tutorials/Part 04 - Reducing the private output size.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Functional Encryption - Classification and information leakage\n",
    "\n",
    "\n",
    "## Purpose\n",
    "\n",
    "We proceed like part 3 except we put a public network over the private output not only for the collateral task but also for main one.\n",
    "\n",
    "Actually, we also provide more epochs for the main task to train as this training is fast and more epochs are required when the model gets deeper. The model isn't deeper for the collateral task so we don't have to increase the number of epochs there.\n",
    "\n",
    "Moreover, we provide a new parameter `PRIVATE_OUTPUT_SIZE = 10` which controls the size of the private output, as this doesn't need anymore to be 10 for we have another public network on top of it.\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# 4 Tempering Collateral Learning\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We will use the code directly from the repo, to make the notebook more readable. Functions are similar to those presented earlier."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "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": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "import torch\n",
    "import torch.nn as nn\n",
    "import torch.optim as optim\n",
    "import torch.nn.functional as F\n",
    "\n",
    "import learn\n",
    "from learn import show_results\n",
    "# We now import from the collateral module\n",
    "from learn import collateral"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 4.1 Simultaneous training"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We will do the training in a single phase, as the collateral phase does not interfere with the main one.\n",
    "\n",
    "We have reduced the collateral network size, it now just a simple FFN, to keep computation fast. We'll get back to CNN later don't worry!\n",
    "\n",
    "The way the procedure is handled is the following: we alternate epochs, one on the main task which updates the quadratic and the character part of the network, and one on the collateral task which only updates the font family part of the network.\n",
    "\n",
    "Therefore, if we call `Q` the quadratic part, `C` the net on top of `Q` used to predict characters and `F` the net for predicting families, the global network will be structured as follows:\n",
    "```\n",
    "  |\n",
    "  Q\n",
    " / \\\n",
    "C   F\n",
    "|   |\n",
    "```\n",
    "\n",
    "We will do a 2-phase process:\n",
    " - `Optimize: Q + C`\n",
    " - `Optimize: Freezed(Q) + F`"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "PRIVATE_OUTPUT_SIZE = 10\n",
    "N_CHARS = 10\n",
    "N_FONTS = 2\n",
    "\n",
    "class Parser:\n",
    "    def __init__(self):\n",
    "        self.epochs = 10\n",
    "        self.lr = 0.002\n",
    "        self.momentum = 0.5\n",
    "        self.test_batch_size = 1000\n",
    "        self.batch_size = 64\n",
    "        self.log_interval = 300\n",
    "\n",
    "class CollateralNet(nn.Module):\n",
    "    def __init__(self):\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",
    "        # --- FFN for families\n",
    "        self.lin3 = nn.Linear(PRIVATE_OUTPUT_SIZE, 32)\n",
    "        self.lin4 = nn.Linear(32, N_FONTS)\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",
    "        # --- FFN\n",
    "        x = F.relu(x)\n",
    "        x = F.relu(self.lin3(x))\n",
    "        x = self.lin4(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.lin3, self.lin4]\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\n",
    "    \n",
    "    "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [],
   "source": [
    "def dual_learning_phase(model):\n",
    "    \"\"\"\n",
    "    Perform a dual learning phase\n",
    "    \"\"\"\n",
    "    args = Parser()\n",
    "    \n",
    "    train_loader, test_loader = collateral.get_data_loaders(args)\n",
    "\n",
    "    #optimizer = optim.Adam(model.parameters(), lr=args.lr)\n",
    "    optimizer = optim.SGD(model.parameters(), lr=args.lr, momentum=args.momentum)\n",
    "    \n",
    "    test_perfs_char = []\n",
    "    test_perfs_font = []\n",
    "    \n",
    "    for epoch in range(1, args.epochs + 1):\n",
    "        collateral.train(args, model, train_loader, optimizer, epoch, 0, True, False, False, False)\n",
    "        test_perf_char, test_perf_font = collateral.test(args, model, test_loader, False)\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"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [],
   "source": [
    "results = {}"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {
    "scrolled": true
   },
   "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: 2.288454 Loss Font: 0.718573\n",
      "Train Epoch: 1 [19200/60000 (32%)]\tLoss Char: 1.878332 Loss Font: 0.690706\n",
      "Train Epoch: 1 [38400/60000 (64%)]\tLoss Char: 0.804431 Loss Font: 0.575631\n",
      "Train Epoch: 1 [57600/60000 (96%)]\tLoss Char: 0.386138 Loss Font: 0.720004\n",
      "\n",
      "Test set: Accuracy Char : 8642/10000 (86.42%)\n",
      "          Accuracy Font : 5981/10000 (59.81%)\n",
      "Train Epoch: 2 [0/60000 (0%)]\tLoss Char: 0.373366 Loss Font: 0.633376\n",
      "Train Epoch: 2 [19200/60000 (32%)]\tLoss Char: 0.397147 Loss Font: 0.640782\n",
      "Train Epoch: 2 [38400/60000 (64%)]\tLoss Char: 0.240672 Loss Font: 0.590604\n",
      "Train Epoch: 2 [57600/60000 (96%)]\tLoss Char: 0.096954 Loss Font: 0.601987\n",
      "\n",
      "Test set: Accuracy Char : 9532/10000 (95.32%)\n",
      "          Accuracy Font : 6719/10000 (67.19%)\n",
      "Train Epoch: 3 [0/60000 (0%)]\tLoss Char: 0.172076 Loss Font: 0.620654\n",
      "Train Epoch: 3 [19200/60000 (32%)]\tLoss Char: 0.143340 Loss Font: 0.624026\n",
      "Train Epoch: 3 [38400/60000 (64%)]\tLoss Char: 0.037905 Loss Font: 0.472311\n",
      "Train Epoch: 3 [57600/60000 (96%)]\tLoss Char: 0.054102 Loss Font: 0.533601\n",
      "\n",
      "Test set: Accuracy Char : 9665/10000 (96.65%)\n",
      "          Accuracy Font : 7081/10000 (70.81%)\n",
      "Train Epoch: 4 [0/60000 (0%)]\tLoss Char: 0.034884 Loss Font: 0.613300\n",
      "Train Epoch: 4 [19200/60000 (32%)]\tLoss Char: 0.050097 Loss Font: 0.591982\n",
      "Train Epoch: 4 [38400/60000 (64%)]\tLoss Char: 0.027165 Loss Font: 0.481869\n",
      "Train Epoch: 4 [57600/60000 (96%)]\tLoss Char: 0.071796 Loss Font: 0.572117\n",
      "\n",
      "Test set: Accuracy Char : 9782/10000 (97.82%)\n",
      "          Accuracy Font : 7438/10000 (74.38%)\n",
      "Train Epoch: 5 [0/60000 (0%)]\tLoss Char: 0.040508 Loss Font: 0.573213\n",
      "Train Epoch: 5 [19200/60000 (32%)]\tLoss Char: 0.020913 Loss Font: 0.447759\n",
      "Train Epoch: 5 [38400/60000 (64%)]\tLoss Char: 0.045977 Loss Font: 0.494982\n",
      "Train Epoch: 5 [57600/60000 (96%)]\tLoss Char: 0.035796 Loss Font: 0.513471\n",
      "\n",
      "Test set: Accuracy Char : 9837/10000 (98.37%)\n",
      "          Accuracy Font : 7730/10000 (77.30%)\n",
      "Train Epoch: 6 [0/60000 (0%)]\tLoss Char: 0.110652 Loss Font: 0.511680\n",
      "Train Epoch: 6 [19200/60000 (32%)]\tLoss Char: 0.055510 Loss Font: 0.509101\n",
      "Train Epoch: 6 [38400/60000 (64%)]\tLoss Char: 0.037457 Loss Font: 0.429949\n",
      "Train Epoch: 6 [57600/60000 (96%)]\tLoss Char: 0.017317 Loss Font: 0.462516\n",
      "\n",
      "Test set: Accuracy Char : 9861/10000 (98.61%)\n",
      "          Accuracy Font : 8009/10000 (80.09%)\n",
      "Train Epoch: 7 [0/60000 (0%)]\tLoss Char: 0.029637 Loss Font: 0.504016\n",
      "Train Epoch: 7 [19200/60000 (32%)]\tLoss Char: 0.015241 Loss Font: 0.300953\n",
      "Train Epoch: 7 [38400/60000 (64%)]\tLoss Char: 0.014054 Loss Font: 0.407681\n",
      "Train Epoch: 7 [57600/60000 (96%)]\tLoss Char: 0.005842 Loss Font: 0.336304\n",
      "\n",
