tutorials/Part 09 bis - Converting a model to integers and impact (extended results).ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Functional Encryption - Classification and information leakage\n",
    "\n",
    " \n",
    "We do the same operation than in Part 9 with a model which has been more trained to resist against adversaries"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# 9 Converting the model to integers (Extended results)\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Load torch and syft packages"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Allow to load packages from parent\n",
    "import sys, os\n",
    "sys.path.insert(1, os.path.realpath(os.path.pardir))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [],
   "source": [
    "import torch"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [],
   "source": [
    "from math import log2, ceil\n",
    "\n",
    "import torch.nn as nn\n",
    "import torch.nn.functional as F\n",
    "import torch.utils.data as utils\n",
    "\n",
    "import learn\n",
    "from learn import collateral"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [],
   "source": [
    "PRIVATE_OUTPUT_SIZE = 4\n",
    "N_CHARS = 10\n",
    "N_FONTS = 2"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "fonts = ['cursive', 'Georgia']\n",
    "letter = \"6\""
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Let's load the quadratic model that we saved in Part 8! _Be sure that the path and file name match._"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {},
   "outputs": [],
   "source": [
    "class CollateralNet(nn.Module):\n",
    "    def __init__(self, private_output_size):\n",
    "        super(CollateralNet, self).__init__()\n",
    "        self.proj1 = nn.Linear(784, 40)\n",
    "        self.diag1 = nn.Linear(40, private_output_size, bias=False)\n",
    "\n",
    "        # --- FFN for characters\n",
    "        self.lin1 = nn.Linear(private_output_size, 32)\n",
    "        self.lin2 = nn.Linear(32, N_CHARS)\n",
    "\n",
    "        # --- Junction\n",
    "        self.jct = nn.Linear(private_output_size, 784)\n",
    "\n",
    "        # --- CNN for families\n",
    "        self.conv1 = nn.Conv2d(1, 20, 5, 1)\n",
    "        self.conv2 = nn.Conv2d(20, 50, 5, 1)\n",
    "        self.fc1 = nn.Linear(4 * 4 * 50, 500)\n",
    "        self.fc2 = nn.Linear(500, N_FONTS)\n",
    "        \n",
    "        # FFN for families\n",
    "        self.lin3 = nn.Linear(private_output_size, 64)\n",
    "        self.lin4 = nn.Linear(64, 32)\n",
    "        self.lin5 = nn.Linear(32, 16)\n",
    "        self.lin6 = nn.Linear(16, 8)\n",
    "        self.lin7 = nn.Linear(8, N_CHARS)\n",
    "\n",
    "    def quad(self, x):\n",
    "        # --- Quadratic\n",
    "        x = x.view(-1, 784)\n",
    "        x = self.proj1(x)\n",
    "        x = x * x\n",
    "        x = self.diag1(x)\n",
    "        return x\n",
    "\n",
    "    def char_net(self, x):\n",
    "        # --- FFN\n",
    "        x = F.relu(x)\n",
    "        x = F.relu(self.lin1(x))\n",
    "        x = self.lin2(x)\n",
    "        return x\n",
    "\n",
    "    def font_net(self, x):\n",
    "        # --- Junction\n",
    "        x = self.jct(x)\n",
    "        x = x.view(-1, 1, 28, 28)\n",
    "\n",
    "        # --- CNN\n",
    "        x = F.relu(self.conv1(x))\n",
    "        x = F.max_pool2d(x, 2, 2)\n",
    "        x = F.relu(self.conv2(x))\n",
    "        x = F.max_pool2d(x, 2, 2)\n",
    "        x = x.view(-1, 4 * 4 * 50)\n",
    "        x = F.relu(self.fc1(x))\n",
    "        x = self.fc2(x)\n",
    "        return x\n",
    "\n",
    "    def forward_char(self, x):\n",
    "        x = self.quad(x)\n",
    "        x = self.char_net(x)\n",
    "        return F.log_softmax(x, dim=1)\n",
    "\n",
    "    def forward_font(self, x):\n",
    "        x = self.quad(x)\n",
    "        x = self.font_net(x)\n",
    "        return F.log_softmax(x, dim=1)\n",
    "    \n",
    "    # We add the ability to freeze some layers to ensure that the collateral task does\n",
    "    # not modify the quadratic net\n",
    "    \n",
    "    def get_params(self, net):\n",
    "        \"\"\"Select the params for a given part of the net\"\"\"\n",
    "        if net == 'quad':\n",
    "            layers = [self.proj1, self.diag1]\n",
    "        elif net == 'char':\n",
    "            layers = [self.lin1, self.lin2]\n",
    "        elif net == 'font':\n",
    "            layers = [self.jct, self.fc1, self.fc2, self.conv1, self.conv2]\n",
    "        else:\n",
    "            raise AttributeError(f'{net} type not recognized')\n",
    "        params = [p for layer in layers for p in layer.parameters()]\n",
    "        return params\n",
    "\n",
    "    def freeze(self, net):\n",
    "        \"\"\"Freeze a part of the net\"\"\"\n",
    "        net_params = self.get_params(net)\n",
    "        for param in net_params:\n",
    "            param.requires_grad = False\n",
    "\n",
    "    def unfreeze(self):\n",
    "        \"\"\"Unfreeze the net\"\"\"\n",
    "        for param in self.parameters():\n",
    "            param.requires_grad = True"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "CollateralNet(\n",
       "  (proj1): Linear(in_features=784, out_features=40, bias=True)\n",
       "  (diag1): Linear(in_features=40, out_features=4, bias=False)\n",
       "  (lin1): Linear(in_features=4, out_features=32, bias=True)\n",
       "  (lin2): Linear(in_features=32, out_features=10, bias=True)\n",
       "  (jct): Linear(in_features=4, out_features=784, bias=True)\n",
       "  (conv1): Conv2d(1, 20, kernel_size=(5, 5), stride=(1, 1))\n",
       "  (conv2): Conv2d(20, 50, kernel_size=(5, 5), stride=(1, 1))\n",
       "  (fc1): Linear(in_features=800, out_features=500, bias=True)\n",
       "  (fc2): Linear(in_features=500, out_features=2, bias=True)\n",
       "  (lin3): Linear(in_features=4, out_features=64, bias=True)\n",
       "  (lin4): Linear(in_features=64, out_features=32, bias=True)\n",
       "  (lin5): Linear(in_features=32, out_features=16, bias=True)\n",
       "  (lin6): Linear(in_features=16, out_features=8, bias=True)\n",
       "  (lin7): Linear(in_features=8, out_features=10, bias=True)\n",
       ")"
      ]
     },
     "execution_count": 36,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#path = '../data/models/quadconvnet_part8.pt'\n",
    "path = 'models/quadconvnet_0.5_0.002_1.7_4_0_par2.pt'\n",
    "model = CollateralNet(private_output_size=PRIVATE_OUTPUT_SIZE)\n",
    "results = {}\n",
    "\n",
    "model.load_state_dict(torch.load(path))\n",
    "model.eval()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Here is a function to analyse a tensor distribution"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "metadata": {},
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "import seaborn as sns\n",
    "import matplotlib.pyplot as plt\n",
    "\n",
    "def print_hist(data):\n",
    "    x = data.view(-1).abs()\n",
    "    # the histogram of the data\n",
    "    n, bins, patches = plt.hist(x, 50, density=True, facecolor='g', alpha=0.75)\n",
    "    plt.xlabel('Weight amplitude')\n",
    "    plt.ylabel('Probability')\n",
    "    plt.title('Weight amplitude distribution')\n",
    "    plt.grid(True)\n",
    "    plt.show()\n",
    "    \n",
    "    \n",
    "def print_data(data):\n",
    "    ax = sns.heatmap(data, linewidth=0.5)\n",
    "    plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "And our function to convert tensors to integers with a precision parameter, and vice-versa"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "metadata": {},
   "outputs": [],
