          (iteration, loss.cpu().detach().numpy()))\n",
    "                \n",
    "                if ((iteration + 1) % check_adv_steps == 0 or\n",
    "                        iteration == max_iterations):\n",
    "                    with torch.no_grad():\n",
    "                        # check if x are adversarial. Only store adversarial examples\n",
    "                        # if they have a larger number of wrong neighbors than orevious\n",
    "                        is_adv, num_nn = self.check_adv(x, label)\n",
    "                        for j in range(batch_size):\n",
    "                            if is_adv[j] and num_nn[j] > best_num_nn[j]:\n",
    "                                x_adv[j] = x[j]\n",
    "                                best_num_nn[j] = num_nn[j]\n",
    "\n",
    "            with torch.no_grad():\n",
    "                is_adv, _ = self.check_adv(x_adv, label)\n",
    "            if verbose:\n",
    "                print('number of successful adv: %d/%d' % (is_adv.sum(), batch_size))\n",
    "\n",
    "        return x_adv\n",
    "\n",
    "    def check_adv(self, x, label):\n",
    "        \"\"\"Check if label of <x> predicted by <dknn> matches with <label>\"\"\"\n",
    "        output = self.dknn.classify(x)\n",
    "        num_nn = output.max(1)\n",
    "        y_pred = output.argmax(1)\n",
    "        is_adv = (y_pred != label).astype(np.float32)\n",
    "#         import pdb; pdb.set_trace()\n",
    "        return is_adv, num_nn\n",
    "\n",
    "    def loss_function(self, reps):\n",
    "        \"\"\"Returns the loss averaged over the batch (first dimension of x) and\n",
    "        L-2 norm squared of the perturbation\n",
    "        \"\"\"\n",
    "\n",
    "        batch_size = reps[self.layers[0]].size(0)\n",
    "        adv_loss = torch.zeros(\n",
    "            (batch_size, len(self.layers)), device=self.device)\n",
    "        # find squared L-2 distance between original samples and their\n",
    "        # adversarial examples at each layer\n",
    "        for l, layer in enumerate(self.layers):\n",
    "            rep = reps[layer].view(batch_size, 1, -1)\n",
    "            dist = ((rep - self.guide_reps[layer])**2).sum(2)\n",
    "#             fx = self.thres - dist\n",
    "#             Fx = torch.max(torch.tensor(0., device=self.device),\n",
    "#                            self.coeff.to(self.device) * fx).sum(1)\n",
    "#             Fx = (- self.coeff.to(self.device) * dist).sum(1)\n",
    "            Fx = dist[:, 1]\n",
    "            adv_loss[:, l] = Fx\n",
    "\n",
    "        return adv_loss.mean()\n",
    "\n",
    "    def find_guide_samples(self, x, label, m=100, layer='relu1'):\n",
    "        \"\"\"Find k nearest neighbors to <x> that all have the same class but not\n",
    "        equal to <label>\n",
    "        \"\"\"\n",
    "        num_classes = self.dknn.num_classes\n",
    "        x_train = self.dknn.x_train\n",
    "        y_train = self.dknn.y_train\n",
    "        batch_size = x.size(0)\n",
    "        nn = torch.zeros((m, ) + x.size()).transpose(0, 1)\n",
    "        D, I = self.dknn.get_neighbors(\n",
    "            x, k=x_train.size(0), layers=[layer])[0]\n",
    "        y_pred = self.dknn.classify(x_train).argmax(1)\n",
    "        is_correct = y_pred == y_train.numpy()\n",
    "\n",
    "        for i, (d, ind) in enumerate(zip(D, I)):\n",
    "#             mean_dist = np.zeros((num_classes, ))\n",
    "#             for j in range(num_classes):\n",
    "#                 mean_dist[j] = np.mean(\n",
    "#                     d[np.where(y_train[ind] == j)[0]][:m // 2])\n",
    "#             mean_dist[label[i]] += INFTY\n",
    "#             nearest_label = mean_dist.argmin()\n",
    "#             nn_ind = np.where(y_train[ind] == nearest_label)[0][:m // 2]\n",
    "#             nn[i, m // 2:] = x_train[ind[nn_ind]]\n",
    "#             nn_ind = np.where(y_train[ind] == label[i])[0][:m // 2]\n",
    "#             nn[i, :m // 2] = x_train[ind[nn_ind]]\n",
    "            # find nearest sample that is correctly classified as j\n",
    "#             import pdb; pdb.set_trace()\n",
    "            is_not_label = y_train.numpy() != label[i]\n",
    "            idx = np.where(is_correct & is_not_label)[0]\n",
    "            nn_ind = (x_train - x[i].cpu())[idx].view(\n",
    "                idx.shape[0], -1).norm(2, 1).argmin()\n",
    "            nn[i, 1] = x_train[idx][nn_ind]\n",
    "\n",
    "        # initialize self.guide_reps if empty\n",
    "        if not self.guide_reps:\n",
    "            guide_rep = self.dknn.get_activations(\n",
    "                nn[0], requires_grad=False)\n",
    "            for l in self.layers:\n",
    "                # set a zero tensor before filling it\n",
    "                size = (batch_size, ) + guide_rep[l].view(m, -1).size()\n",
    "                self.guide_reps[l] = torch.zeros(size, device=self.device)\n",
    "\n",
    "        # fill self.guide_reps\n",
    "        for i in range(batch_size):\n",
    "            guide_rep = self.dknn.get_activations(\n",
    "                nn[i], requires_grad=False)\n",
    "            self.guide_reps[layer][i] = guide_rep[layer].view(\n",
    "                m, -1).detach()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "    step: 0; loss: 2257.268\n",
      "    step: 200; loss: 1195.299\n",
      "    step: 400; loss: 1519.205\n",
      "    step: 600; loss: 1286.897\n",
      "    step: 800; loss: 978.852\n",
      "    step: 1000; loss: 791.657\n",
      "    step: 1200; loss: 660.375\n",
      "    step: 1400; loss: 611.989\n",
      "    step: 1600; loss: 573.965\n",
      "    step: 1800; loss: 537.115\n",
      "number of successful adv: 189/200\n"
     ]
    }
   ],
   "source": [
    "attack = DKNN_PGD(dknn)\n",
    "\n",
    "def attack_batch(x, y, batch_size):\n",
    "    x_adv = torch.zeros_like(x)\n",
    "    total_num = x.size(0)\n",
    "    num_batches = total_num // batch_size\n",
    "    for i in range(num_batches):\n",
    "        begin = i * batch_size\n",
    "        end = (i + 1) * batch_size\n",
    "        x_adv[begin:end] = attack(\n",
    "            x[begin:end], y[begin:end],\n",
    "            guide_layer=layers[0], m=2, epsilon=0.001,\n",
    "            max_epsilon=0.3, max_iterations=2000, num_restart=10,\n",
    "            rand_start=False, thres_steps=200, check_adv_steps=200,\n",
    "            verbose=True)\n",
    "    return x_adv\n",
    "\n",
    "num = 200\n",
    "x_adv = attack_batch(x_test[ind][:num].cuda(), y_test[ind][:num], 200)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 58,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Parameter containing:\n",
       "tensor(0.0246, device='cuda:0')"
      ]
     },
     "execution_count": 58,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "net.log_it"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
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  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.6.5"
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