      "Test set: Accuracy Char : 9813/10000 (98.13%)\n",
      "          Accuracy Font : 8035/10000 (80.35%)\n",
      "Train Epoch: 8 [0/60000 (0%)]\tLoss Char: 0.031052 Loss Font: 0.459736\n",
      "Train Epoch: 8 [19200/60000 (32%)]\tLoss Char: 0.001792 Loss Font: 0.359137\n",
      "Train Epoch: 8 [38400/60000 (64%)]\tLoss Char: 0.045489 Loss Font: 0.342570\n",
      "Train Epoch: 8 [57600/60000 (96%)]\tLoss Char: 0.015744 Loss Font: 0.390139\n",
      "\n",
      "Test set: Accuracy Char : 9905/10000 (99.05%)\n",
      "          Accuracy Font : 8146/10000 (81.46%)\n",
      "Train Epoch: 9 [0/60000 (0%)]\tLoss Char: 0.000978 Loss Font: 0.327789\n",
      "Train Epoch: 9 [19200/60000 (32%)]\tLoss Char: 0.005115 Loss Font: 0.440453\n",
      "Train Epoch: 9 [38400/60000 (64%)]\tLoss Char: 0.076040 Loss Font: 0.429572\n",
      "Train Epoch: 9 [57600/60000 (96%)]\tLoss Char: 0.029108 Loss Font: 0.282935\n",
      "\n",
      "Test set: Accuracy Char : 9899/10000 (98.99%)\n",
      "          Accuracy Font : 7982/10000 (79.82%)\n",
      "Train Epoch: 10 [0/60000 (0%)]\tLoss Char: 0.003419 Loss Font: 0.471650\n",
      "Train Epoch: 10 [19200/60000 (32%)]\tLoss Char: 0.003097 Loss Font: 0.365084\n",
      "Train Epoch: 10 [38400/60000 (64%)]\tLoss Char: 0.005389 Loss Font: 0.423010\n",
      "Train Epoch: 10 [57600/60000 (96%)]\tLoss Char: 0.051891 Loss Font: 0.488096\n",
      "\n",
      "Test set: Accuracy Char : 9913/10000 (99.13%)\n",
      "          Accuracy Font : 8341/10000 (83.41%)\n"
     ]
    }
   ],
   "source": [
    "model = CollateralNet()\n",
    "test_perfs_char_perturbate, test_perfs_font_perturbate = dual_learning_phase(model)\n",
    "results[f\"Main task with output size {PRIVATE_OUTPUT_SIZE}\"] = test_perfs_char_perturbate\n",
    "results[f\"Collateral task with output size {PRIVATE_OUTPUT_SIZE}\"] = test_perfs_font_perturbate"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 720x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "show_results(results, title=\"Main and Collateral accuracies in a dual learning phase\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "scrolled": true
   },
   "source": [
    "As expected, the main task is not affected by our new process. As expected, the collateral network performs less well compared to Part 3 which is also expected as it is way smaller, but still achieves >82% accuracy. We'll do the test with the CNN at the end of this tutorial to make sure we can infer results on CNN looking at smaller networks which are faster to train."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 4.2 Reducing the private output size\n",
    "\n",
    "\n",
    "We now change the output of the private network to see how the main and collateral accuracy degrade."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      " ----- 10\n",
      "Training set 60000 items\n",
      "Testing set  10000 items\n",
      "Train Epoch: 1 [0/60000 (0%)]\tLoss Char: 2.269649 Loss Font: 0.708297\n",
      "Train Epoch: 1 [19200/60000 (32%)]\tLoss Char: 1.616001 Loss Font: 0.674110\n",
      "Train Epoch: 1 [38400/60000 (64%)]\tLoss Char: 0.591641 Loss Font: 0.627130\n",
      "Train Epoch: 1 [57600/60000 (96%)]\tLoss Char: 0.459737 Loss Font: 0.605778\n",
      "\n",
      "Test set: Accuracy Char : 8791/10000 (87.91%)\n",
      "          Accuracy Font : 6617/10000 (66.17%)\n",
      "Train Epoch: 2 [0/60000 (0%)]\tLoss Char: 0.451807 Loss Font: 0.588058\n",
      "Train Epoch: 2 [19200/60000 (32%)]\tLoss Char: 0.382171 Loss Font: 0.644877\n",
      "Train Epoch: 2 [38400/60000 (64%)]\tLoss Char: 0.204297 Loss Font: 0.607601\n",
      "Train Epoch: 2 [57600/60000 (96%)]\tLoss Char: 0.037514 Loss Font: 0.515182\n",
      "\n",
      "Test set: Accuracy Char : 9569/10000 (95.69%)\n",
      "          Accuracy Font : 7207/10000 (72.07%)\n",
      "Train Epoch: 3 [0/60000 (0%)]\tLoss Char: 0.234842 Loss Font: 0.543624\n",
      "Train Epoch: 3 [19200/60000 (32%)]\tLoss Char: 0.144722 Loss Font: 0.471594\n",
      "Train Epoch: 3 [38400/60000 (64%)]\tLoss Char: 0.033942 Loss Font: 0.512029\n",
      "Train Epoch: 3 [57600/60000 (96%)]\tLoss Char: 0.042745 Loss Font: 0.474944\n",
      "\n",
      "Test set: Accuracy Char : 9671/10000 (96.71%)\n",
      "          Accuracy Font : 7815/10000 (78.15%)\n",
      "Train Epoch: 4 [0/60000 (0%)]\tLoss Char: 0.053908 Loss Font: 0.379256\n",
      "Train Epoch: 4 [19200/60000 (32%)]\tLoss Char: 0.019423 Loss Font: 0.464003\n",
      "Train Epoch: 4 [38400/60000 (64%)]\tLoss Char: 0.060639 Loss Font: 0.343484\n",
      "Train Epoch: 4 [57600/60000 (96%)]\tLoss Char: 0.060828 Loss Font: 0.390431\n",
      "\n",
      "Test set: Accuracy Char : 9784/10000 (97.84%)\n",
      "          Accuracy Font : 7997/10000 (79.97%)\n",
      "Train Epoch: 5 [0/60000 (0%)]\tLoss Char: 0.048515 Loss Font: 0.393689\n",
      "Train Epoch: 5 [19200/60000 (32%)]\tLoss Char: 0.028303 Loss Font: 0.310104\n",
      "Train Epoch: 5 [38400/60000 (64%)]\tLoss Char: 0.041884 Loss Font: 0.460928\n",
      "Train Epoch: 5 [57600/60000 (96%)]\tLoss Char: 0.012439 Loss Font: 0.272664\n",
      "\n",
      "Test set: Accuracy Char : 9800/10000 (98.00%)\n",
      "          Accuracy Font : 8107/10000 (81.07%)\n",
      "Train Epoch: 6 [0/60000 (0%)]\tLoss Char: 0.082171 Loss Font: 0.474009\n",
      "Train Epoch: 6 [19200/60000 (32%)]\tLoss Char: 0.097228 Loss Font: 0.478209\n",
      "Train Epoch: 6 [38400/60000 (64%)]\tLoss Char: 0.041225 Loss Font: 0.329477\n",
      "Train Epoch: 6 [57600/60000 (96%)]\tLoss Char: 0.005404 Loss Font: 0.323991\n",
      "\n",
      "Test set: Accuracy Char : 9817/10000 (98.17%)\n",
      "          Accuracy Font : 8269/10000 (82.69%)\n",
      "Train Epoch: 7 [0/60000 (0%)]\tLoss Char: 0.034444 Loss Font: 0.423975\n",
      "Train Epoch: 7 [19200/60000 (32%)]\tLoss Char: 0.022005 Loss Font: 0.404997\n",
      "Train Epoch: 7 [38400/60000 (64%)]\tLoss Char: 0.009552 Loss Font: 0.318323\n",
      "Train Epoch: 7 [57600/60000 (96%)]\tLoss Char: 0.003057 Loss Font: 0.578605\n",
      "\n",
      "Test set: Accuracy Char : 9856/10000 (98.56%)\n",
      "          Accuracy Font : 8303/10000 (83.03%)\n",
      "Train Epoch: 8 [0/60000 (0%)]\tLoss Char: 0.031105 Loss Font: 0.443438\n",
      "Train Epoch: 8 [19200/60000 (32%)]\tLoss Char: 0.004255 Loss Font: 0.271911\n",
      "Train Epoch: 8 [38400/60000 (64%)]\tLoss Char: 0.054545 Loss Font: 0.267766\n",
      "Train Epoch: 8 [57600/60000 (96%)]\tLoss Char: 0.011697 Loss Font: 0.324914\n",
      "\n",
      "Test set: Accuracy Char : 9858/10000 (98.58%)\n",
      "          Accuracy Font : 8416/10000 (84.16%)\n",
      "Train Epoch: 9 [0/60000 (0%)]\tLoss Char: 0.004240 Loss Font: 0.430130\n",
      "Train Epoch: 9 [19200/60000 (32%)]\tLoss Char: 0.003841 Loss Font: 0.438880\n",
      "Train Epoch: 9 [38400/60000 (64%)]\tLoss Char: 0.080681 Loss Font: 0.380081\n",
      "Train Epoch: 9 [57600/60000 (96%)]\tLoss Char: 0.013111 Loss Font: 0.322915\n",
      "\n",
      "Test set: Accuracy Char : 9882/10000 (98.82%)\n",
      "          Accuracy Font : 8383/10000 (83.83%)\n",
      "Train Epoch: 10 [0/60000 (0%)]\tLoss Char: 0.007307 Loss Font: 0.348269\n",
      "Train Epoch: 10 [19200/60000 (32%)]\tLoss Char: 0.000959 Loss Font: 0.509799\n",
      "Train Epoch: 10 [38400/60000 (64%)]\tLoss Char: 0.005925 Loss Font: 0.433465\n",
      "Train Epoch: 10 [57600/60000 (96%)]\tLoss Char: 0.009379 Loss Font: 0.366334\n",
      "\n",
      "Test set: Accuracy Char : 9884/10000 (98.84%)\n",
      "          Accuracy Font : 8522/10000 (85.22%)\n",
      "\n",
      " ----- 9\n",
      "Training set 60000 items\n",
      "Testing set  10000 items\n",
      "Train Epoch: 1 [0/60000 (0%)]\tLoss Char: 2.290752 Loss Font: 0.731439\n",
      "Train Epoch: 1 [19200/60000 (32%)]\tLoss Char: 2.144266 Loss Font: 0.705699\n",
      "Train Epoch: 1 [38400/60000 (64%)]\tLoss Char: 0.679333 Loss Font: 0.666036\n",