   "source": [
    "def fix_precision(tensor, precision_bits, rm_outlier_frac=100, parameter=True):\n",
    "    tensor = (tensor * 2**precision_bits).long()\n",
    "    max_value = max(\n",
    "        np.abs(np.percentile(tensor, rm_outlier_frac)),\n",
    "        np.abs(np.percentile(tensor, 100 - rm_outlier_frac))\n",
    "    )\n",
    "    cp_tensor = 1 * tensor\n",
    "    tensor = tensor.clamp(min=-max_value, max=max_value)\n",
    "    if parameter:\n",
    "        return nn.Parameter(tensor, requires_grad=False)\n",
    "    else:\n",
    "        return tensor\n",
    "    \n",
    "def float_precision(tensor, precision_bits):\n",
    "    tensor = tensor.float()/2**precision_bits\n",
    "    return tensor"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "metadata": {},
   "outputs": [],
   "source": [
    "class Parser:\n",
    "    \"\"\"Parameters for the testing\"\"\"\n",
    "    def __init__(self):\n",
    "        self.batch_size = 64\n",
    "        self.test_batch_size = 10"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Training set 60000 items\n",
      "Testing set  10000 items\n"
     ]
    }
   ],
   "source": [
    "args = Parser()\n",
    "\n",
    "data = learn.load_data()\n",
    "train_data, train_target_char, train_target_family, test_data, test_target_char, test_target_family = data\n",
    "test_target = test_target_char\n",
    "test_dataset = learn.build_tensor_dataset(test_data, test_target)\n",
    "test_loader = utils.DataLoader(\n",
    "    test_dataset,\n",
    "    batch_size=args.test_batch_size, shuffle=True\n",
    ")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Our test function modifies the data first in the encrypted setting (ie values are integers) and then converts back the output to float to run the public part of the net used to predict digits."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "metadata": {},
   "outputs": [],
   "source": [
    "def test(model, test_loader, prec):\n",
    "    data_prec, proj_prec, diag_prec = prec \n",
    "    correct = 0\n",
    "    with torch.no_grad():\n",
    "        for data, target in test_loader:\n",
    "            # Private Part\n",
    "            data = fix_precision(data, data_prec, parameter=False)\n",
    "            private_output = model.quad(data)\n",
    "            # Public Part\n",
    "            output = float_precision(private_output, sum(prec))\n",
    "            output = model.char_net(output)\n",
    "\n",
    "            pred = output.argmax(1, keepdim=True)  # get the index of the max log-probability\n",
    "\n",
    "            correct += pred.eq(target.view_as(pred)).sum().item()\n",
    "\n",
    "    acc = 100. * correct / len(test_loader.dataset)\n",
    "    print('\\nTest set: Accuracy: {}/{} ({:.2f}%)\\n'.format(\n",
    "        correct, len(test_loader.dataset), acc))\n",
    "    \n",
    "    return acc"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Given a precision setting, this returns the accuracy of the main task and the maximum number of bits needed to store the output."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 178,
   "metadata": {},
   "outputs": [],
   "source": [
    "def test_compression(prec, rm_outlier_frac=100, show_distrib=False, cv=6):\n",
    "    data = collateral.data.load_resistance_data(font1=None, font2=None, cv=cv)\n",
    "    train_data, train_target_char, train_target_family, test_data, test_target_char, test_target_family = data\n",
    "    # Merge the target datasets\n",
    "    train_target = list(zip(train_target_char, train_target_family))\n",
    "    test_target = list(zip(test_target_char, test_target_family))\n",
    "\n",
    "    # We use here the slightly modified version of this function\n",
    "    train_dataset = learn.utils.build_tensor_dataset(train_data, train_target_char)\n",
    "    test_dataset = learn.utils.build_tensor_dataset(test_data, test_target_char)\n",
    "    \n",
    "    model = CollateralNet(private_output_size=PRIVATE_OUTPUT_SIZE)\n",
    "    model.load_state_dict(torch.load(path))\n",
    "    model.eval()\n",
    "\n",
    "    data_prec, proj_prec, diag_prec = prec \n",
    "    \n",
    "    if show_distrib and False:\n",
    "        print_hist(model.proj1.weight.detach())\n",
    "        print_hist(model.proj1.bias.detach())\n",
    "        print_hist(model.diag1.weight.detach())\n",
    "        \n",
    "    # Convert the model\n",
    "    model.proj1.weight = fix_precision(model.proj1.weight, proj_prec, rm_outlier_frac)\n",
    "    model.proj1.bias = fix_precision(model.proj1.bias, proj_prec + data_prec, rm_outlier_frac)\n",
    "    model.diag1.weight = fix_precision(model.diag1.weight, diag_prec, rm_outlier_frac)\n",
    "        \n",
    "    if show_distrib:\n",
    "        print_hist(model.proj1.weight)\n",
    "        print_hist(model.proj1.bias)\n",
    "        print_hist(model.diag1.weight)\n",
    "    \n",
    "    data_sample = fix_precision(test_dataset[0][0], data_prec, parameter=False)\n",
    "    n_bits = (\n",
    "        ceil(log2(torch.max(data_sample))) * 2 +\n",
    "        ceil(log2(max(\n",
    "            torch.max(model.proj1.bias / 2 ** data_prec),\n",
    "            torch.max(model.proj1.weight)\n",
    "        ) * 2)) * 2 + \n",
    "        ceil(log2(torch.max(model.diag1.weight) * 2))   \n",
    "    )\n",
    "    print(\"approx size\", n_bits)\n",
    "        \n",
    "    test_dataset_prec = torch.utils.data.TensorDataset(\n",
    "        fix_precision(test_dataset.tensors[0], data_prec, parameter=False),\n",
    "        test_dataset.tensors[1]\n",
    "    )\n",
    "    test_loader_prec = torch.utils.data.DataLoader(test_dataset_prec, batch_size=args.test_batch_size)\n",
    "    acc_char = test(model, test_loader_prec, prec)\n",
    "    \n",
    "    return model, data_prec, acc_char\n",
    "    "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 179,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Training set 60000 items\n",
      "Testing set  10000 items\n",
      "approx size 19\n",
      "\n",
      "Test set: Accuracy: 9953/10000 (99.53%)\n",
      "\n"
     ]
    }
   ],
   "source": [
    "prec=(3, 7, 5) # (3, 7, 5)\n",
    "data_prec, proj_prec, diag_prec = prec \n",
    "model, data_prec, acc_char = test_compression(prec=prec, rm_outlier_frac=99.9, show_distrib=False)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 181,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Training set 60000 items\n",
      "Testing set  10000 items\n",
      "approx size 17\n",
      "\n",
      "Test set: Accuracy: 9782/10000 (97.82%)\n",
      "\n",
      "Training set 60000 items\n",
      "Testing set  10000 items\n",
      "approx size 19\n",
      "\n",
      "Test set: Accuracy: 9789/10000 (97.89%)\n",
      "\n",
      "Training set 60000 items\n",
      "Testing set  10000 items\n",
      "approx size 19\n",
      "\n",
      "Test set: Accuracy: 9705/10000 (97.05%)\n",
      "\n",
      "Training set 60000 items\n",
      "Testing set  10000 items\n",
      "approx size 17\n",
      "\n",
      "Test set: Accuracy: 9761/10000 (97.61%)\n",
      "\n",
      "Training set 60000 items\n",
      "Testing set  10000 items\n",
      "approx size 19\n",
      "\n",
      "Test set: Accuracy: 9775/10000 (97.75%)\n",
      "\n",
      "Training set 60000 items\n",
      "Testing set  10000 items\n",
      "approx size 19\n",
      "\n",
      "Test set: Accuracy: 9805/10000 (98.05%)\n",
      "\n",
      "Training set 60000 items\n",
      "Testing set  10000 items\n",
      "approx size 19\n",
      "\n",
      "Test set: Accuracy: 9789/10000 (97.89%)\n",
      "\n",