      "Train Epoch: 1 [57600/60000 (96%)]\tLoss Char: 0.497185 Loss Font: 0.652838\n",
      "\n",
      "Test set: Accuracy Char : 8582/10000 (85.82%)\n",
      "          Accuracy Font : 5695/10000 (56.95%)\n",
      "Train Epoch: 2 [0/60000 (0%)]\tLoss Char: 0.453146 Loss Font: 0.716914\n",
      "Train Epoch: 2 [19200/60000 (32%)]\tLoss Char: 0.337143 Loss Font: 0.698862\n",
      "Train Epoch: 2 [38400/60000 (64%)]\tLoss Char: 0.210882 Loss Font: 0.670526\n",
      "Train Epoch: 2 [57600/60000 (96%)]\tLoss Char: 0.097874 Loss Font: 0.646670\n",
      "\n",
      "Test set: Accuracy Char : 9567/10000 (95.67%)\n",
      "          Accuracy Font : 6488/10000 (64.88%)\n",
      "Train Epoch: 3 [0/60000 (0%)]\tLoss Char: 0.219386 Loss Font: 0.609541\n",
      "Train Epoch: 3 [19200/60000 (32%)]\tLoss Char: 0.217925 Loss Font: 0.575599\n",
      "Train Epoch: 3 [38400/60000 (64%)]\tLoss Char: 0.060202 Loss Font: 0.565363\n",
      "Train Epoch: 3 [57600/60000 (96%)]\tLoss Char: 0.020350 Loss Font: 0.538482\n",
      "\n",
      "Test set: Accuracy Char : 9705/10000 (97.05%)\n",
      "          Accuracy Font : 7206/10000 (72.06%)\n",
      "Train Epoch: 4 [0/60000 (0%)]\tLoss Char: 0.041911 Loss Font: 0.586926\n",
      "Train Epoch: 4 [19200/60000 (32%)]\tLoss Char: 0.076958 Loss Font: 0.513714\n",
      "Train Epoch: 4 [38400/60000 (64%)]\tLoss Char: 0.021288 Loss Font: 0.449231\n",
      "Train Epoch: 4 [57600/60000 (96%)]\tLoss Char: 0.071424 Loss Font: 0.480911\n",
      "\n",
      "Test set: Accuracy Char : 9798/10000 (97.98%)\n",
      "          Accuracy Font : 7501/10000 (75.01%)\n",
      "Train Epoch: 5 [0/60000 (0%)]\tLoss Char: 0.152592 Loss Font: 0.481703\n",
      "Train Epoch: 5 [19200/60000 (32%)]\tLoss Char: 0.021700 Loss Font: 0.435381\n",
      "Train Epoch: 5 [38400/60000 (64%)]\tLoss Char: 0.111967 Loss Font: 0.405505\n",
      "Train Epoch: 5 [57600/60000 (96%)]\tLoss Char: 0.048643 Loss Font: 0.516910\n",
      "\n",
      "Test set: Accuracy Char : 9845/10000 (98.45%)\n",
      "          Accuracy Font : 7853/10000 (78.53%)\n",
      "Train Epoch: 6 [0/60000 (0%)]\tLoss Char: 0.145860 Loss Font: 0.608430\n",
      "Train Epoch: 6 [19200/60000 (32%)]\tLoss Char: 0.076398 Loss Font: 0.515699\n",
      "Train Epoch: 6 [38400/60000 (64%)]\tLoss Char: 0.046870 Loss Font: 0.458234\n",
      "Train Epoch: 6 [57600/60000 (96%)]\tLoss Char: 0.007161 Loss Font: 0.407923\n",
      "\n",
      "Test set: Accuracy Char : 9848/10000 (98.48%)\n",
      "          Accuracy Font : 7846/10000 (78.46%)\n",
      "Train Epoch: 7 [0/60000 (0%)]\tLoss Char: 0.080616 Loss Font: 0.472494\n",
      "Train Epoch: 7 [19200/60000 (32%)]\tLoss Char: 0.013851 Loss Font: 0.380437\n",
      "Train Epoch: 7 [38400/60000 (64%)]\tLoss Char: 0.013727 Loss Font: 0.462330\n",
      "Train Epoch: 7 [57600/60000 (96%)]\tLoss Char: 0.002038 Loss Font: 0.394799\n",
      "\n",
      "Test set: Accuracy Char : 9871/10000 (98.71%)\n",
      "          Accuracy Font : 8014/10000 (80.14%)\n",
      "Train Epoch: 8 [0/60000 (0%)]\tLoss Char: 0.013133 Loss Font: 0.472011\n",
      "Train Epoch: 8 [19200/60000 (32%)]\tLoss Char: 0.002780 Loss Font: 0.459271\n",
      "Train Epoch: 8 [38400/60000 (64%)]\tLoss Char: 0.072915 Loss Font: 0.474973\n",
      "Train Epoch: 8 [57600/60000 (96%)]\tLoss Char: 0.008169 Loss Font: 0.344777\n",
      "\n",
      "Test set: Accuracy Char : 9887/10000 (98.87%)\n",
      "          Accuracy Font : 8111/10000 (81.11%)\n",
      "Train Epoch: 9 [0/60000 (0%)]\tLoss Char: 0.004419 Loss Font: 0.432305\n",
      "Train Epoch: 9 [19200/60000 (32%)]\tLoss Char: 0.002034 Loss Font: 0.402524\n",
      "Train Epoch: 9 [38400/60000 (64%)]\tLoss Char: 0.056151 Loss Font: 0.412566\n",
      "Train Epoch: 9 [57600/60000 (96%)]\tLoss Char: 0.021515 Loss Font: 0.244458\n",
      "\n",
      "Test set: Accuracy Char : 9896/10000 (98.96%)\n",
      "          Accuracy Font : 7679/10000 (76.79%)\n",
      "Train Epoch: 10 [0/60000 (0%)]\tLoss Char: 0.003713 Loss Font: 0.444535\n",
      "Train Epoch: 10 [19200/60000 (32%)]\tLoss Char: 0.006426 Loss Font: 0.492533\n",
      "Train Epoch: 10 [38400/60000 (64%)]\tLoss Char: 0.002501 Loss Font: 0.484679\n",
      "Train Epoch: 10 [57600/60000 (96%)]\tLoss Char: 0.005489 Loss Font: 0.405778\n",
      "\n",
      "Test set: Accuracy Char : 9896/10000 (98.96%)\n",
      "          Accuracy Font : 8199/10000 (81.99%)\n",
      "\n",
      " ----- 8\n",
      "Training set 60000 items\n",
      "Testing set  10000 items\n",
      "Train Epoch: 1 [0/60000 (0%)]\tLoss Char: 2.288626 Loss Font: 0.694659\n",
      "Train Epoch: 1 [19200/60000 (32%)]\tLoss Char: 1.666921 Loss Font: 0.685386\n",
      "Train Epoch: 1 [38400/60000 (64%)]\tLoss Char: 0.686711 Loss Font: 0.660161\n",
      "Train Epoch: 1 [57600/60000 (96%)]\tLoss Char: 0.566376 Loss Font: 0.660253\n",
      "\n",
      "Test set: Accuracy Char : 8425/10000 (84.25%)\n",
      "          Accuracy Font : 6028/10000 (60.28%)\n",
      "Train Epoch: 2 [0/60000 (0%)]\tLoss Char: 0.492938 Loss Font: 0.683992\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Train Epoch: 2 [19200/60000 (32%)]\tLoss Char: 0.501741 Loss Font: 0.659975\n",
      "Train Epoch: 2 [38400/60000 (64%)]\tLoss Char: 0.299572 Loss Font: 0.683927\n",
      "Train Epoch: 2 [57600/60000 (96%)]\tLoss Char: 0.065714 Loss Font: 0.637012\n",
      "\n",
      "Test set: Accuracy Char : 9505/10000 (95.05%)\n",
      "          Accuracy Font : 6426/10000 (64.26%)\n",
      "Train Epoch: 3 [0/60000 (0%)]\tLoss Char: 0.209793 Loss Font: 0.648991\n",
      "Train Epoch: 3 [19200/60000 (32%)]\tLoss Char: 0.158073 Loss Font: 0.621779\n",
      "Train Epoch: 3 [38400/60000 (64%)]\tLoss Char: 0.021595 Loss Font: 0.567591\n",
      "Train Epoch: 3 [57600/60000 (96%)]\tLoss Char: 0.036201 Loss Font: 0.609439\n",
      "\n",
      "Test set: Accuracy Char : 9692/10000 (96.92%)\n",
      "          Accuracy Font : 6726/10000 (67.26%)\n",
      "Train Epoch: 4 [0/60000 (0%)]\tLoss Char: 0.130387 Loss Font: 0.551832\n",
      "Train Epoch: 4 [19200/60000 (32%)]\tLoss Char: 0.108617 Loss Font: 0.548671\n",
      "Train Epoch: 4 [38400/60000 (64%)]\tLoss Char: 0.040601 Loss Font: 0.653528\n",
      "Train Epoch: 4 [57600/60000 (96%)]\tLoss Char: 0.165607 Loss Font: 0.583914\n",
      "\n",
      "Test set: Accuracy Char : 9762/10000 (97.62%)\n",
      "          Accuracy Font : 7201/10000 (72.01%)\n",
      "Train Epoch: 5 [0/60000 (0%)]\tLoss Char: 0.034131 Loss Font: 0.535922\n",
      "Train Epoch: 5 [19200/60000 (32%)]\tLoss Char: 0.065573 Loss Font: 0.574175\n",
      "Train Epoch: 5 [38400/60000 (64%)]\tLoss Char: 0.014687 Loss Font: 0.484691\n",
      "Train Epoch: 5 [57600/60000 (96%)]\tLoss Char: 0.112312 Loss Font: 0.546690\n",
      "\n",
      "Test set: Accuracy Char : 9797/10000 (97.97%)\n",
      "          Accuracy Font : 7480/10000 (74.80%)\n",
      "Train Epoch: 6 [0/60000 (0%)]\tLoss Char: 0.121333 Loss Font: 0.562570\n",
      "Train Epoch: 6 [19200/60000 (32%)]\tLoss Char: 0.062533 Loss Font: 0.503228\n",
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      "Train Epoch: 6 [57600/60000 (96%)]\tLoss Char: 0.022780 Loss Font: 0.531299\n",
      "\n",
      "Test set: Accuracy Char : 9813/10000 (98.13%)\n",
      "          Accuracy Font : 7619/10000 (76.19%)\n",
      "Train Epoch: 7 [0/60000 (0%)]\tLoss Char: 0.051277 Loss Font: 0.494009\n",
      "Train Epoch: 7 [19200/60000 (32%)]\tLoss Char: 0.046205 Loss Font: 0.501263\n",
      "Train Epoch: 7 [38400/60000 (64%)]\tLoss Char: 0.007560 Loss Font: 0.487394\n",
      "Train Epoch: 7 [57600/60000 (96%)]\tLoss Char: 0.019422 Loss Font: 0.424807\n",
      "\n",
      "Test set: Accuracy Char : 9840/10000 (98.40%)\n",
      "          Accuracy Font : 7677/10000 (76.77%)\n",
      "Train Epoch: 8 [0/60000 (0%)]\tLoss Char: 0.029538 Loss Font: 0.508400\n",