      "97.72285714285714 0.3020271646115056\n"
     ]
    }
   ],
   "source": [
    "acc_chars = [] \n",
    "for cv in range(7):\n",
    "    path = f'models/quadconvnet_0.5_0.002_1.7_4_{cv}_par2.pt'\n",
    "    prec=(3, 7, 5) # (3, 7, 5)\n",
    "    data_prec, proj_prec, diag_prec = prec \n",
    "    model, data_prec, acc_char = test_compression(prec=prec, rm_outlier_frac=99.9, show_distrib=False, cv=cv)\n",
    "    acc_chars.append(acc_char)\n",
    "\n",
    "print(np.mean(acc_chars), np.std(acc_chars))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "So, as you observe, we keep a 98% accuracy with weight and data inputs integers between 0 and 16 (so on 4 bits)!.\n",
    "Experiments show that the total output is around 27 bits long, so we'll construct a discrete log algorithm which can work for output up to 30 bits long (so < 10^9)\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Let's save this model now"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "metadata": {},
   "outputs": [],
   "source": [
    "saving_path = '/Users/tryffel/code/reading-in-the-dark/mnist/objects/ml_models/quad_conv.pt'\n",
    "torch.save(model.state_dict(), saving_path)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 62,
   "metadata": {},
   "outputs": [],
   "source": [
    "proj = model.proj1.weight.t().tolist()\n",
    "proj.append((model.proj1.bias / 8).tolist())\n",
    "diag = model.diag1.weight.t().tolist()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 63,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(785, 40)"
      ]
     },
     "execution_count": 63,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "len(proj), len(proj[0])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 64,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(40, 4)"
      ]
     },
     "execution_count": 64,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "len(diag), len(diag[0])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 67,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
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1, 0], [1, 0, 2, -4, 2, 0, -3, -2, -2, 1, -3, -4, -1, 3, 2, -4, -4, -4, 0, 4, -4, 4, -2, 4, 1, 0, 0, 1, -2, 0, -2, -3, 2, -2, 4, -3, -2, 3, -1, 0], [1, 0, 3, 2, 2, 1, -2, -3, 1, -2, -3, 0, -3, -1, 3, 3, 0, 1, 0, 3, 0, 0, 1, -1, -3, 2, 3, -3, 0, -4, -4, 3, -3, 1, -2, 0, 1, 4, -1, -1], [4, 0, -2, -2, -2, 2, 3, -2, 0, 2, 1, 3, -4, 1, -3, -4, 2, 0, 0, -2, -2, -3, -1, 0, -2, -1, -1, -1, -1, -1, 2, -1, -2, -1, -1, -5, -3, -4, 1, -1], [0, -3, 2, -3, 0, 0, -1, -2, 0, 3, 0, -4, 0, 0, -2, 3, 1, 1, -2, -2, 2, 1, -4, 4, 0, 0, -3, 3, -4, 0, 2, 0, 0, 0, 0, -1, 0, 3, 1, 4], [5, -5, -3, 0, 1, 3, 0, 0, 0, 0, 0, 1, 0, 1, 0, 3, 1, -2, -3, 3, -3, 2, -3, 2, 4, 4, 4, 4, 3, 2, 3, -1, 1, 2, 1, -3, 0, 0, 2, -4], [2, 1, 3, -3, 2, 0, -2, 0, 2, 0, -2, -1, 0, -1, 1, 0, 3, 0, 2, 1, 0, 0, 0, -3, 2, 1, 2, -3, 0, 0, 4, 2, -4, 3, -1, 2, -4, 1, 3, -2], [2, -3, 2, 0, 0, -3, 0, -1, -2, 0, -5, -2, -5, -6, 0, 3, -1, 0, -3, 2, 2, 1, 3, 0, 4, -3, 0, -3, 1, 1, -2, -3, 4, 0, -1, 2, 0, 1, 5, 1], [0, 3, 5, 4, -2, -10, -3, 0, -3, 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2, 0, -4], [3, -2, 0, 3, -4, 3, 0, 2, -5, 0, -2, -1, 3, 1, -3, 2, 0, 2, 2, -3, 2, 3, 1, -2, 0, -2, -3, 1, 4, 0, 2, 3, 0, -2, -1, 0, -2, 1, -1, 2], [5, 0, 0, 0, -5, -3, -5, 2, 0, 2, -3, -5, 1, 3, -5, -3, 4, -5, 4, 0, -3, -2, 2, -4, -1, -3, 2, -1, -2, 0, 3, -3, -5, 2, 4, 6, -3, 2, -4, -2], [0, -2, -1, 3, -5, -6, 2, 3, 2, 5, 1, -1, -3, -3, -4, 0, -1, -1, -4, 1, -2, 2, 0, 0, -7, 1, 0, 0, 0, 2, -3, -2, -5, 0, -4, 0, 0, 3, -3, 0], [1, 0, -7, -2, 2, -5, -5, -1, 1, 6, -4, -6, 0, -2, -14, -3, 0, 0, -2, 4, -3, 0, 0, 4, -6, 0, 3, 0, -1, -1, 4, 1, 1, 0, 3, 1, -3, 4, 0, 1], [1, 0, -1, -3, 0, -3, -6, 4, 2, 3, 0, -8, -2, -2, -10, 0, 0, -3, -3, 2, 3, 0, 3, -2, -4, 2, -3, -3, 2, 3, -1, -4, 3, 4, 5, 0, -1, -2, -4, -3], [-1, -2, -3, -3, -3, 0, -7, 6, 0, 5, 3, -1, 0, 0, -6, 0, -4, -5, 1, 2, -3, 4, 5, 0, -1, -4, 4, 4, 6, 2, -3, -1, 0, 0, 5, 0, 3, 0, -5, 0], [3, -8, -2, 3, 3, -1, -4, -1, -3, 0, 0, -1, 0, -4, -1, -1, 3, 0, 0, 0, -1, -2, 0, -3, -6, -1, -2, -2, 0, 2, 0, 1, -2, 2, 5, 3, -2, 0, -4, -3], [4, -1, -5, 2, 2, -1, 3, -2, 0, 0, 5, -7, 2, -1, -6, 1, 0, 0, -1, -3, 1, -3, 2, -3, -1, 3, -3, 4, 0, 1, -1, -2, -3, -1, 4, 3, 1, 2, -1, 0], [3, -1, 0, -2, 1, -2, -1, 0, -4, -1, 5, -5, -3, 0, 0, 0, -1, -4, -2, 1, 2, 1, 1, 3, 0, 1, -2, 2, 0, 1, -4, -4, 3, -2, -1, -3, -1, -2, 4, 2], [-1, -1, 3, -1, 3, 3, -3, 0, -4, -1, -3, -4, -3, 2, 1, 3, 3, 1, -4, -1, 2, -3, -1, -1, -2, 3, 0, -1, 2, 0, -1, 1, 1, 4, -1, -4, 0, -1, -1, -1], [5, -4, 0, -3, 3, -3, 2, 0, 1, 0, 0, -3, 0, -1, -1, 3, 3, 3, -3, -2, 1, 3, 0, 0, 0, 3, 3, 0, 3, -3, -1, -2, -2, 4, -3, -1, -4, 0, 0, -1], [0, -4, 2, 1, 0, -3, -2, 0, -2, -2, -1, -4, -1, -3, 1, 2, 3, -2, -1, -3, -2, 0, -1, -2, 4, 0, 4, -2, -2, 3, 0, 0, 0, 4, 0, 0, 1, 0, 4, 0], [-2, -3, 0, 2, -2, 1, -1, -5, -1, -1, 3, -5, -3, 0, -2, 1, 1, 2, 2, 0, -2, -2, 2, 3, 2, -2, 0, -2, 4, 2, 0, 0, -1, 1, -2, -1, 2, -3, -3, 4], [-2, 1, 3, -2, -1, 1, 0, -3, -1, 1, 2, 0, -4, -1, -2, -3, 0, 3, 3, 1, -4, 0, -3, 1, 4, 3, -1, 1, 2, 2, 2, 1, 0, 0, -1, -2, -1, 2, 4, -1], [1, -2, 4, 2, 2, 0, -3, 1, 1, 3, -3, 3, -3, -1, 0, -4, -1, 0, 3, 3, -2, 4, -1, 1, 0, 0, 1, -1, 2, 3, 0, -1, 3, 2, 2, 2, 0, -2, 3, 3], [0, -4, -1, -2, 1, 4, 0, 1, -1, -1, -3, -3, -2, 3, -1, 1, 4, 1, 0, 2, -3, 4, -3, -2, -1, -1, 4, 0, 0, -1, 0, -1, 2, 1, -3, -3, -2, -3, 0, -1], [1, -5, -1, 0, -3, 2, -2, 3, -1, 0, 0, -1, 4, 3, 2, 3, -4, -2, 2, -2, -1, 3, 0, 0, 0, -2, 0, 3, 0, 0, 0, -4, -2, -2, 1, -3, -3, 4, -3, -2], [0, -5, -3, 0, -3, 0, 1, -4, 3, -3, -3, 3, -1, 2, -2, 0, 2, -2, 3, -2, 2, 4, 3, 0, 2, 1, 2, 1, 0, 3, -3, 2, -4, 0, 3, -4, -2, 1, -4, 2], [0, 0, 1, 2, 3, 1, -1, 0, 1, -2, 0, -4, -1, 1, 0, 2, 2, 2, 2, -4, 1, 0, -1, -2, -3, -2, -2, -3, 0, 3, 2, 0, 1, -2, -2, -1, -3, 3, 1, 3], [1, -2, 4, 2, 0, 1, -2, 2, -2, 1, 4, -3, 2, 3, 3, 0, 1, 3, 2, 3, -1, 2, -3, 0, -4, 0, 0, -2, -3, 1, 0, -1, -3, 0, -1, -5, -3, 4, 1, 0], [-1, 0, 3, 1, -3, -2, 1, 3, 3, 2, 0, -2, 2, 3, -3, -4, 2, 3, -2, -3, 4, 4, -3, 0, -3, -2, 2, 2, 0, -2, -4, 1, -3, 1, 4, 2, 0, 0, 2, 1], [-2, -5, 0, -2, 0, 3, -4, -4, -2, 4, -1, 1, -1, 4, 3, -2, 4, 3, -4, -2, 2, 0, 1, -3, 4, -4, -1, 2, 3, 0, 2, -2, 2, -1, 0, 1, -2, 0, 2, 4], [4, -4, -1, 2, 2, -2, 0, 1, 2, 3, 2, -3, 2, 0, 2, 1, -2, -3, 0, -3, -2, 1, 3, -1, 2, -2, 3, -3, 0, 0, 2, -4, 1, 4, 0, -5, -2, -1, 2, -4], [2, -3, -1, 2, 4, -1, -3, 1, 2, 2, 3, 3, -2, -2, -3, 2, -2, 4, -4, -3, 0, 3, -3, 2, -3, -2, -2, 1, -4, 0, -2, -4, 0, 3, -3, -2, 4, -4, 1, 4], [5, -1, 0, -4, 0, -2, 3, 0, -3, 1, 3, 1, 3, 4, 0, 0, 0, 0, 2, 2, -3, -1, -2, 1, 4, 4, 0, -1, 2, -3, -4, 3, 0, -1, -3, -4, 3, 0, 0, 0], [1, 0, 0, -1, -3, -2, 0, 0, -2, 2, 0, 1, -2, -3, -1, -1, 2, -2, 0, 2, 4, -1, -1, 1, 3, 0, -4, 0, 4, 2, 1, -1, 3, 0, 2, 3, -2, 3, 1, 0], [1, -4, 2, 1, 0, -2, 3, 0, 2, 0, -2, -4, 2, 2, 1, -1, -1, -4, 0, 0, 2, 0, 2, -1, -3, 0, 0, 3, -3, -4, -2, -4, 1, -2, -2, -4, 1, 2, -2, 3], [4, -3, 1, -1, -1, 0, 2, 3, 2, 0, 0, -3, -2, 2, 0, -1, 3, -3, 3, 0, 0, 0, -4, -1, 1, -1, 0, -4, -3, 1, 1, 2, 4, 0, 3, 0, -2, 1, 0, -1], [6, 1, 0, -2, 1, 0, 3, -3, 0, 1, 4, 0, -2, 0, 0, 0, -1, -4, 0, 4, -3, 2, 3, 4, 4, -1, -3, 0, 1, 0, 1, -2, -3, 2, -1, 3, -1, -1, 4, 1], [3, -6, -3, -2, 1, 0, -5, -1, 0, -2, -3, 0, 3, 3, 3, -4, 1, -3, -2, -2, 1, 0, 0, 3, 0, 1, 4, 0, 0, -1, 0, 3, -4, -2, -1, 0, 1, 1, -3, 2], [0, -4, 2, 1, -2, 0, 0, -4, 0, 0, -4, -5, 2, 5, 0, -5, 1, 2, 0, 0, 1, 2, 1, 0, -3, 2, 3, 0, -2, -1, -1, -2, 1, 0, -1, 4, 4, 2, -1, -4], [4, 0, 3, 4, 0, -4, 2, 0, 1, -1, 0, -3, -1, -3, -6, 0, 3, -5, 2, 3, -1, 4, 1, -3, 2, -4, 4, 3, 0, 1, -3, -3, 0, 1, -1, -3, -4, 0, -2, -4], [7, -8, -2, 1, 1, 1, 1, 0, 0, 0, 0, -2, 3, -1, -2, -5, -1, 0, -2, -3, -2, 0, -3, 3, 3, 1, 2, 4, -3, -1, 2, -2, -3, 3, 5, -3, 0, 0, -1, 0], [1, -1, 0, 0, 1, -1, 0, 2, 2, 3, 3, -1, 0, 0, 1, 1, 2, 2, 1, -2, 2, 1, -1, 4, 1, 0, 2, 0, -3, -2, 2, -1, 0, 1, 7, -3, -4, 1, 2, -1], [-1, -5, 1, 2, -4, -4, 0, 1, 4, 4, 0, -6, 0, 2, 0, -3, -2, 4, -4, 4, -1, -4, 1, 0, -2, -4, 1, 4, 1, 0, 3, -4, 0, 0, 8, -2, -3, 3, -4, 0], [5, -3, 3, -1, -1, 3, -2, -2, 0, -2, -2, -3, 2, 3, 2, -1, -2, -2, -2, 2, 2, -3, 2, 3, -1, -3, 4, -3, -1, 0, -2, -2, 2, 0, 5, -3, 0, 0, -1, 0], [-2, -5, 3, 2, 0, 0, 0, -2, 0, 1, -2, -2, 1, 2, 0, 1, -1, 1, 3, 0, -4, -2, -4, 3, 3, 4, 0, 4, 0, 3, 0, -4, -2, 2, 3, 3, -2, -2, 3, -1], [0, -6, 2, -3, 4, 0, 0, 0, 0, 1, 0, -4, 0, 0, -3, 3, 0, 2, 3, 2, 0, -3, 2, -2, 2, 3, 3, -2, 0, 0, -1, -1, 2, -1, 3, 1, 0, -4, -3, 1], [3, -3, -1, 0, 2, 0, -2, 0, -2, -3, -3, 0, 4, 3, -1, -3, 0, 0, -3, -4, -4, 3, -2, 4, -2, 2, 1, 0, 0, 1, 0, 0, -2, 3, -4, -2, 0, -1, 1, -1], [4, -2, -2, -2, 0, 0, 1, 0, -3, 2, -1, -3, 1, 1, -1, 1, -3, 0, -3, -1, 0, -3, 2, 0, 0, 4, -2, 0, 4, 0, 3, -2, 3, -2, 2, -1, -1, 0, 2, -2], [0, 1, 2, -4, 0, -3, -1, -1, -1, 3, 2, 0, -1, -1, 4, -3, 4, 3, -3, 3, 0, -2, -2, -2, 4, 3, 4, 1, 4, -3, -1, -3, -3, 1, -1, -4, -1, -1, 4, -2], [3, -3, 3, -1, -1, 0, 2, 0, -2, 3, 3, -2, 4, -2, -4, -3, 3, 3, 2, 0, 2, 4, -2, 1, 0, -4, 0, 0, 0, 0, -4, 0, 3, 4, -1, 0, 0, 3, 1, -3], [4, 0, 3, -2, 1, 0, -3, -3, -2, -1, -3, -1, 4, -3, -4, -3, 4, 4, -1, 0, -2, 4, 3, 1, 0, 0, 0, 1, 3, -2, 4, -2, 2, 0, -4, -1, -2, 0, 0, 2], [1, -6, 2, -4, 0, -3, -3, 3, 2, 2, 2, 2, -3, -2, 3, 0, 0, 0, 0, -2, 2, -1, 3, -3, 1, 2, 0, -1, 4, 0, -4, 0, 1, 3, 3, 0, -1, 0, -2, 2], [0, -3, 4, -4, 0, -3, -2, 2, -3, -1, 1, -2, 4, 3, -2, 0, 0, -1, 4, -1, -4, -2, -3, 1, -3, -1, -1, -3, -3, 2, -1, -1, -3, 3, 0, 1, -2, -1, 1, -3], [3, -4, -1, 3, -1, -2, 2, -3, -2, 0, -2, 0, 4, 3, 4, 1, -4, 0, -4, 0, 3, 3, -4, 1, 2, 4, -2, 0, -2, -1, -2, -1, 0, -2, -2, -2, -3, 3, 4, 4], [1, -2, -4, 1, 3, -2, -4, 0, -2, 0, 0, 2, 0, 1, 3, 1, 4, -3, 2, 2, -4, 2, 0, 3, 2, 0, -3, 0, -2, -2, -4, 0, 1, 4, 1, -4, -3, -2, 3, 0], [0, -5, -1, 0, -3, 2, 0, 1, 0, -2, -1, 3, 2, -1, -2, 1, 0, 0, 4, -2, -3, -1, -1, -4, 2, -2, 4, -2, 4, -2, -1, 0, 0, -3, -3, -3, 0, -2, -3, 2], [5, -2, -2, -3, -1, 0, 0, -1, 3, 1, -1, -3, 4, 0, -1, 3, 1, 2, 1, -3, -2, 1, 2, 1, 2, 1, 3, -1, 2, 1, -1, -3, -1, 0, 0, 0, 3, -2, 0, 0], [-1, -2, -3, 1, 1, -1, -3, 2, 1, 0, -3, -5, -3, 3, -2, -4, 0, 0, -3, 0, 3, 3, 1, 4, 4, 1, 4, 3, 1, 1, 2, 0, 2, 1, -1, -2, 0, 4, 2, 2], [4, 1, -2, 3, -1, -2, 1, 1, -2, 1, -1, -2, -1, -1, -3, 0, 3, -2, -2, -2, -2, -3, 2, 0, 0, -4, 1, 2, 3, 0, 3, 2, -3, 4, -3, -1, -3, -1, -1, 2], [-1, -1, 2, 1, -2, 0, -2, 2, 0, -1, -3, 1, 1, -3, -3, -1, -3, -1, 0, -2, 0, 0, -1, 4, 0, -1, -1, 0, 0, 0, 0, 3, -3, 2, 3, -3, 3, -1, -2, 1], [1, 0, 1, -2, 4, 3, -4, 0, 3, -1, -3, -3, -3, 3, -3, 3, 1, -2, -3, 3, 3, 4, 1, -3, 4, -2, -4, 0, 0, -3, -2, -4, 4, -3, -3, 0, 3, 3, 2, 3], [4, 1, 0, -2, 4, 2, -1, -4, -2, -2, -1, -3, -2, 0, -2, 0, -1, 0, 3, -1, -2, 0, -1, 0, -2, 2, 3, 4, 0, 2, -1, 1, -4, 1, -3, -4, 0, 0, 0, -3], [3, 0, -2, 2, 3, 4, 2, 0, 0, -1, 0, -3, 4, -3, 0, -3, -2, 1, 3, 2, 3, 4, 3, -1, 2, -3, 0, -3, -2, -1, 0, 0, 2, 3, 1, 2, -1, -3, 3, 0], [4, 1, 0, 0, 3, 0, 0, 1, 0, 4, -3, -4, 1, -3, 0, -4, 4, 3, 2, -3, -2, -1, 0, 0, -3, -3, 0, 1, -1, 3, -2, 2, 0, -1, 1, -2, 3, -1, 0, 4], [3, -6, -4, 2, -2, -3, -3, 0, -1, 3, 0, -2, -1, 3, 0, -1, -1, 1, -4, 0, 1, -1, -3, -3, 0, -2, -2, 0, 0, 0, -2, -2, -3, -1, 4, 3, -2, 0, 4, 4], [-1, -4, 2, 4, 3, -1, -3, 2, 1, 1, 1, -1, 4, -3, 2, 0, -2, 2, 3, 1, 0, -3, -3, -1, -3, -1, -1, -2, -1, 0, 0, 3, 0, 3, 0, 2, -2, 3, 0, 0], [0, 0, 1, 2, 2, -4, 2, 0, 0, -1, 2, 0, 0, 1, 3, -5, 4, 0, 3, -3, 2, 0, -2, 0, -3, 0, -2, 4, 4, 4, 1, -1, 3, 0, 4, -4, 3, 0, -4, 0], [1, -6, 4, 2, 4, -4, 0, 2, -2, 2, -6, -5, 0, 2, 0, 2, 3, -1, 0, 0, 0, -2, 0, -1, -2, 0, 2, 4, 4, -4, -1, -1, 1, -2, 4, -4, 0, 3, -3, 0], [5, -3, 4, 3, -4, 0, 2, -3, -4, -1, 4, -1, -1, -3, 3, -1, 0, 4, 1, -2, 0, 2, 0, 2, 0, 0, 4, -2, 0, -2, 1, 2, 3, -1, 5, 0, 0, 0, -2, 1], [-1, -2, 1, 1, 0, -3, -3, -3, -3, -3, 0, 1, 1, 0, 2, -2, 0, 1, -1, 0, 3, 2, 0, -3, 0, 2, 2, -2, 2, -1, 2, 2, 3, -2, -2, -1, -4, 0, 0, -3], [5, -2, 4, 1, 1, -1, -3, 0, -1, -3, 0, -3, 1, 1, -3, 2, 2, -1, -3, -3, -1, -1, -2, 2, -3, -2, -3, 1, 2, 1, -3, 2, 0, 4, 2, 1, 0, 2, 1, 1], [0, -3, 3, 3, -4, -4, 0, 2, 2, -3, -3, 0, -2, -2, -3, 0, -1, -2, 0, -2, -3, -3, -2, -1, 4, -3, -4, -1, 0, 0, 4, -2, 0, -1, -3, -2, -1, 1, 0, -2], [4, 1, 0, 2, 1, 4, -3, -1, 3, 0, 4, -3, -4, -3, 4, 0, -2, 0, 0, 0, 1, 3, 1, 0, -3, 4, 3, 0, -1, -4, 0, -3, 3, -1, -2, 0, -2, 0, 0, 2], [1, 2, 4, -1, 4, 1, -1, 3, 2, -2, -2, 0, -2, -2, 0, -3, 0, 3, 3, -3, 3, -3, 3, -1, -2, 2, 3, 4, 1, -2, -3, 3, 1, 0, -1, 0, 0, -2, 2, 2], [0, -3, -4, 2, 1, 2, 0, 1, 1, -2, -1, 1, 2, 0, -4, -3, -2, 3, -1, -3, 0, -3, 0, 1, -3, 1, -3, -3, -1, -3, 0, 2, -1, -1, 0, -4, 2, -3, 0, 0], [-2, 0, -2, 0, 0, 3, 3, -1, -1, 0, -3, 1, 0, -3, 4, -3, 2, 0, 3, 2, 0, 0, -3, -2, 3, -3, -2, -1, -3, 2, 3, 1, 1, 2, 3, 1, 3, 1, 3, 1], [-1, -1, -2, 3, -2, 0, -4, -1, -3, 2, -1, 0, -2, 1, 3, 1, -2, 0, 1, 2, -2, -2, 3, 0, 1, 2, 3, 1, 3, 4, -1, -4, -4, 2, -3, 2, 0, 0, -2, -3], [5, -2, 3, -1, 0, 1, 0, 0, 0, 0, 0, 0, 0, 1, 0, 2, -1, -1, -3, -2, -3, 2, -4, 1, 0, 4, 3, -2, 0, 3, 1, 0, 0, 0, -2, -5, 0, 1, 1, -2], [5, -4, -1, 1, 1, 0, -4, 1, -1, 3, 4, -4, 3, 0, 0, 0, 0, 0, -3, 4, -2, 3, -3, 3, 0, 0, -2, -3, 0, -1, 2, -4, -2, -1, 4, 1, -4, 0, 1, -3], [4, -2, -2, -4, 0, 1, -4, 0, 3, 3, -3, 3, -3, -1, 0, -1, 0, -3, 1, 1, -1, -3, -2, 0, 0, 4, 0, -3, 3, 4, -1, 0, 3, 4, 0, 3, -1, 2, 0, -3], [0, -3, -1, -3, 3, 4, -2, 0, 4, 2, 3, 3, 0, -3, 1, -3, -1, 3, 3, 0, 3, 0, 1, 0, 1, 4, -4, 0, -2, 1, -4, -1, 2, -1, 0, 3, -3, -3, 1, 0], [2, -3, 0, 1, 0, 1, 3, 3, 0, 2, 0, -1, 0, 2, 2, -3, -1, -3, 0, -4, -2, 4, -4, 4, 4, -2, 0, 2, -1, -1, 1, -3, -1, -2, 0, 2, -1, 0, 1, 2], [2, 1, 1, 1, -3, 0, -3, 1, 0, 2, 3, 1, -2, -2, -4, 0, 3, -3, 0, -3, -3, -3, -2, 4, 4, -3, -2, -1, -3, 0, -3, 3, 3, 0, 3, 2, -3, 0, -3, 3], [-3, 3, -9, 0, 4, 4, 4, -8, 3, -1, -9, 10, 4, -3, 12, 0, 0, 7, 1, 2, -3, -1, 1, 9, 5, 0, 4, 6, -1, -4, 0, 0, 1, 0, -5, -6, -1, -2, -3, 20]]\n",
      "[[1, 3, 0, 13], [7, 1, -3, 13], [-13, 5, -2, 5], [10, 11, 0, 0], [-6, -2, 7, 5], [10, -11, 1, 2], [8, -1, 3, -10], [-5, 5, -5, 10], [-9, 0, -9, -3], [0, -3, 10, -3], [-9, 8, -4, -10], [13, 4, 6, 10], [5, -5, -12, 3], [12, 11, -2, 0], [11, -2, -4, -9], [1, -8, -7, -12], [0, -2, 12, -2], [1, 7, -1, -10], [-11, 3, 0, -11], [-1, 3, -1, 2], [-4, 6, 0, 0], [-2, -4, -8, 10], [8, 2, -14, -1], [-10, -3, -6, 7], [-3, -2, 13, -1], [4, 0, -4, -2], [-4, -5, -11, 9], [2, 9, 8, -7], [-11, 0, 6, -8], [1, 0, -12, -4], [4, 0, -13, -4], [-9, 5, 9, -7], [7, 10, 1, -7], [0, 3, -9, 6], [-11, 6, 1, -11], [-9, -2, 1, 10], [1, 4, 10, -3], [-1, 3, -7, -1], [-8, -5, 10, 0], [1, -4, 11, 8]]\n"
     ]
    }
   ],
   "source": [
    "print(proj)\n",
    "print(diag)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Collateral efficiency\n",
    "Let's also test the impact of weight conversion and compression the the CNN model against which resistance had been built. You can observe that there is no visible impact."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 114,
   "metadata": {},
   "outputs": [],
   "source": [
    "import torch.optim as optim"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 132,
   "metadata": {},
   "outputs": [],
   "source": [
    "class Parser:\n",
    "    \"\"\"Parameters for the training\"\"\"\n",
    "    def __init__(self):\n",
    "        self.epochs = 10\n",
    "        self.sabotage_epochs = 50\n",
    "        self.new_adversary_epochs = 50\n",
    "        self.lr = 0.002001\n",
    "        self.momentum = 0.5\n",
    "        self.test_batch_size = 1000\n",
    "        self.batch_size = 64\n",
    "        self.log_interval = 100"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 143,
   "metadata": {},
   "outputs": [],
   "source": [
    "def test_collateral(prec, rm_outlier_frac):\n",