      "Train Epoch: 8 [19200/60000 (32%)]\tLoss Char: 0.020897 Loss Font: 0.510239\n",
      "Train Epoch: 8 [38400/60000 (64%)]\tLoss Char: 0.130756 Loss Font: 0.473106\n",
      "Train Epoch: 8 [57600/60000 (96%)]\tLoss Char: 0.020892 Loss Font: 0.385006\n",
      "\n",
      "Test set: Accuracy Char : 9848/10000 (98.48%)\n",
      "          Accuracy Font : 7879/10000 (78.79%)\n",
      "Train Epoch: 9 [0/60000 (0%)]\tLoss Char: 0.006768 Loss Font: 0.377589\n",
      "Train Epoch: 9 [19200/60000 (32%)]\tLoss Char: 0.004126 Loss Font: 0.476235\n",
      "Train Epoch: 9 [38400/60000 (64%)]\tLoss Char: 0.049608 Loss Font: 0.496741\n",
      "Train Epoch: 9 [57600/60000 (96%)]\tLoss Char: 0.012208 Loss Font: 0.447643\n",
      "\n",
      "Test set: Accuracy Char : 9839/10000 (98.39%)\n",
      "          Accuracy Font : 7504/10000 (75.04%)\n",
      "Train Epoch: 10 [0/60000 (0%)]\tLoss Char: 0.009649 Loss Font: 0.559905\n",
      "Train Epoch: 10 [19200/60000 (32%)]\tLoss Char: 0.002149 Loss Font: 0.458244\n",
      "Train Epoch: 10 [38400/60000 (64%)]\tLoss Char: 0.002579 Loss Font: 0.728092\n",
      "Train Epoch: 10 [57600/60000 (96%)]\tLoss Char: 0.028048 Loss Font: 0.489256\n",
      "\n",
      "Test set: Accuracy Char : 9865/10000 (98.65%)\n",
      "          Accuracy Font : 7797/10000 (77.97%)\n",
      "\n",
      " ----- 7\n",
      "Training set 60000 items\n",
      "Testing set  10000 items\n",
      "Train Epoch: 1 [0/60000 (0%)]\tLoss Char: 2.321266 Loss Font: 0.701296\n",
      "Train Epoch: 1 [19200/60000 (32%)]\tLoss Char: 2.211762 Loss Font: 0.692773\n",
      "Train Epoch: 1 [38400/60000 (64%)]\tLoss Char: 1.153905 Loss Font: 0.660807\n",
      "Train Epoch: 1 [57600/60000 (96%)]\tLoss Char: 0.643651 Loss Font: 0.734524\n",
      "\n",
      "Test set: Accuracy Char : 8508/10000 (85.08%)\n",
      "          Accuracy Font : 6381/10000 (63.81%)\n",
      "Train Epoch: 2 [0/60000 (0%)]\tLoss Char: 0.460199 Loss Font: 0.693585\n",
      "Train Epoch: 2 [19200/60000 (32%)]\tLoss Char: 0.347450 Loss Font: 0.628499\n",
      "Train Epoch: 2 [38400/60000 (64%)]\tLoss Char: 0.276283 Loss Font: 0.630412\n",
      "Train Epoch: 2 [57600/60000 (96%)]\tLoss Char: 0.092610 Loss Font: 0.638946\n",
      "\n",
      "Test set: Accuracy Char : 9481/10000 (94.81%)\n",
      "          Accuracy Font : 6564/10000 (65.64%)\n",
      "Train Epoch: 3 [0/60000 (0%)]\tLoss Char: 0.202475 Loss Font: 0.609674\n",
      "Train Epoch: 3 [19200/60000 (32%)]\tLoss Char: 0.169098 Loss Font: 0.616062\n",
      "Train Epoch: 3 [38400/60000 (64%)]\tLoss Char: 0.030289 Loss Font: 0.577377\n",
      "Train Epoch: 3 [57600/60000 (96%)]\tLoss Char: 0.020559 Loss Font: 0.595406\n",
      "\n",
      "Test set: Accuracy Char : 9519/10000 (95.19%)\n",
      "          Accuracy Font : 6566/10000 (65.66%)\n",
      "Train Epoch: 4 [0/60000 (0%)]\tLoss Char: 0.085319 Loss Font: 0.678954\n",
      "Train Epoch: 4 [19200/60000 (32%)]\tLoss Char: 0.062847 Loss Font: 0.696236\n",
      "Train Epoch: 4 [38400/60000 (64%)]\tLoss Char: 0.025752 Loss Font: 0.544895\n",
      "Train Epoch: 4 [57600/60000 (96%)]\tLoss Char: 0.204103 Loss Font: 0.663473\n",
      "\n",
      "Test set: Accuracy Char : 9638/10000 (96.38%)\n",
      "          Accuracy Font : 6676/10000 (66.76%)\n",
      "Train Epoch: 5 [0/60000 (0%)]\tLoss Char: 0.186448 Loss Font: 0.617107\n",
      "Train Epoch: 5 [19200/60000 (32%)]\tLoss Char: 0.022115 Loss Font: 0.568461\n",
      "Train Epoch: 5 [38400/60000 (64%)]\tLoss Char: 0.056943 Loss Font: 0.577775\n",
      "Train Epoch: 5 [57600/60000 (96%)]\tLoss Char: 0.023434 Loss Font: 0.592557\n",
      "\n",
      "Test set: Accuracy Char : 9771/10000 (97.71%)\n",
      "          Accuracy Font : 6865/10000 (68.65%)\n",
      "Train Epoch: 6 [0/60000 (0%)]\tLoss Char: 0.118742 Loss Font: 0.564632\n",
      "Train Epoch: 6 [19200/60000 (32%)]\tLoss Char: 0.173585 Loss Font: 0.509099\n",
      "Train Epoch: 6 [38400/60000 (64%)]\tLoss Char: 0.043675 Loss Font: 0.527773\n",
      "Train Epoch: 6 [57600/60000 (96%)]\tLoss Char: 0.014529 Loss Font: 0.509183\n",
      "\n",
      "Test set: Accuracy Char : 9806/10000 (98.06%)\n",
      "          Accuracy Font : 7303/10000 (73.03%)\n",
      "Train Epoch: 7 [0/60000 (0%)]\tLoss Char: 0.096286 Loss Font: 0.529215\n",
      "Train Epoch: 7 [19200/60000 (32%)]\tLoss Char: 0.018571 Loss Font: 0.521356\n",
      "Train Epoch: 7 [38400/60000 (64%)]\tLoss Char: 0.022626 Loss Font: 0.518730\n",
      "Train Epoch: 7 [57600/60000 (96%)]\tLoss Char: 0.002870 Loss Font: 0.582464\n",
      "\n",
      "Test set: Accuracy Char : 9803/10000 (98.03%)\n",
      "          Accuracy Font : 7224/10000 (72.24%)\n",
      "Train Epoch: 8 [0/60000 (0%)]\tLoss Char: 0.060816 Loss Font: 0.540703\n",
      "Train Epoch: 8 [19200/60000 (32%)]\tLoss Char: 0.027204 Loss Font: 0.426318\n",
      "Train Epoch: 8 [38400/60000 (64%)]\tLoss Char: 0.041466 Loss Font: 0.509606\n",
      "Train Epoch: 8 [57600/60000 (96%)]\tLoss Char: 0.075961 Loss Font: 0.550028\n",
      "\n",
      "Test set: Accuracy Char : 9795/10000 (97.95%)\n",
      "          Accuracy Font : 7407/10000 (74.07%)\n",
      "Train Epoch: 9 [0/60000 (0%)]\tLoss Char: 0.021951 Loss Font: 0.479410\n",
      "Train Epoch: 9 [19200/60000 (32%)]\tLoss Char: 0.019596 Loss Font: 0.519340\n",
      "Train Epoch: 9 [38400/60000 (64%)]\tLoss Char: 0.163980 Loss Font: 0.479149\n",
      "Train Epoch: 9 [57600/60000 (96%)]\tLoss Char: 0.030453 Loss Font: 0.413288\n",
      "\n",
      "Test set: Accuracy Char : 9843/10000 (98.43%)\n",
      "          Accuracy Font : 7446/10000 (74.46%)\n",
      "Train Epoch: 10 [0/60000 (0%)]\tLoss Char: 0.040738 Loss Font: 0.475680\n",
      "Train Epoch: 10 [19200/60000 (32%)]\tLoss Char: 0.006892 Loss Font: 0.572388\n",
      "Train Epoch: 10 [38400/60000 (64%)]\tLoss Char: 0.021808 Loss Font: 0.537488\n",
      "Train Epoch: 10 [57600/60000 (96%)]\tLoss Char: 0.049072 Loss Font: 0.566564\n",
      "\n",
      "Test set: Accuracy Char : 9854/10000 (98.54%)\n",
      "          Accuracy Font : 7500/10000 (75.00%)\n",
      "\n",
      " ----- 6\n",
      "Training set 60000 items\n",
      "Testing set  10000 items\n",
      "Train Epoch: 1 [0/60000 (0%)]\tLoss Char: 2.312324 Loss Font: 0.691528\n",
      "Train Epoch: 1 [19200/60000 (32%)]\tLoss Char: 1.813446 Loss Font: 0.699115\n",
      "Train Epoch: 1 [38400/60000 (64%)]\tLoss Char: 0.724266 Loss Font: 0.676862\n",
      "Train Epoch: 1 [57600/60000 (96%)]\tLoss Char: 0.516053 Loss Font: 0.678527\n",
      "\n",
      "Test set: Accuracy Char : 8210/10000 (82.10%)\n",
      "          Accuracy Font : 5879/10000 (58.79%)\n",
      "Train Epoch: 2 [0/60000 (0%)]\tLoss Char: 0.535614 Loss Font: 0.680805\n",
      "Train Epoch: 2 [19200/60000 (32%)]\tLoss Char: 0.466656 Loss Font: 0.704552\n",
      "Train Epoch: 2 [38400/60000 (64%)]\tLoss Char: 0.327307 Loss Font: 0.636413\n",
      "Train Epoch: 2 [57600/60000 (96%)]\tLoss Char: 0.200108 Loss Font: 0.620807\n",
      "\n",
      "Test set: Accuracy Char : 9335/10000 (93.35%)\n",
      "          Accuracy Font : 6810/10000 (68.10%)\n",
      "Train Epoch: 3 [0/60000 (0%)]\tLoss Char: 0.256928 Loss Font: 0.633857\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Train Epoch: 3 [19200/60000 (32%)]\tLoss Char: 0.205491 Loss Font: 0.593643\n",
      "Train Epoch: 3 [38400/60000 (64%)]\tLoss Char: 0.058040 Loss Font: 0.596879\n",
      "Train Epoch: 3 [57600/60000 (96%)]\tLoss Char: 0.137083 Loss Font: 0.551917\n",
      "\n",
      "Test set: Accuracy Char : 9462/10000 (94.62%)\n",
      "          Accuracy Font : 6705/10000 (67.05%)\n",
      "Train Epoch: 4 [0/60000 (0%)]\tLoss Char: 0.031928 Loss Font: 0.638296\n",
      "Train Epoch: 4 [19200/60000 (32%)]\tLoss Char: 0.040590 Loss Font: 0.671783\n",
      "Train Epoch: 4 [38400/60000 (64%)]\tLoss Char: 0.100789 Loss Font: 0.584382\n",
      "Train Epoch: 4 [57600/60000 (96%)]\tLoss Char: 0.189373 Loss Font: 0.569883\n",
      "\n",
      "Test set: Accuracy Char : 9508/10000 (95.08%)\n",
      "          Accuracy Font : 6934/10000 (69.34%)\n",
      "Train Epoch: 5 [0/60000 (0%)]\tLoss Char: 0.245857 Loss Font: 0.662132\n",
      "Train Epoch: 5 [19200/60000 (32%)]\tLoss Char: 0.057571 Loss Font: 0.588520\n",
      "Train Epoch: 5 [38400/60000 (64%)]\tLoss Char: 0.133787 Loss Font: 0.517221\n",
      "Train Epoch: 5 [57600/60000 (96%)]\tLoss Char: 0.080670 Loss Font: 0.584703\n",
      "\n",
      "Test set: Accuracy Char : 9691/10000 (96.91%)\n",
      "          Accuracy Font : 7269/10000 (72.69%)\n",
      "Train Epoch: 6 [0/60000 (0%)]\tLoss Char: 0.283800 Loss Font: 0.586363\n",
      "Train Epoch: 6 [19200/60000 (32%)]\tLoss Char: 0.276308 Loss Font: 0.600927\n",
      "Train Epoch: 6 [38400/60000 (64%)]\tLoss Char: 0.149192 Loss Font: 0.437796\n",
      "Train Epoch: 6 [57600/60000 (96%)]\tLoss Char: 0.018749 Loss Font: 0.542219\n",
      "\n",
      "Test set: Accuracy Char : 9713/10000 (97.13%)\n",
      "          Accuracy Font : 7266/10000 (72.66%)\n",
      "Train Epoch: 7 [0/60000 (0%)]\tLoss Char: 0.034303 Loss Font: 0.593055\n",
      "Train Epoch: 7 [19200/60000 (32%)]\tLoss Char: 0.070396 Loss Font: 0.497124\n",
      "Train Epoch: 7 [38400/60000 (64%)]\tLoss Char: 0.021101 Loss Font: 0.551450\n",
      "Train Epoch: 7 [57600/60000 (96%)]\tLoss Char: 0.015396 Loss Font: 0.565255\n",
      "\n",
      "Test set: Accuracy Char : 9726/10000 (97.26%)\n",
      "          Accuracy Font : 7396/10000 (73.96%)\n",
      "Train Epoch: 8 [0/60000 (0%)]\tLoss Char: 0.065624 Loss Font: 0.537696\n",
      "Train Epoch: 8 [19200/60000 (32%)]\tLoss Char: 0.003593 Loss Font: 0.397728\n",
      "Train Epoch: 8 [38400/60000 (64%)]\tLoss Char: 0.155475 Loss Font: 0.529028\n",
      "Train Epoch: 8 [57600/60000 (96%)]\tLoss Char: 0.039767 Loss Font: 0.476216\n",
      "\n",
      "Test set: Accuracy Char : 9786/10000 (97.86%)\n",
      "          Accuracy Font : 7864/10000 (78.64%)\n",
      "Train Epoch: 9 [0/60000 (0%)]\tLoss Char: 0.005188 Loss Font: 0.471207\n",
      "Train Epoch: 9 [19200/60000 (32%)]\tLoss Char: 0.018014 Loss Font: 0.431884\n",
      "Train Epoch: 9 [38400/60000 (64%)]\tLoss Char: 0.089665 Loss Font: 0.392964\n",
      "Train Epoch: 9 [57600/60000 (96%)]\tLoss Char: 0.016886 Loss Font: 0.411115\n",
      "\n",
      "Test set: Accuracy Char : 9775/10000 (97.75%)\n",
      "          Accuracy Font : 7469/10000 (74.69%)\n",
      "Train Epoch: 10 [0/60000 (0%)]\tLoss Char: 0.006034 Loss Font: 0.553617\n",
      "Train Epoch: 10 [19200/60000 (32%)]\tLoss Char: 0.003038 Loss Font: 0.567290\n",
      "Train Epoch: 10 [38400/60000 (64%)]\tLoss Char: 0.012728 Loss Font: 0.668286\n",
      "Train Epoch: 10 [57600/60000 (96%)]\tLoss Char: 0.014213 Loss Font: 0.498052\n",
      "\n",
      "Test set: Accuracy Char : 9788/10000 (97.88%)\n",
      "          Accuracy Font : 7731/10000 (77.31%)\n",
      "\n",
      " ----- 5\n",
      "Training set 60000 items\n",
      "Testing set  10000 items\n",
      "Train Epoch: 1 [0/60000 (0%)]\tLoss Char: 2.286050 Loss Font: 0.694513\n",
      "Train Epoch: 1 [19200/60000 (32%)]\tLoss Char: 1.695562 Loss Font: 0.777637\n",
      "Train Epoch: 1 [38400/60000 (64%)]\tLoss Char: 0.937366 Loss Font: 0.744189\n",
      "Train Epoch: 1 [57600/60000 (96%)]\tLoss Char: 0.534895 Loss Font: 0.825282\n",
      "\n",
      "Test set: Accuracy Char : 8596/10000 (85.96%)\n",
      "          Accuracy Font : 5448/10000 (54.48%)\n",
      "Train Epoch: 2 [0/60000 (0%)]\tLoss Char: 0.348548 Loss Font: 0.669371\n",
      "Train Epoch: 2 [19200/60000 (32%)]\tLoss Char: 0.373401 Loss Font: 0.631348\n",
      "Train Epoch: 2 [38400/60000 (64%)]\tLoss Char: 0.210047 Loss Font: 0.653245\n",
      "Train Epoch: 2 [57600/60000 (96%)]\tLoss Char: 0.242391 Loss Font: 0.608854\n",
      "\n",
      "Test set: Accuracy Char : 9330/10000 (93.30%)\n",
      "          Accuracy Font : 6552/10000 (65.52%)\n",
      "Train Epoch: 3 [0/60000 (0%)]\tLoss Char: 0.192798 Loss Font: 0.594408\n",
      "Train Epoch: 3 [19200/60000 (32%)]\tLoss Char: 0.122011 Loss Font: 0.561734\n",
      "Train Epoch: 3 [38400/60000 (64%)]\tLoss Char: 0.030435 Loss Font: 0.561621\n",
      "Train Epoch: 3 [57600/60000 (96%)]\tLoss Char: 0.031940 Loss Font: 0.529016\n",
      "\n",
      "Test set: Accuracy Char : 9596/10000 (95.96%)\n",
      "          Accuracy Font : 7196/10000 (71.96%)\n",
      "Train Epoch: 4 [0/60000 (0%)]\tLoss Char: 0.034951 Loss Font: 0.574760\n",
      "Train Epoch: 4 [19200/60000 (32%)]\tLoss Char: 0.119376 Loss Font: 0.588766\n",
      "Train Epoch: 4 [38400/60000 (64%)]\tLoss Char: 0.032128 Loss Font: 0.510428\n",
      "Train Epoch: 4 [57600/60000 (96%)]\tLoss Char: 0.089725 Loss Font: 0.571496\n",
      "\n",
      "Test set: Accuracy Char : 9648/10000 (96.48%)\n",
      "          Accuracy Font : 7276/10000 (72.76%)\n",
      "Train Epoch: 5 [0/60000 (0%)]\tLoss Char: 0.121648 Loss Font: 0.640954\n",
      "Train Epoch: 5 [19200/60000 (32%)]\tLoss Char: 0.022043 Loss Font: 0.578001\n",
      "Train Epoch: 5 [38400/60000 (64%)]\tLoss Char: 0.072894 Loss Font: 0.548032\n",
      "Train Epoch: 5 [57600/60000 (96%)]\tLoss Char: 0.143091 Loss Font: 0.588870\n",
      "\n",
      "Test set: Accuracy Char : 9735/10000 (97.35%)\n",
      "          Accuracy Font : 7286/10000 (72.86%)\n",
      "Train Epoch: 6 [0/60000 (0%)]\tLoss Char: 0.235980 Loss Font: 0.478657\n",
      "Train Epoch: 6 [19200/60000 (32%)]\tLoss Char: 0.211216 Loss Font: 0.469452\n",
      "Train Epoch: 6 [38400/60000 (64%)]\tLoss Char: 0.078077 Loss Font: 0.517194\n",
      "Train Epoch: 6 [57600/60000 (96%)]\tLoss Char: 0.031942 Loss Font: 0.506022\n",
      "\n",
      "Test set: Accuracy Char : 9738/10000 (97.38%)\n",
      "          Accuracy Font : 7310/10000 (73.10%)\n",
      "Train Epoch: 7 [0/60000 (0%)]\tLoss Char: 0.076793 Loss Font: 0.572275\n",
      "Train Epoch: 7 [19200/60000 (32%)]\tLoss Char: 0.033624 Loss Font: 0.465049\n",
      "Train Epoch: 7 [38400/60000 (64%)]\tLoss Char: 0.045677 Loss Font: 0.509345\n",
      "Train Epoch: 7 [57600/60000 (96%)]\tLoss Char: 0.020323 Loss Font: 0.447559\n",
      "\n",
      "Test set: Accuracy Char : 9727/10000 (97.27%)\n",
      "          Accuracy Font : 7111/10000 (71.11%)\n",
      "Train Epoch: 8 [0/60000 (0%)]\tLoss Char: 0.080551 Loss Font: 0.522676\n",
      "Train Epoch: 8 [19200/60000 (32%)]\tLoss Char: 0.024090 Loss Font: 0.406656\n",
      "Train Epoch: 8 [38400/60000 (64%)]\tLoss Char: 0.108163 Loss Font: 0.518682\n",
      "Train Epoch: 8 [57600/60000 (96%)]\tLoss Char: 0.057944 Loss Font: 0.568278\n",
      "\n",
      "Test set: Accuracy Char : 9766/10000 (97.66%)\n",
      "          Accuracy Font : 7165/10000 (71.65%)\n",
      "Train Epoch: 9 [0/60000 (0%)]\tLoss Char: 0.011923 Loss Font: 0.511761\n",
      "Train Epoch: 9 [19200/60000 (32%)]\tLoss Char: 0.028193 Loss Font: 0.445875\n",
      "Train Epoch: 9 [38400/60000 (64%)]\tLoss Char: 0.167158 Loss Font: 0.517643\n",
      "Train Epoch: 9 [57600/60000 (96%)]\tLoss Char: 0.038459 Loss Font: 0.546183\n",
      "\n",
      "Test set: Accuracy Char : 9801/10000 (98.01%)\n",
      "          Accuracy Font : 7230/10000 (72.30%)\n",
      "Train Epoch: 10 [0/60000 (0%)]\tLoss Char: 0.018628 Loss Font: 0.473040\n",
      "Train Epoch: 10 [19200/60000 (32%)]\tLoss Char: 0.018284 Loss Font: 0.580940\n",
      "Train Epoch: 10 [38400/60000 (64%)]\tLoss Char: 0.007863 Loss Font: 0.530013\n",
      "Train Epoch: 10 [57600/60000 (96%)]\tLoss Char: 0.062784 Loss Font: 0.485255\n",
      "\n",
      "Test set: Accuracy Char : 9771/10000 (97.71%)\n",
      "          Accuracy Font : 7295/10000 (72.95%)\n",
      "\n",
      " ----- 4\n",
      "Training set 60000 items\n",
      "Testing set  10000 items\n",
      "Train Epoch: 1 [0/60000 (0%)]\tLoss Char: 2.279992 Loss Font: 0.683670\n",