    "    model = CollateralNet(private_output_size=PRIVATE_OUTPUT_SIZE)\n",
    "    model.load_state_dict(torch.load(path))\n",
    "    model.eval()\n",
    "\n",
    "    data_prec, proj_prec, diag_prec = prec \n",
    "        \n",
    "    # Convert the model\n",
    "    model.proj1.weight = nn.Parameter(\n",
    "        fix_precision(model.proj1.weight, proj_prec, rm_outlier_frac).float() / 2**proj_prec\n",
    "        , requires_grad=False)\n",
    "    model.proj1.bias = nn.Parameter(\n",
    "        fix_precision(model.proj1.bias, proj_prec + data_prec, rm_outlier_frac).float() / 2**(proj_prec + data_prec)\n",
    "        , requires_grad=False)\n",
    "    model.diag1.weight = nn.Parameter(\n",
    "        fix_precision(model.diag1.weight, diag_prec, rm_outlier_frac).float() / 2**diag_prec\n",
    "        , requires_grad=False)\n",
    "        \n",
    "    \n",
    "    args = Parser()\n",
    "    alpha = 1.7\n",
    "    \n",
    "    train_dataset, test_dataset = collateral.data.get_collateral_datasets(*fonts, letter)\n",
    "\n",
    "    optimizer = optim.SGD(model.parameters(), lr=args.lr, momentum=args.momentum)\n",
    "    \n",
    "    train_dataset_prec = torch.utils.data.TensorDataset(\n",
    "        fix_precision(train_dataset.tensors[0], data_prec, parameter=False).float() / 2**data_prec,\n",
    "        train_dataset.tensors[1]\n",
    "    )\n",
    "    train_loader_prec = torch.utils.data.DataLoader(train_dataset_prec, batch_size=args.batch_size)\n",
    "    \n",
    "    test_dataset_prec = torch.utils.data.TensorDataset(\n",
    "        fix_precision(test_dataset.tensors[0], data_prec, parameter=False).float() / 2**data_prec,\n",
    "        test_dataset.tensors[1]\n",
    "    )\n",
    "    test_loader_prec = torch.utils.data.DataLoader(test_dataset_prec, batch_size=args.test_batch_size)\n",
    "    \n",
    "    test_perfs_char = []\n",
    "    test_perfs_font = []\n",
    "    for epoch in range(1, args.new_adversary_epochs + 1):\n",
    "        \n",
    "        initial_phase = False\n",
    "        perturbate = False\n",
    "        recover = True\n",
    "        new_adversary = False\n",
    "        \n",
    "        collateral.train(\n",
    "            args, model, train_loader_prec, optimizer, epoch, alpha,\n",
    "            initial_phase, perturbate, recover, new_adversary\n",
    "        )\n",
    "        test_perf_char, test_perf_font = collateral.test(args, model, test_loader_prec, new_adversary)\n",
    "        test_perfs_char.append(test_perf_char)\n",
    "        test_perfs_font.append(test_perf_font)\n",
    "\n",
    "    return test_perfs_char, test_perfs_font\n",
    "    "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 144,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Training set 60000 items\n",
      "Testing set  10000 items\n",
      "Train Epoch: 1 [0/60000 (0%)]\tLoss Char: 0.000000 Loss Font: 0.696381\n",
      "Train Epoch: 1 [6400/60000 (11%)]\tLoss Char: 0.000000 Loss Font: 0.688837\n",
      "Train Epoch: 1 [12800/60000 (21%)]\tLoss Char: 0.000000 Loss Font: 0.694149\n",
      "Train Epoch: 1 [19200/60000 (32%)]\tLoss Char: 0.000000 Loss Font: 0.695527\n",
      "Train Epoch: 1 [25600/60000 (43%)]\tLoss Char: 0.000000 Loss Font: 0.681827\n",
      "Train Epoch: 1 [32000/60000 (53%)]\tLoss Char: 0.000000 Loss Font: 0.700897\n",
      "Train Epoch: 1 [38400/60000 (64%)]\tLoss Char: 0.000000 Loss Font: 0.693258\n",
      "Train Epoch: 1 [44800/60000 (75%)]\tLoss Char: 0.000000 Loss Font: 0.691493\n",
      "Train Epoch: 1 [51200/60000 (85%)]\tLoss Char: 0.000000 Loss Font: 0.688664\n",
      "Train Epoch: 1 [57600/60000 (96%)]\tLoss Char: 0.000000 Loss Font: 0.689833\n",
      "\n",
      "Test set: Accuracy Char : 9988/10000 (99.88%)\n",
      "          Accuracy Font : 5274/10000 (52.74%)\n",
      "Train Epoch: 2 [0/60000 (0%)]\tLoss Char: 0.000000 Loss Font: 0.681593\n",
      "Train Epoch: 2 [6400/60000 (11%)]\tLoss Char: 0.000000 Loss Font: 0.687208\n",
      "Train Epoch: 2 [12800/60000 (21%)]\tLoss Char: 0.000000 Loss Font: 0.692635\n",
      "Train Epoch: 2 [19200/60000 (32%)]\tLoss Char: 0.000000 Loss Font: 0.691360\n",
      "Train Epoch: 2 [25600/60000 (43%)]\tLoss Char: 0.000000 Loss Font: 0.674954\n",
      "Train Epoch: 2 [32000/60000 (53%)]\tLoss Char: 0.000000 Loss Font: 0.705193\n",
      "Train Epoch: 2 [38400/60000 (64%)]\tLoss Char: 0.000000 Loss Font: 0.695406\n",
      "Train Epoch: 2 [44800/60000 (75%)]\tLoss Char: 0.000000 Loss Font: 0.687477\n",
      "Train Epoch: 2 [51200/60000 (85%)]\tLoss Char: 0.000000 Loss Font: 0.686623\n",
      "Train Epoch: 2 [57600/60000 (96%)]\tLoss Char: 0.000000 Loss Font: 0.687451\n",
      "\n",
      "Test set: Accuracy Char : 9992/10000 (99.92%)\n",
      "          Accuracy Font : 5329/10000 (53.29%)\n",
      "Train Epoch: 3 [0/60000 (0%)]\tLoss Char: 0.000000 Loss Font: 0.678783\n",
      "Train Epoch: 3 [6400/60000 (11%)]\tLoss Char: 0.000000 Loss Font: 0.686926\n",
      "Train Epoch: 3 [12800/60000 (21%)]\tLoss Char: 0.000000 Loss Font: 0.691507\n",
      "Train Epoch: 3 [19200/60000 (32%)]\tLoss Char: 0.000000 Loss Font: 0.690836\n",
      "Train Epoch: 3 [25600/60000 (43%)]\tLoss Char: 0.000000 Loss Font: 0.672577\n",
      "Train Epoch: 3 [32000/60000 (53%)]\tLoss Char: 0.000000 Loss Font: 0.702231\n",
      "Train Epoch: 3 [38400/60000 (64%)]\tLoss Char: 0.000000 Loss Font: 0.695539\n",
      "Train Epoch: 3 [44800/60000 (75%)]\tLoss Char: 0.000000 Loss Font: 0.686318\n",
      "Train Epoch: 3 [51200/60000 (85%)]\tLoss Char: 0.000000 Loss Font: 0.684264\n",
      "Train Epoch: 3 [57600/60000 (96%)]\tLoss Char: 0.000000 Loss Font: 0.687680\n",
      "\n",
      "Test set: Accuracy Char : 9995/10000 (99.95%)\n",
      "          Accuracy Font : 5342/10000 (53.42%)\n",
      "Train Epoch: 4 [0/60000 (0%)]\tLoss Char: 0.000000 Loss Font: 0.676559\n",
      "Train Epoch: 4 [6400/60000 (11%)]\tLoss Char: 0.000000 Loss Font: 0.686564\n",
      "Train Epoch: 4 [12800/60000 (21%)]\tLoss Char: 0.000000 Loss Font: 0.692136\n",
      "Train Epoch: 4 [19200/60000 (32%)]\tLoss Char: 0.000000 Loss Font: 0.691221\n",
      "Train Epoch: 4 [25600/60000 (43%)]\tLoss Char: 0.000000 Loss Font: 0.670019\n",
      "Train Epoch: 4 [32000/60000 (53%)]\tLoss Char: 0.000000 Loss Font: 0.697782\n",
      "Train Epoch: 4 [38400/60000 (64%)]\tLoss Char: 0.000000 Loss Font: 0.696065\n",
      "Train Epoch: 4 [44800/60000 (75%)]\tLoss Char: 0.000000 Loss Font: 0.684860\n",
      "Train Epoch: 4 [51200/60000 (85%)]\tLoss Char: 0.000000 Loss Font: 0.681431\n",
      "Train Epoch: 4 [57600/60000 (96%)]\tLoss Char: 0.000000 Loss Font: 0.687990\n",
      "\n",
      "Test set: Accuracy Char : 9995/10000 (99.95%)\n",
      "          Accuracy Font : 5373/10000 (53.73%)\n",
      "Train Epoch: 5 [0/60000 (0%)]\tLoss Char: 0.000000 Loss Font: 0.677375\n",
      "Train Epoch: 5 [6400/60000 (11%)]\tLoss Char: 0.000000 Loss Font: 0.685357\n",
      "Train Epoch: 5 [12800/60000 (21%)]\tLoss Char: 0.000000 Loss Font: 0.692363\n",
      "Train Epoch: 5 [19200/60000 (32%)]\tLoss Char: 0.000000 Loss Font: 0.691760\n",
      "Train Epoch: 5 [25600/60000 (43%)]\tLoss Char: 0.000000 Loss Font: 0.668645\n",
      "Train Epoch: 5 [32000/60000 (53%)]\tLoss Char: 0.000000 Loss Font: 0.692814\n",
      "Train Epoch: 5 [38400/60000 (64%)]\tLoss Char: 0.000000 Loss Font: 0.694649\n",
      "Train Epoch: 5 [44800/60000 (75%)]\tLoss Char: 0.000000 Loss Font: 0.683270\n",
      "Train Epoch: 5 [51200/60000 (85%)]\tLoss Char: 0.000000 Loss Font: 0.678096\n",
      "Train Epoch: 5 [57600/60000 (96%)]\tLoss Char: 0.000000 Loss Font: 0.688363\n",
      "\n",
      "Test set: Accuracy Char : 9998/10000 (99.98%)\n",
      "          Accuracy Font : 5388/10000 (53.88%)\n",
      "Train Epoch: 6 [0/60000 (0%)]\tLoss Char: 0.000000 Loss Font: 0.677777\n",
      "Train Epoch: 6 [6400/60000 (11%)]\tLoss Char: 0.000000 Loss Font: 0.683712\n",
      "Train Epoch: 6 [12800/60000 (21%)]\tLoss Char: 0.000000 Loss Font: 0.691483\n",
      "Train Epoch: 6 [19200/60000 (32%)]\tLoss Char: 0.000000 Loss Font: 0.691395\n",
      "Train Epoch: 6 [25600/60000 (43%)]\tLoss Char: 0.000000 Loss Font: 0.665864\n",
      "Train Epoch: 6 [32000/60000 (53%)]\tLoss Char: 0.000000 Loss Font: 0.686350\n",
      "Train Epoch: 6 [38400/60000 (64%)]\tLoss Char: 0.000000 Loss Font: 0.696227\n",
      "Train Epoch: 6 [44800/60000 (75%)]\tLoss Char: 0.000000 Loss Font: 0.681484\n",
      "Train Epoch: 6 [51200/60000 (85%)]\tLoss Char: 0.000000 Loss Font: 0.676317\n",
      "Train Epoch: 6 [57600/60000 (96%)]\tLoss Char: 0.000000 Loss Font: 0.687926\n",
      "\n",
      "Test set: Accuracy Char : 9999/10000 (99.99%)\n",
      "          Accuracy Font : 5391/10000 (53.91%)\n",
      "Train Epoch: 7 [0/60000 (0%)]\tLoss Char: 0.000000 Loss Font: 0.678085\n",
      "Train Epoch: 7 [6400/60000 (11%)]\tLoss Char: 0.000000 Loss Font: 0.683123\n",
      "Train Epoch: 7 [12800/60000 (21%)]\tLoss Char: 0.000000 Loss Font: 0.691943\n",
      "Train Epoch: 7 [19200/60000 (32%)]\tLoss Char: 0.000000 Loss Font: 0.691215\n",
      "Train Epoch: 7 [25600/60000 (43%)]\tLoss Char: 0.000000 Loss Font: 0.665455\n",
      "Train Epoch: 7 [32000/60000 (53%)]\tLoss Char: 0.000000 Loss Font: 0.680688\n",
      "Train Epoch: 7 [38400/60000 (64%)]\tLoss Char: 0.000000 Loss Font: 0.695750\n",
      "Train Epoch: 7 [44800/60000 (75%)]\tLoss Char: 0.000000 Loss Font: 0.679561\n",
      "Train Epoch: 7 [51200/60000 (85%)]\tLoss Char: 0.000000 Loss Font: 0.674507\n",
      "Train Epoch: 7 [57600/60000 (96%)]\tLoss Char: 0.000000 Loss Font: 0.688193\n",
      "\n",
      "Test set: Accuracy Char : 9999/10000 (99.99%)\n",
      "          Accuracy Font : 5418/10000 (54.18%)\n",
      "Train Epoch: 8 [0/60000 (0%)]\tLoss Char: 0.000000 Loss Font: 0.678321\n",
      "Train Epoch: 8 [6400/60000 (11%)]\tLoss Char: 0.000000 Loss Font: 0.682429\n",
      "Train Epoch: 8 [12800/60000 (21%)]\tLoss Char: 0.000000 Loss Font: 0.692453\n",
      "Train Epoch: 8 [19200/60000 (32%)]\tLoss Char: 0.000000 Loss Font: 0.692590\n",
      "Train Epoch: 8 [25600/60000 (43%)]\tLoss Char: 0.000000 Loss Font: 0.665972\n",
      "Train Epoch: 8 [32000/60000 (53%)]\tLoss Char: 0.000000 Loss Font: 0.676562\n",
      "Train Epoch: 8 [38400/60000 (64%)]\tLoss Char: 0.000000 Loss Font: 0.696270\n",
      "Train Epoch: 8 [44800/60000 (75%)]\tLoss Char: 0.000000 Loss Font: 0.679397\n",
      "Train Epoch: 8 [51200/60000 (85%)]\tLoss Char: 0.000000 Loss Font: 0.672536\n",
      "Train Epoch: 8 [57600/60000 (96%)]\tLoss Char: 0.000000 Loss Font: 0.688200\n",
      "\n",
      "Test set: Accuracy Char : 9999/10000 (99.99%)\n",
      "          Accuracy Font : 5371/10000 (53.71%)\n",
      "Train Epoch: 9 [0/60000 (0%)]\tLoss Char: 0.000000 Loss Font: 0.680262\n",
      "Train Epoch: 9 [6400/60000 (11%)]\tLoss Char: 0.000000 Loss Font: 0.681977\n",
      "Train Epoch: 9 [12800/60000 (21%)]\tLoss Char: 0.000000 Loss Font: 0.693345\n",
      "Train Epoch: 9 [19200/60000 (32%)]\tLoss Char: 0.000000 Loss Font: 0.692322\n",
      "Train Epoch: 9 [25600/60000 (43%)]\tLoss Char: 0.000000 Loss Font: 0.665488\n",
      "Train Epoch: 9 [32000/60000 (53%)]\tLoss Char: 0.000000 Loss Font: 0.672845\n",
      "Train Epoch: 9 [38400/60000 (64%)]\tLoss Char: 0.000000 Loss Font: 0.697394\n",
      "Train Epoch: 9 [44800/60000 (75%)]\tLoss Char: 0.000000 Loss Font: 0.680703\n",
      "Train Epoch: 9 [51200/60000 (85%)]\tLoss Char: 0.000000 Loss Font: 0.670975\n",
      "Train Epoch: 9 [57600/60000 (96%)]\tLoss Char: 0.000000 Loss Font: 0.687028\n",
      "\n",
      "Test set: Accuracy Char : 9999/10000 (99.99%)\n",
      "          Accuracy Font : 5388/10000 (53.88%)\n",
      "Train Epoch: 10 [0/60000 (0%)]\tLoss Char: 0.000000 Loss Font: 0.680408\n",
      "Train Epoch: 10 [6400/60000 (11%)]\tLoss Char: 0.000000 Loss Font: 0.680298\n",
      "Train Epoch: 10 [12800/60000 (21%)]\tLoss Char: 0.000000 Loss Font: 0.691186\n",
      "Train Epoch: 10 [19200/60000 (32%)]\tLoss Char: 0.000000 Loss Font: 0.692651\n",
      "Train Epoch: 10 [25600/60000 (43%)]\tLoss Char: 0.000000 Loss Font: 0.664470\n",
      "Train Epoch: 10 [32000/60000 (53%)]\tLoss Char: 0.000000 Loss Font: 0.670685\n",
      "Train Epoch: 10 [38400/60000 (64%)]\tLoss Char: 0.000000 Loss Font: 0.696616\n",
      "Train Epoch: 10 [44800/60000 (75%)]\tLoss Char: 0.000000 Loss Font: 0.681160\n",
      "Train Epoch: 10 [51200/60000 (85%)]\tLoss Char: 0.000000 Loss Font: 0.669814\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Train Epoch: 10 [57600/60000 (96%)]\tLoss Char: 0.000000 Loss Font: 0.687288\n",
      "\n",
      "Test set: Accuracy Char : 9999/10000 (99.99%)\n",
      "          Accuracy Font : 5369/10000 (53.69%)\n",
      "Train Epoch: 11 [0/60000 (0%)]\tLoss Char: 0.000000 Loss Font: 0.683382\n",
      "Train Epoch: 11 [6400/60000 (11%)]\tLoss Char: 0.000000 Loss Font: 0.679464\n",
      "Train Epoch: 11 [12800/60000 (21%)]\tLoss Char: 0.000000 Loss Font: 0.691261\n",
      "Train Epoch: 11 [19200/60000 (32%)]\tLoss Char: 0.000000 Loss Font: 0.692540\n",
      "Train Epoch: 11 [25600/60000 (43%)]\tLoss Char: 0.000000 Loss Font: 0.663808\n",
      "Train Epoch: 11 [32000/60000 (53%)]\tLoss Char: 0.000000 Loss Font: 0.666749\n",
      "Train Epoch: 11 [38400/60000 (64%)]\tLoss Char: 0.000000 Loss Font: 0.696775\n",
      "Train Epoch: 11 [44800/60000 (75%)]\tLoss Char: 0.000000 Loss Font: 0.681743\n",
      "Train Epoch: 11 [51200/60000 (85%)]\tLoss Char: 0.000000 Loss Font: 0.668213\n",
      "Train Epoch: 11 [57600/60000 (96%)]\tLoss Char: 0.000000 Loss Font: 0.685006\n",
      "\n",
      "Test set: Accuracy Char : 9999/10000 (99.99%)\n",
      "          Accuracy Font : 5386/10000 (53.86%)\n",
      "Train Epoch: 12 [0/60000 (0%)]\tLoss Char: 0.000000 Loss Font: 0.684261\n",
      "Train Epoch: 12 [6400/60000 (11%)]\tLoss Char: 0.000000 Loss Font: 0.678899\n",
      "Train Epoch: 12 [12800/60000 (21%)]\tLoss Char: 0.000000 Loss Font: 0.691317\n",
      "Train Epoch: 12 [19200/60000 (32%)]\tLoss Char: 0.000000 Loss Font: 0.693394\n",
      "Train Epoch: 12 [25600/60000 (43%)]\tLoss Char: 0.000000 Loss Font: 0.664582\n",
      "Train Epoch: 12 [32000/60000 (53%)]\tLoss Char: 0.000000 Loss Font: 0.666079\n",
      "Train Epoch: 12 [38400/60000 (64%)]\tLoss Char: 0.000000 Loss Font: 0.699937\n",
      "Train Epoch: 12 [44800/60000 (75%)]\tLoss Char: 0.000000 Loss Font: 0.681862\n",
      "Train Epoch: 12 [51200/60000 (85%)]\tLoss Char: 0.000000 Loss Font: 0.665900\n",
      "Train Epoch: 12 [57600/60000 (96%)]\tLoss Char: 0.000000 Loss Font: 0.684980\n",
      "\n",
      "Test set: Accuracy Char : 10000/10000 (100.00%)\n",
      "          Accuracy Font : 5406/10000 (54.06%)\n",
      "Train Epoch: 13 [0/60000 (0%)]\tLoss Char: 0.000000 Loss Font: 0.684904\n",
      "Train Epoch: 13 [6400/60000 (11%)]\tLoss Char: 0.000000 Loss Font: 0.677682\n",
      "Train Epoch: 13 [12800/60000 (21%)]\tLoss Char: 0.000000 Loss Font: 0.691665\n",
      "Train Epoch: 13 [19200/60000 (32%)]\tLoss Char: 0.000000 Loss Font: 0.692236\n",
      "Train Epoch: 13 [25600/60000 (43%)]\tLoss Char: 0.000000 Loss Font: 0.664350\n",
      "Train Epoch: 13 [32000/60000 (53%)]\tLoss Char: 0.000000 Loss Font: 0.663160\n",
      "Train Epoch: 13 [38400/60000 (64%)]\tLoss Char: 0.000000 Loss Font: 0.699757\n",