      "Train Epoch: 1 [19200/60000 (32%)]\tLoss Char: 2.040343 Loss Font: 0.708205\n",
      "Train Epoch: 1 [38400/60000 (64%)]\tLoss Char: 1.220432 Loss Font: 0.678293\n",
      "Train Epoch: 1 [57600/60000 (96%)]\tLoss Char: 0.615856 Loss Font: 0.827249\n",
      "\n",
      "Test set: Accuracy Char : 7364/10000 (73.64%)\n",
      "          Accuracy Font : 5755/10000 (57.55%)\n",
      "Train Epoch: 2 [0/60000 (0%)]\tLoss Char: 0.679765 Loss Font: 0.717024\n",
      "Train Epoch: 2 [19200/60000 (32%)]\tLoss Char: 0.447954 Loss Font: 0.680574\n",
      "Train Epoch: 2 [38400/60000 (64%)]\tLoss Char: 0.422535 Loss Font: 0.845626\n",
      "Train Epoch: 2 [57600/60000 (96%)]\tLoss Char: 0.173719 Loss Font: 0.723149\n",
      "\n",
      "Test set: Accuracy Char : 9344/10000 (93.44%)\n",
      "          Accuracy Font : 6663/10000 (66.63%)\n",
      "Train Epoch: 3 [0/60000 (0%)]\tLoss Char: 0.246523 Loss Font: 0.543474\n",
      "Train Epoch: 3 [19200/60000 (32%)]\tLoss Char: 0.209401 Loss Font: 0.595871\n",
      "Train Epoch: 3 [38400/60000 (64%)]\tLoss Char: 0.074822 Loss Font: 0.553823\n",
      "Train Epoch: 3 [57600/60000 (96%)]\tLoss Char: 0.046936 Loss Font: 0.571325\n",
      "\n",
      "Test set: Accuracy Char : 9585/10000 (95.85%)\n",
      "          Accuracy Font : 6968/10000 (69.68%)\n",
      "Train Epoch: 4 [0/60000 (0%)]\tLoss Char: 0.154628 Loss Font: 0.607611\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Train Epoch: 4 [19200/60000 (32%)]\tLoss Char: 0.056116 Loss Font: 0.611965\n",
      "Train Epoch: 4 [38400/60000 (64%)]\tLoss Char: 0.059324 Loss Font: 0.588510\n",
      "Train Epoch: 4 [57600/60000 (96%)]\tLoss Char: 0.140013 Loss Font: 0.722780\n",
      "\n",
      "Test set: Accuracy Char : 9661/10000 (96.61%)\n",
      "          Accuracy Font : 7159/10000 (71.59%)\n",
      "Train Epoch: 5 [0/60000 (0%)]\tLoss Char: 0.103128 Loss Font: 0.532660\n",
      "Train Epoch: 5 [19200/60000 (32%)]\tLoss Char: 0.018894 Loss Font: 0.517555\n",
      "Train Epoch: 5 [38400/60000 (64%)]\tLoss Char: 0.040878 Loss Font: 0.526978\n",
      "Train Epoch: 5 [57600/60000 (96%)]\tLoss Char: 0.046790 Loss Font: 0.504615\n",
      "\n",
      "Test set: Accuracy Char : 9765/10000 (97.65%)\n",
      "          Accuracy Font : 7280/10000 (72.80%)\n",
      "Train Epoch: 6 [0/60000 (0%)]\tLoss Char: 0.221688 Loss Font: 0.690722\n",
      "Train Epoch: 6 [19200/60000 (32%)]\tLoss Char: 0.349356 Loss Font: 0.495458\n",
      "Train Epoch: 6 [38400/60000 (64%)]\tLoss Char: 0.121067 Loss Font: 0.564838\n",
      "Train Epoch: 6 [57600/60000 (96%)]\tLoss Char: 0.070899 Loss Font: 0.524759\n",
      "\n",
      "Test set: Accuracy Char : 9676/10000 (96.76%)\n",
      "          Accuracy Font : 7408/10000 (74.08%)\n",
      "Train Epoch: 7 [0/60000 (0%)]\tLoss Char: 0.070929 Loss Font: 0.611471\n",
      "Train Epoch: 7 [19200/60000 (32%)]\tLoss Char: 0.034996 Loss Font: 0.424944\n",
      "Train Epoch: 7 [38400/60000 (64%)]\tLoss Char: 0.007149 Loss Font: 0.572521\n",
      "Train Epoch: 7 [57600/60000 (96%)]\tLoss Char: 0.010857 Loss Font: 0.469834\n",
      "\n",
      "Test set: Accuracy Char : 9789/10000 (97.89%)\n",
      "          Accuracy Font : 7431/10000 (74.31%)\n",
      "Train Epoch: 8 [0/60000 (0%)]\tLoss Char: 0.129413 Loss Font: 0.512608\n",
      "Train Epoch: 8 [19200/60000 (32%)]\tLoss Char: 0.025252 Loss Font: 0.372567\n",
      "Train Epoch: 8 [38400/60000 (64%)]\tLoss Char: 0.045488 Loss Font: 0.472218\n",
      "Train Epoch: 8 [57600/60000 (96%)]\tLoss Char: 0.048650 Loss Font: 0.475020\n",
      "\n",
      "Test set: Accuracy Char : 9792/10000 (97.92%)\n",
      "          Accuracy Font : 7632/10000 (76.32%)\n",
      "Train Epoch: 9 [0/60000 (0%)]\tLoss Char: 0.041269 Loss Font: 0.433793\n",
      "Train Epoch: 9 [19200/60000 (32%)]\tLoss Char: 0.009741 Loss Font: 0.470447\n",
      "Train Epoch: 9 [38400/60000 (64%)]\tLoss Char: 0.228428 Loss Font: 0.456384\n",
      "Train Epoch: 9 [57600/60000 (96%)]\tLoss Char: 0.060197 Loss Font: 0.457864\n",
      "\n",
      "Test set: Accuracy Char : 9815/10000 (98.15%)\n",
      "          Accuracy Font : 7173/10000 (71.73%)\n",
      "Train Epoch: 10 [0/60000 (0%)]\tLoss Char: 0.030807 Loss Font: 0.497063\n",
      "Train Epoch: 10 [19200/60000 (32%)]\tLoss Char: 0.004432 Loss Font: 0.481536\n",
      "Train Epoch: 10 [38400/60000 (64%)]\tLoss Char: 0.025980 Loss Font: 0.486453\n",
      "Train Epoch: 10 [57600/60000 (96%)]\tLoss Char: 0.010275 Loss Font: 0.614267\n",
      "\n",
      "Test set: Accuracy Char : 9804/10000 (98.04%)\n",
      "          Accuracy Font : 7513/10000 (75.13%)\n",
      "\n",
      " ----- 3\n",
      "Training set 60000 items\n",
      "Testing set  10000 items\n",
      "Train Epoch: 1 [0/60000 (0%)]\tLoss Char: 2.339617 Loss Font: 0.702497\n",
      "Train Epoch: 1 [19200/60000 (32%)]\tLoss Char: 2.295047 Loss Font: 0.677164\n",
      "Train Epoch: 1 [38400/60000 (64%)]\tLoss Char: 1.573095 Loss Font: 0.703626\n",
      "Train Epoch: 1 [57600/60000 (96%)]\tLoss Char: 1.000675 Loss Font: 0.677664\n",
      "\n",
      "Test set: Accuracy Char : 5938/10000 (59.38%)\n",
      "          Accuracy Font : 5699/10000 (56.99%)\n",
      "Train Epoch: 2 [0/60000 (0%)]\tLoss Char: 0.993095 Loss Font: 0.679910\n",
      "Train Epoch: 2 [19200/60000 (32%)]\tLoss Char: 0.846656 Loss Font: 0.702021\n",
      "Train Epoch: 2 [38400/60000 (64%)]\tLoss Char: 0.582078 Loss Font: 0.766754\n",
      "Train Epoch: 2 [57600/60000 (96%)]\tLoss Char: 0.422679 Loss Font: 0.755562\n",
      "\n",
      "Test set: Accuracy Char : 8662/10000 (86.62%)\n",
      "          Accuracy Font : 5529/10000 (55.29%)\n",
      "Train Epoch: 3 [0/60000 (0%)]\tLoss Char: 0.376773 Loss Font: 0.674851\n",
      "Train Epoch: 3 [19200/60000 (32%)]\tLoss Char: 0.261040 Loss Font: 0.645065\n",
      "Train Epoch: 3 [38400/60000 (64%)]\tLoss Char: 0.176324 Loss Font: 0.660612\n",
      "Train Epoch: 3 [57600/60000 (96%)]\tLoss Char: 0.132209 Loss Font: 0.693755\n",
      "\n",
      "Test set: Accuracy Char : 9020/10000 (90.20%)\n",
      "          Accuracy Font : 5729/10000 (57.29%)\n",
      "Train Epoch: 4 [0/60000 (0%)]\tLoss Char: 0.233003 Loss Font: 0.679655\n",
      "Train Epoch: 4 [19200/60000 (32%)]\tLoss Char: 0.164984 Loss Font: 0.717523\n",
      "Train Epoch: 4 [38400/60000 (64%)]\tLoss Char: 0.189348 Loss Font: 0.684598\n",
      "Train Epoch: 4 [57600/60000 (96%)]\tLoss Char: 0.202476 Loss Font: 0.674929\n",
      "\n",
      "Test set: Accuracy Char : 9112/10000 (91.12%)\n",
      "          Accuracy Font : 5721/10000 (57.21%)\n",
      "Train Epoch: 5 [0/60000 (0%)]\tLoss Char: 0.259464 Loss Font: 0.660413\n",
      "Train Epoch: 5 [19200/60000 (32%)]\tLoss Char: 0.245434 Loss Font: 0.648582\n",
      "Train Epoch: 5 [38400/60000 (64%)]\tLoss Char: 0.278905 Loss Font: 0.664720\n",
      "Train Epoch: 5 [57600/60000 (96%)]\tLoss Char: 0.206778 Loss Font: 0.683885\n",
      "\n",
      "Test set: Accuracy Char : 9390/10000 (93.90%)\n",
      "          Accuracy Font : 6234/10000 (62.34%)\n",
      "Train Epoch: 6 [0/60000 (0%)]\tLoss Char: 0.316107 Loss Font: 0.694043\n",
      "Train Epoch: 6 [19200/60000 (32%)]\tLoss Char: 0.133016 Loss Font: 0.642320\n",
      "Train Epoch: 6 [38400/60000 (64%)]\tLoss Char: 0.189831 Loss Font: 0.682623\n",
      "Train Epoch: 6 [57600/60000 (96%)]\tLoss Char: 0.100112 Loss Font: 0.658139\n",
      "\n",
      "Test set: Accuracy Char : 9440/10000 (94.40%)\n",
      "          Accuracy Font : 6243/10000 (62.43%)\n",
      "Train Epoch: 7 [0/60000 (0%)]\tLoss Char: 0.098584 Loss Font: 0.667993\n",
      "Train Epoch: 7 [19200/60000 (32%)]\tLoss Char: 0.095444 Loss Font: 0.607986\n",
      "Train Epoch: 7 [38400/60000 (64%)]\tLoss Char: 0.094642 Loss Font: 0.612895\n",
      "Train Epoch: 7 [57600/60000 (96%)]\tLoss Char: 0.124188 Loss Font: 0.696658\n",
      "\n",