      "Train Epoch: 13 [44800/60000 (75%)]\tLoss Char: 0.000000 Loss Font: 0.682027\n",
      "Train Epoch: 13 [51200/60000 (85%)]\tLoss Char: 0.000000 Loss Font: 0.666935\n",
      "Train Epoch: 13 [57600/60000 (96%)]\tLoss Char: 0.000000 Loss Font: 0.684874\n",
      "\n",
      "Test set: Accuracy Char : 10000/10000 (100.00%)\n",
      "          Accuracy Font : 5407/10000 (54.07%)\n",
      "Train Epoch: 14 [0/60000 (0%)]\tLoss Char: 0.000000 Loss Font: 0.684253\n",
      "Train Epoch: 14 [6400/60000 (11%)]\tLoss Char: 0.000000 Loss Font: 0.676415\n",
      "Train Epoch: 14 [12800/60000 (21%)]\tLoss Char: 0.000000 Loss Font: 0.691665\n",
      "Train Epoch: 14 [19200/60000 (32%)]\tLoss Char: 0.000000 Loss Font: 0.691105\n",
      "Train Epoch: 14 [25600/60000 (43%)]\tLoss Char: 0.000000 Loss Font: 0.664483\n",
      "Train Epoch: 14 [32000/60000 (53%)]\tLoss Char: 0.000000 Loss Font: 0.663911\n",
      "Train Epoch: 14 [38400/60000 (64%)]\tLoss Char: 0.000000 Loss Font: 0.697780\n",
      "Train Epoch: 14 [44800/60000 (75%)]\tLoss Char: 0.000000 Loss Font: 0.683348\n",
      "Train Epoch: 14 [51200/60000 (85%)]\tLoss Char: 0.000000 Loss Font: 0.666018\n",
      "Train Epoch: 14 [57600/60000 (96%)]\tLoss Char: 0.000000 Loss Font: 0.683535\n",
      "\n",
      "Test set: Accuracy Char : 10000/10000 (100.00%)\n",
      "          Accuracy Font : 5419/10000 (54.19%)\n",
      "Train Epoch: 15 [0/60000 (0%)]\tLoss Char: 0.000000 Loss Font: 0.686296\n",
      "Train Epoch: 15 [6400/60000 (11%)]\tLoss Char: 0.000000 Loss Font: 0.675384\n",
      "Train Epoch: 15 [12800/60000 (21%)]\tLoss Char: 0.000000 Loss Font: 0.692610\n",
      "Train Epoch: 15 [19200/60000 (32%)]\tLoss Char: 0.000000 Loss Font: 0.688101\n",
      "Train Epoch: 15 [25600/60000 (43%)]\tLoss Char: 0.000000 Loss Font: 0.663611\n",
      "Train Epoch: 15 [32000/60000 (53%)]\tLoss Char: 0.000000 Loss Font: 0.662550\n",
      "Train Epoch: 15 [38400/60000 (64%)]\tLoss Char: 0.000000 Loss Font: 0.698858\n",
      "Train Epoch: 15 [44800/60000 (75%)]\tLoss Char: 0.000000 Loss Font: 0.683026\n",
      "Train Epoch: 15 [51200/60000 (85%)]\tLoss Char: 0.000000 Loss Font: 0.664657\n",
      "Train Epoch: 15 [57600/60000 (96%)]\tLoss Char: 0.000000 Loss Font: 0.682869\n",
      "\n",
      "Test set: Accuracy Char : 10000/10000 (100.00%)\n",
      "          Accuracy Font : 5423/10000 (54.23%)\n",
      "Train Epoch: 16 [0/60000 (0%)]\tLoss Char: 0.000000 Loss Font: 0.689034\n",
      "Train Epoch: 16 [6400/60000 (11%)]\tLoss Char: 0.000000 Loss Font: 0.674443\n",
      "Train Epoch: 16 [12800/60000 (21%)]\tLoss Char: 0.000000 Loss Font: 0.688832\n",
      "Train Epoch: 16 [19200/60000 (32%)]\tLoss Char: 0.000000 Loss Font: 0.692155\n",
      "Train Epoch: 16 [25600/60000 (43%)]\tLoss Char: 0.000000 Loss Font: 0.663750\n",
      "Train Epoch: 16 [32000/60000 (53%)]\tLoss Char: 0.000000 Loss Font: 0.664181\n",
      "Train Epoch: 16 [38400/60000 (64%)]\tLoss Char: 0.000000 Loss Font: 0.698358\n",
      "Train Epoch: 16 [44800/60000 (75%)]\tLoss Char: 0.000000 Loss Font: 0.683820\n",
      "Train Epoch: 16 [51200/60000 (85%)]\tLoss Char: 0.000000 Loss Font: 0.663268\n",
      "Train Epoch: 16 [57600/60000 (96%)]\tLoss Char: 0.000000 Loss Font: 0.682880\n",
      "\n",
      "Test set: Accuracy Char : 10000/10000 (100.00%)\n",
      "          Accuracy Font : 5410/10000 (54.10%)\n",
      "Train Epoch: 17 [0/60000 (0%)]\tLoss Char: 0.000000 Loss Font: 0.691605\n",
      "Train Epoch: 17 [6400/60000 (11%)]\tLoss Char: 0.000000 Loss Font: 0.672657\n",
      "Train Epoch: 17 [12800/60000 (21%)]\tLoss Char: 0.000000 Loss Font: 0.688819\n",
      "Train Epoch: 17 [19200/60000 (32%)]\tLoss Char: 0.000000 Loss Font: 0.686678\n",
      "Train Epoch: 17 [25600/60000 (43%)]\tLoss Char: 0.000000 Loss Font: 0.664842\n",
      "Train Epoch: 17 [32000/60000 (53%)]\tLoss Char: 0.000000 Loss Font: 0.663528\n",
      "Train Epoch: 17 [38400/60000 (64%)]\tLoss Char: 0.000000 Loss Font: 0.695802\n",
      "Train Epoch: 17 [44800/60000 (75%)]\tLoss Char: 0.000000 Loss Font: 0.683674\n",
      "Train Epoch: 17 [51200/60000 (85%)]\tLoss Char: 0.000000 Loss Font: 0.664334\n",
      "Train Epoch: 17 [57600/60000 (96%)]\tLoss Char: 0.000000 Loss Font: 0.683041\n",
      "\n",
      "Test set: Accuracy Char : 10000/10000 (100.00%)\n",
      "          Accuracy Font : 5457/10000 (54.57%)\n",
      "Train Epoch: 18 [0/60000 (0%)]\tLoss Char: 0.000000 Loss Font: 0.688675\n",
      "Train Epoch: 18 [6400/60000 (11%)]\tLoss Char: 0.000000 Loss Font: 0.669871\n",
      "Train Epoch: 18 [12800/60000 (21%)]\tLoss Char: 0.000000 Loss Font: 0.687979\n",
      "Train Epoch: 18 [19200/60000 (32%)]\tLoss Char: 0.000000 Loss Font: 0.689797\n",
      "Train Epoch: 18 [25600/60000 (43%)]\tLoss Char: 0.000000 Loss Font: 0.661711\n",
      "Train Epoch: 18 [32000/60000 (53%)]\tLoss Char: 0.000000 Loss Font: 0.663981\n",
      "Train Epoch: 18 [38400/60000 (64%)]\tLoss Char: 0.000000 Loss Font: 0.700009\n",
      "Train Epoch: 18 [44800/60000 (75%)]\tLoss Char: 0.000000 Loss Font: 0.682487\n",
      "Train Epoch: 18 [51200/60000 (85%)]\tLoss Char: 0.000000 Loss Font: 0.663821\n",
      "Train Epoch: 18 [57600/60000 (96%)]\tLoss Char: 0.000000 Loss Font: 0.683154\n",
      "\n",
      "Test set: Accuracy Char : 10000/10000 (100.00%)\n",
      "          Accuracy Font : 5446/10000 (54.46%)\n",
      "Train Epoch: 19 [0/60000 (0%)]\tLoss Char: 0.000000 Loss Font: 0.687355\n",
      "Train Epoch: 19 [6400/60000 (11%)]\tLoss Char: 0.000000 Loss Font: 0.669563\n",
      "Train Epoch: 19 [12800/60000 (21%)]\tLoss Char: 0.000000 Loss Font: 0.688216\n",
      "Train Epoch: 19 [19200/60000 (32%)]\tLoss Char: 0.000000 Loss Font: 0.685210\n",
      "Train Epoch: 19 [25600/60000 (43%)]\tLoss Char: 0.000000 Loss Font: 0.665411\n",
      "Train Epoch: 19 [32000/60000 (53%)]\tLoss Char: 0.000000 Loss Font: 0.663749\n",
      "Train Epoch: 19 [38400/60000 (64%)]\tLoss Char: 0.000000 Loss Font: 0.700905\n",
      "Train Epoch: 19 [44800/60000 (75%)]\tLoss Char: 0.000000 Loss Font: 0.682693\n",
      "Train Epoch: 19 [51200/60000 (85%)]\tLoss Char: 0.000000 Loss Font: 0.663639\n",
      "Train Epoch: 19 [57600/60000 (96%)]\tLoss Char: 0.000000 Loss Font: 0.682816\n",
      "\n",
      "Test set: Accuracy Char : 10000/10000 (100.00%)\n",
      "          Accuracy Font : 5463/10000 (54.63%)\n",
      "Train Epoch: 20 [0/60000 (0%)]\tLoss Char: 0.000000 Loss Font: 0.688727\n",
      "Train Epoch: 20 [6400/60000 (11%)]\tLoss Char: 0.000000 Loss Font: 0.670392\n",
      "Train Epoch: 20 [12800/60000 (21%)]\tLoss Char: 0.000000 Loss Font: 0.685503\n",
      "Train Epoch: 20 [19200/60000 (32%)]\tLoss Char: 0.000000 Loss Font: 0.686835\n",
      "Train Epoch: 20 [25600/60000 (43%)]\tLoss Char: 0.000000 Loss Font: 0.668431\n",
      "Train Epoch: 20 [32000/60000 (53%)]\tLoss Char: 0.000000 Loss Font: 0.662481\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Train Epoch: 20 [38400/60000 (64%)]\tLoss Char: 0.000000 Loss Font: 0.696285\n",
      "Train Epoch: 20 [44800/60000 (75%)]\tLoss Char: 0.000000 Loss Font: 0.682676\n",
      "Train Epoch: 20 [51200/60000 (85%)]\tLoss Char: 0.000000 Loss Font: 0.664621\n",
      "Train Epoch: 20 [57600/60000 (96%)]\tLoss Char: 0.000000 Loss Font: 0.681711\n",
      "\n",
      "Test set: Accuracy Char : 10000/10000 (100.00%)\n",
      "          Accuracy Font : 5527/10000 (55.27%)\n",
      "Train Epoch: 21 [0/60000 (0%)]\tLoss Char: 0.000000 Loss Font: 0.683505\n",
      "Train Epoch: 21 [6400/60000 (11%)]\tLoss Char: 0.000000 Loss Font: 0.668380\n",
      "Train Epoch: 21 [12800/60000 (21%)]\tLoss Char: 0.000000 Loss Font: 0.683663\n"
     ]
    },
    {
     "ename": "KeyboardInterrupt",
     "evalue": "",
     "output_type": "error",
     "traceback": [
      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[0;31mKeyboardInterrupt\u001b[0m                         Traceback (most recent call last)",
      "\u001b[0;32m<ipython-input-144-9704ef20d2dc>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m\u001b[0m\n\u001b[1;32m      1\u001b[0m \u001b[0mprec\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;36m3\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;36m7\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;36m5\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m----> 2\u001b[0;31m \u001b[0mtest_perfs_char\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mtest_perfs_font\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mtest_collateral\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mprec\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mrm_outlier_frac\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;36m99.9\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m",
      "\u001b[0;32m<ipython-input-143-57e67c94522d>\u001b[0m in \u001b[0;36mtest_collateral\u001b[0;34m(prec, rm_outlier_frac)\u001b[0m\n\u001b[1;32m     48\u001b[0m         collateral.train(\n\u001b[1;32m     49\u001b[0m             \u001b[0margs\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mmodel\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mtrain_loader_prec\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0moptimizer\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mepoch\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0malpha\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m---> 50\u001b[0;31m             \u001b[0minitial_phase\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mperturbate\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mrecover\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mnew_adversary\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m     51\u001b[0m         )\n\u001b[1;32m     52\u001b[0m         \u001b[0mtest_perf_char\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mtest_perf_font\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mcollateral\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mtest\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0margs\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mmodel\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mtest_loader_prec\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mnew_adversary\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m~/code/collateral-learning/learn/collateral/train.py\u001b[0m in \u001b[0;36mtrain\u001b[0;34m(args, model, train_loader, optimizer, epoch, alpha, initial_phase, perturbate, recover, new_collateral)\u001b[0m\n\u001b[1;32m     42\u001b[0m                 \u001b[0moutput_font\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mmodel\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mforward_adv_font\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mdata\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     43\u001b[0m             \u001b[0;32melse\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m---> 44\u001b[0;31m                 \u001b[0moutput_font\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mmodel\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mforward_font\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mdata\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m     45\u001b[0m             \u001b[0mloss_font\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mF\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mnll_loss\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0moutput_font\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mtarget_font\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     46\u001b[0m             \u001b[0mloss_font\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mbackward\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m<ipython-input-27-f2caeb7970c5>\u001b[0m in \u001b[0;36mforward_font\u001b[0;34m(self, x)\u001b[0m\n\u001b[1;32m     62\u001b[0m     \u001b[0;32mdef\u001b[0m \u001b[0mforward_font\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mx\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     63\u001b[0m         \u001b[0mx\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mquad\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mx\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m---> 64\u001b[0;31m         \u001b[0mx\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mfont_net\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mx\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m     65\u001b[0m         \u001b[0;32mreturn\u001b[0m \u001b[0mF\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mlog_softmax\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mx\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mdim\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;36m1\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     66\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m<ipython-input-27-f2caeb7970c5>\u001b[0m in \u001b[0;36mfont_net\u001b[0;34m(self, x)\u001b[0m\n\u001b[1;32m     42\u001b[0m     \u001b[0;32mdef\u001b[0m \u001b[0mfont_net\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mx\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     43\u001b[0m         \u001b[0;31m# --- Junction\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m---> 44\u001b[0;31m         \u001b[0mx\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mjct\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mx\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m     45\u001b[0m         \u001b[0mx\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mx\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mview\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m-\u001b[0m\u001b[0;36m1\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;36m1\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;36m28\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;36m28\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     46\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m~/code/env/pysyft/lib/python3.7/site-packages/torch/nn/modules/module.py\u001b[0m in \u001b[0;36m__call__\u001b[0;34m(self, *input, **kwargs)\u001b[0m\n\u001b[1;32m    488\u001b[0m         \u001b[0;32mfor\u001b[0m \u001b[0mhook\u001b[0m \u001b[0;32min\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_forward_pre_hooks\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mvalues\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    489\u001b[0m             \u001b[0mhook\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0minput\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 490\u001b[0;31m         \u001b[0;32mif\u001b[0m \u001b[0mtorch\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_C\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_get_tracing_state\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    491\u001b[0m             \u001b[0mresult\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_slow_forward\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m*\u001b[0m\u001b[0minput\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m**\u001b[0m\u001b[0mkwargs\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    492\u001b[0m         \u001b[0;32melse\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;31mKeyboardInterrupt\u001b[0m: "
     ]
    }
   ],
   "source": [
    "prec=(3, 7, 5)\n",
    "test_perfs_char, test_perfs_font = test_collateral(prec, rm_outlier_frac=99.9)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
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