      "Test set: Accuracy Char : 9518/10000 (95.18%)\n",
      "          Accuracy Font : 6327/10000 (63.27%)\n",
      "Train Epoch: 8 [0/60000 (0%)]\tLoss Char: 0.191117 Loss Font: 0.656802\n",
      "Train Epoch: 8 [19200/60000 (32%)]\tLoss Char: 0.045230 Loss Font: 0.614363\n",
      "Train Epoch: 8 [38400/60000 (64%)]\tLoss Char: 0.281908 Loss Font: 0.665169\n",
      "Train Epoch: 8 [57600/60000 (96%)]\tLoss Char: 0.211639 Loss Font: 0.749412\n",
      "\n",
      "Test set: Accuracy Char : 9547/10000 (95.47%)\n",
      "          Accuracy Font : 6352/10000 (63.52%)\n",
      "Train Epoch: 9 [0/60000 (0%)]\tLoss Char: 0.049075 Loss Font: 0.639075\n",
      "Train Epoch: 9 [19200/60000 (32%)]\tLoss Char: 0.051223 Loss Font: 0.549838\n",
      "Train Epoch: 9 [38400/60000 (64%)]\tLoss Char: 0.270124 Loss Font: 0.612874\n",
      "Train Epoch: 9 [57600/60000 (96%)]\tLoss Char: 0.091266 Loss Font: 0.615921\n",
      "\n",
      "Test set: Accuracy Char : 9524/10000 (95.24%)\n",
      "          Accuracy Font : 5811/10000 (58.11%)\n",
      "Train Epoch: 10 [0/60000 (0%)]\tLoss Char: 0.035597 Loss Font: 0.690383\n",
      "Train Epoch: 10 [19200/60000 (32%)]\tLoss Char: 0.086896 Loss Font: 0.655434\n",
      "Train Epoch: 10 [38400/60000 (64%)]\tLoss Char: 0.084148 Loss Font: 0.683775\n",
      "Train Epoch: 10 [57600/60000 (96%)]\tLoss Char: 0.127398 Loss Font: 0.613134\n",
      "\n",
      "Test set: Accuracy Char : 9606/10000 (96.06%)\n",
      "          Accuracy Font : 6479/10000 (64.79%)\n",
      "\n",
      " ----- 2\n",
      "Training set 60000 items\n",
      "Testing set  10000 items\n",
      "Train Epoch: 1 [0/60000 (0%)]\tLoss Char: 2.349372 Loss Font: 0.707449\n",
      "Train Epoch: 1 [19200/60000 (32%)]\tLoss Char: 2.016835 Loss Font: 0.699316\n",
      "Train Epoch: 1 [38400/60000 (64%)]\tLoss Char: 1.423170 Loss Font: 0.688596\n",
      "Train Epoch: 1 [57600/60000 (96%)]\tLoss Char: 1.090201 Loss Font: 0.661782\n",
      "\n",
      "Test set: Accuracy Char : 5450/10000 (54.50%)\n",
      "          Accuracy Font : 4966/10000 (49.66%)\n",
      "Train Epoch: 2 [0/60000 (0%)]\tLoss Char: 1.168280 Loss Font: 0.685449\n",
      "Train Epoch: 2 [19200/60000 (32%)]\tLoss Char: 1.050771 Loss Font: 0.700483\n",
      "Train Epoch: 2 [38400/60000 (64%)]\tLoss Char: 0.953397 Loss Font: 0.679983\n",
      "Train Epoch: 2 [57600/60000 (96%)]\tLoss Char: 0.757731 Loss Font: 0.689432\n",
      "\n",
      "Test set: Accuracy Char : 6957/10000 (69.57%)\n",
      "          Accuracy Font : 5512/10000 (55.12%)\n",
      "Train Epoch: 3 [0/60000 (0%)]\tLoss Char: 0.746480 Loss Font: 0.716046\n",
      "Train Epoch: 3 [19200/60000 (32%)]\tLoss Char: 0.687709 Loss Font: 0.674919\n",
      "Train Epoch: 3 [38400/60000 (64%)]\tLoss Char: 0.704423 Loss Font: 0.664073\n",
      "Train Epoch: 3 [57600/60000 (96%)]\tLoss Char: 0.475612 Loss Font: 0.636456\n",
      "\n",
      "Test set: Accuracy Char : 7479/10000 (74.79%)\n",
      "          Accuracy Font : 5417/10000 (54.17%)\n",
      "Train Epoch: 4 [0/60000 (0%)]\tLoss Char: 0.491933 Loss Font: 0.715679\n",
      "Train Epoch: 4 [19200/60000 (32%)]\tLoss Char: 0.533327 Loss Font: 0.676821\n",
      "Train Epoch: 4 [38400/60000 (64%)]\tLoss Char: 0.529978 Loss Font: 0.705871\n",
      "Train Epoch: 4 [57600/60000 (96%)]\tLoss Char: 0.613263 Loss Font: 0.660105\n",
      "\n",
      "Test set: Accuracy Char : 8052/10000 (80.52%)\n",
      "          Accuracy Font : 5624/10000 (56.24%)\n",
      "Train Epoch: 5 [0/60000 (0%)]\tLoss Char: 0.501588 Loss Font: 0.682078\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Train Epoch: 5 [19200/60000 (32%)]\tLoss Char: 0.551763 Loss Font: 0.707495\n",
      "Train Epoch: 5 [38400/60000 (64%)]\tLoss Char: 0.502762 Loss Font: 0.693962\n",
      "Train Epoch: 5 [57600/60000 (96%)]\tLoss Char: 0.638529 Loss Font: 0.662704\n",
      "\n",
      "Test set: Accuracy Char : 8108/10000 (81.08%)\n",
      "          Accuracy Font : 5709/10000 (57.09%)\n",
      "Train Epoch: 6 [0/60000 (0%)]\tLoss Char: 0.480537 Loss Font: 0.672220\n",
      "Train Epoch: 6 [19200/60000 (32%)]\tLoss Char: 1.115065 Loss Font: 0.684632\n",
      "Train Epoch: 6 [38400/60000 (64%)]\tLoss Char: 0.713154 Loss Font: 0.672375\n",
      "Train Epoch: 6 [57600/60000 (96%)]\tLoss Char: 0.378615 Loss Font: 0.642764\n",
      "\n",
      "Test set: Accuracy Char : 8234/10000 (82.34%)\n",
      "          Accuracy Font : 5776/10000 (57.76%)\n",
      "Train Epoch: 7 [0/60000 (0%)]\tLoss Char: 0.360710 Loss Font: 0.662114\n",
      "Train Epoch: 7 [19200/60000 (32%)]\tLoss Char: 0.422290 Loss Font: 0.687145\n",
      "Train Epoch: 7 [38400/60000 (64%)]\tLoss Char: 0.458750 Loss Font: 0.654657\n",
      "Train Epoch: 7 [57600/60000 (96%)]\tLoss Char: 0.300948 Loss Font: 0.661674\n",
      "\n",
      "Test set: Accuracy Char : 8372/10000 (83.72%)\n",
      "          Accuracy Font : 5630/10000 (56.30%)\n",
      "Train Epoch: 8 [0/60000 (0%)]\tLoss Char: 0.593508 Loss Font: 0.658780\n",
      "Train Epoch: 8 [19200/60000 (32%)]\tLoss Char: 0.293811 Loss Font: 0.662549\n",
      "Train Epoch: 8 [38400/60000 (64%)]\tLoss Char: 0.403689 Loss Font: 0.649237\n",
      "Train Epoch: 8 [57600/60000 (96%)]\tLoss Char: 0.322006 Loss Font: 0.647830\n",
      "\n",
      "Test set: Accuracy Char : 8310/10000 (83.10%)\n",
      "          Accuracy Font : 5739/10000 (57.39%)\n",
      "Train Epoch: 9 [0/60000 (0%)]\tLoss Char: 0.491834 Loss Font: 0.647836\n",
      "Train Epoch: 9 [19200/60000 (32%)]\tLoss Char: 0.336199 Loss Font: 0.661178\n",
      "Train Epoch: 9 [38400/60000 (64%)]\tLoss Char: 0.358010 Loss Font: 0.648316\n",
      "Train Epoch: 9 [57600/60000 (96%)]\tLoss Char: 0.445364 Loss Font: 0.670564\n",
      "\n",
      "Test set: Accuracy Char : 8490/10000 (84.90%)\n",
      "          Accuracy Font : 5974/10000 (59.74%)\n",
      "Train Epoch: 10 [0/60000 (0%)]\tLoss Char: 0.255030 Loss Font: 0.684257\n",
      "Train Epoch: 10 [19200/60000 (32%)]\tLoss Char: 0.300202 Loss Font: 0.662815\n",
      "Train Epoch: 10 [38400/60000 (64%)]\tLoss Char: 0.331601 Loss Font: 0.652033\n",
      "Train Epoch: 10 [57600/60000 (96%)]\tLoss Char: 0.318252 Loss Font: 0.612839\n",
      "\n",
      "Test set: Accuracy Char : 8485/10000 (84.85%)\n",
      "          Accuracy Font : 5880/10000 (58.80%)\n",
      "\n",
      " ----- 1\n",
      "Training set 60000 items\n",
      "Testing set  10000 items\n",
      "Train Epoch: 1 [0/60000 (0%)]\tLoss Char: 2.279107 Loss Font: 0.689968\n",
      "Train Epoch: 1 [19200/60000 (32%)]\tLoss Char: 2.298383 Loss Font: 0.699354\n",
      "Train Epoch: 1 [38400/60000 (64%)]\tLoss Char: 2.082085 Loss Font: 0.690724\n",
      "Train Epoch: 1 [57600/60000 (96%)]\tLoss Char: 1.999364 Loss Font: 0.692924\n",
      "\n",
      "Test set: Accuracy Char : 2692/10000 (26.92%)\n",
      "          Accuracy Font : 5062/10000 (50.62%)\n",
      "Train Epoch: 2 [0/60000 (0%)]\tLoss Char: 1.848481 Loss Font: 0.693429\n",
      "Train Epoch: 2 [19200/60000 (32%)]\tLoss Char: 1.752253 Loss Font: 0.704825\n",
      "Train Epoch: 2 [38400/60000 (64%)]\tLoss Char: 1.661406 Loss Font: 0.691817\n",
      "Train Epoch: 2 [57600/60000 (96%)]\tLoss Char: 1.510804 Loss Font: 0.695650\n",
      "\n",
      "Test set: Accuracy Char : 3942/10000 (39.42%)\n",
      "          Accuracy Font : 5068/10000 (50.68%)\n",
      "Train Epoch: 3 [0/60000 (0%)]\tLoss Char: 1.451113 Loss Font: 0.681731\n",
      "Train Epoch: 3 [19200/60000 (32%)]\tLoss Char: 1.524296 Loss Font: 0.690305\n",
      "Train Epoch: 3 [38400/60000 (64%)]\tLoss Char: 1.422434 Loss Font: 0.699410\n",
      "Train Epoch: 3 [57600/60000 (96%)]\tLoss Char: 1.323521 Loss Font: 0.724214\n",
      "\n",
      "Test set: Accuracy Char : 3595/10000 (35.95%)\n",
      "          Accuracy Font : 5201/10000 (52.01%)\n",
      "Train Epoch: 4 [0/60000 (0%)]\tLoss Char: 1.390806 Loss Font: 0.693331\n",
      "Train Epoch: 4 [19200/60000 (32%)]\tLoss Char: 1.495805 Loss Font: 0.695917\n",
      "Train Epoch: 4 [38400/60000 (64%)]\tLoss Char: 1.171377 Loss Font: 0.714248\n",
      "Train Epoch: 4 [57600/60000 (96%)]\tLoss Char: 1.552839 Loss Font: 0.693964\n",
      "\n",
      "Test set: Accuracy Char : 4101/10000 (41.01%)\n",
      "          Accuracy Font : 5003/10000 (50.03%)\n",
      "Train Epoch: 5 [0/60000 (0%)]\tLoss Char: 1.473134 Loss Font: 0.695776\n",
      "Train Epoch: 5 [19200/60000 (32%)]\tLoss Char: 1.236209 Loss Font: 0.679366\n",
      "Train Epoch: 5 [38400/60000 (64%)]\tLoss Char: 1.321528 Loss Font: 0.680037\n",
      "Train Epoch: 5 [57600/60000 (96%)]\tLoss Char: 1.353726 Loss Font: 0.691699\n",
      "\n",
      "Test set: Accuracy Char : 4225/10000 (42.25%)\n",
      "          Accuracy Font : 5187/10000 (51.87%)\n",
      "Train Epoch: 6 [0/60000 (0%)]\tLoss Char: 1.453663 Loss Font: 0.721466\n",
      "Train Epoch: 6 [19200/60000 (32%)]\tLoss Char: 1.389663 Loss Font: 0.700123\n",
      "Train Epoch: 6 [38400/60000 (64%)]\tLoss Char: 1.273540 Loss Font: 0.711972\n",
      "Train Epoch: 6 [57600/60000 (96%)]\tLoss Char: 1.156831 Loss Font: 0.679096\n",
      "\n",
      "Test set: Accuracy Char : 3959/10000 (39.59%)\n",
      "          Accuracy Font : 4995/10000 (49.95%)\n",
      "Train Epoch: 7 [0/60000 (0%)]\tLoss Char: 1.439226 Loss Font: 0.709578\n",
      "Train Epoch: 7 [19200/60000 (32%)]\tLoss Char: 1.327433 Loss Font: 0.694668\n",
      "Train Epoch: 7 [38400/60000 (64%)]\tLoss Char: 1.138806 Loss Font: 0.667662\n",
      "Train Epoch: 7 [57600/60000 (96%)]\tLoss Char: 1.220692 Loss Font: 0.830241\n",
      "\n",
      "Test set: Accuracy Char : 4553/10000 (45.53%)\n",
      "          Accuracy Font : 5002/10000 (50.02%)\n",
      "Train Epoch: 8 [0/60000 (0%)]\tLoss Char: 1.429413 Loss Font: 0.691399\n",
      "Train Epoch: 8 [19200/60000 (32%)]\tLoss Char: 1.232744 Loss Font: 0.705403\n",
      "Train Epoch: 8 [38400/60000 (64%)]\tLoss Char: 1.352300 Loss Font: 0.718155\n",
      "Train Epoch: 8 [57600/60000 (96%)]\tLoss Char: 1.151601 Loss Font: 0.688193\n",
      "\n",
      "Test set: Accuracy Char : 4406/10000 (44.06%)\n",
      "          Accuracy Font : 5040/10000 (50.40%)\n",
      "Train Epoch: 9 [0/60000 (0%)]\tLoss Char: 1.218843 Loss Font: 0.690396\n",
      "Train Epoch: 9 [19200/60000 (32%)]\tLoss Char: 1.237016 Loss Font: 0.694271\n",
      "Train Epoch: 9 [38400/60000 (64%)]\tLoss Char: 1.106397 Loss Font: 0.709589\n",
      "Train Epoch: 9 [57600/60000 (96%)]\tLoss Char: 1.237300 Loss Font: 0.695770\n",
      "\n",
      "Test set: Accuracy Char : 4750/10000 (47.50%)\n",
      "          Accuracy Font : 5188/10000 (51.88%)\n",
      "Train Epoch: 10 [0/60000 (0%)]\tLoss Char: 1.140436 Loss Font: 0.705609\n",
      "Train Epoch: 10 [19200/60000 (32%)]\tLoss Char: 1.300877 Loss Font: 0.745681\n",
      "Train Epoch: 10 [38400/60000 (64%)]\tLoss Char: 1.258707 Loss Font: 0.724920\n",
      "Train Epoch: 10 [57600/60000 (96%)]\tLoss Char: 1.108032 Loss Font: 0.725843\n",
      "\n",
      "Test set: Accuracy Char : 4808/10000 (48.08%)\n",
      "          Accuracy Font : 5029/10000 (50.29%)\n"
     ]
    }
   ],
   "source": [
    "for PRIVATE_OUTPUT_SIZE in [10, 9, 8, 7, 6, 5, 4, 3, 2, 1]:\n",
    "    print('\\n -----', PRIVATE_OUTPUT_SIZE)\n",
    "    model = CollateralNet()\n",
    "    test_perfs_char_perturbate, test_perfs_font_perturbate = dual_learning_phase(model)\n",
    "    results[f\"Main task with output size {PRIVATE_OUTPUT_SIZE}\"] = test_perfs_char_perturbate\n",
    "    results[f\"Collateral task with output size {PRIVATE_OUTPUT_SIZE}\"] = test_perfs_font_perturbate"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 720x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "show_results(results, title=\"Main and Collateral accuracies depending of the private output size\")\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Let's zoom on the main accuracy"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 49,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 720x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "main_results = {k:v for k, v in results.items() if 'Main' in k}\n",
    "show_results(main_results, title=\"Main accuracy depending of the private output size\", ymin=87)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "And the collateral one, even if it is not very readable"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 50,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 720x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "collateral_results = {k:v for k, v in results.items() if 'Collateral' in k}\n",
    "show_results(collateral_results, title=\"Collateral accuracy depending of the private output size\", ymax=86)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "import matplotlib.pyplot as plt"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 51,
   "metadata": {
    "scrolled": true
   },
   "outputs": [],
   "source": [
    "def draw_pareto(main_results, collateral_results):\n",
    "    output_sizes = sorted([int(title.split(' ')[-1]) for title in main_results.keys()])\n",
    "    main_results = {int(title.split(' ')[-1]): res for title, res in main_results.items()}\n",
    "    collateral_results = {int(title.split(' ')[-1]): res for title, res in collateral_results.items()}\n",
    "    \n",
    "    y_m = []\n",
    "    y_c = []\n",
    "    for output_size in output_sizes:\n",
    "        y_m.append(\n",
    "            100 - np.mean(main_results[output_size][-4:])\n",
    "        )\n",
    "        y_c.append(\n",
    "            100 - np.mean(collateral_results[output_size][-4:])\n",
    "        )\n",
    "\n",
    "    plt.figure(figsize=(7, 7))\n",
    "    plt.plot(y_m, y_c, 'o-', label=\"Output size\")\n",
    "    for i, output_size in enumerate(output_sizes):\n",
    "        plt.annotate(str(output_size), (y_m[i] + 1, y_c[i] - 1))\n",
    "    plt.ylabel('Collateral Error')\n",
    "    plt.xlabel('Main Task Error')\n",
    "    #plt.ylim(,)\n",
    "    #plt.title(\"Trade-off between the main and collateral accuracies\")\n",
    "    plt.legend()\n",
    "    plt.savefig('Pareto_OutputSize_noSabotage.png')\n",
    "    plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 52,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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h5pbkqm2lrC4o5bPDNWpJioSZgk6kh5UermFVOy3J5h231ZIUCR8FnUg3tW5Jrt5Wyge71JIUiSYKOpEuqKlv5O2PvFmSbbQkZ+RkkD20v1qSIlFAQScSpPZakp8fm86MHLUkRaKVgk6kHc0tyeYdt9WSFIlNCjqRVtprSeaqJSkSsxR04ntHW5KlrN2xTy1JkTijoBPf6agl+UWvJXmeWpIicUNBJ75QU9/IOx+VtVwCsOeQWpIifqGgk7hV6q0l+XpBKet27Ke6vpE+KYl8fuwQ/vWyM7k4O4P0/mpJisQ7BZ3EjeaWZPNCye+3akl+IW9EYJbkmEH0TlZLUsRPFHQS0zpqSd51+ZnMyBnKuFPVkhTxMwWdxJzSihpWF5Syalspa7erJSkiHVPQSdRzzvHhHm+WZBstyUvGZXDeaYPVkhSRNinoJCq115L83Ai1JEXk5CjoJKyWbyxhycpCdpdXMzwtlQUzs5k9JRMItCTXeLMk1ZIUkZ6ioJOwWb6xhEXLNlNd3whASXk19/zpA1Zu/Yzd5dUtLcnhp/Rm7tkjmJGjlqSIdJ+CTsJmycrClpBrVtvQxMtbPtMsSREJGQWdhM3u8uo2jxuw/JvTwluMiPiGgk5CrrHJ8czfdmIGzp34+PC01PAXJSK+oaCTkNpQfIB7V2xlS8lhTh/Sl13l1dQ2NLU8npqcyIKZ2RGsUETiXUKkC5D4VHq4hjv/sIkbfvkOZUfq+Pm8Kbz+nYv4zxsmk5mWihG4Du6BOZNaZl229umnn3LxxRczfvx4JkyYwMMPPxz+v4SIxAVzbfWSokxeXp7Lz8+PdBkShLqGJp56+xN+tmoHdQ1N/POFY/jmxWfQJ+Xkmgd79uxhz549nHXWWVRUVHD22WezfPlyxo8fH6LKRSTWmNkG51xeZ89T61J6zJt/38d9f97Kx/sqmTEug3+fNZ6sIX279F7Dhg1j2LBhAPTv35+cnBxKSkoUdCJy0hR00m2fHqjiBy98yKsf7iVrcB+evPkcLh6X0WPvX1RUxMaNG5k6dWqPvaeI+IeCTrqsuq6RX/7lI/7fXz4iMcG4+4psbr1gTI/uzH3kyBFuuOEGHnroIQYMGNBj7ysi/qGgk5PmnOOVLZ/xwxcLKCmv5trPDWfRVeMYdkrPXiZQX1/PDTfcwPz585kzZ06PvreI+IeCTk7K9r0V3PfnrazbUca4U/vzh9vPY+ppg3v8c5xz3HrrreTk5HDnnXf2+PuLiH8o6CQoh2vqefj17Sx9u4g+KYl8/7oJfPncUSQlhuYKlXXr1vE///M/TJo0idzcXAB+/OMfc9VVV4Xk80QkfinopENNTY4/vbeL/3ylkLLKWr50zigWzMxmUN+UkH7uBRdcQCxc+iIi0U9BJ+36YFc5967Yysad5Zw1Ko0nbz6HSSNOiXRZIiInRUEnJyg7UsuSlYX8If9TBvftxX994XNcPyWThATtKCAisUdBJy0aGpt4+t1ifvra36mqa+S2C8bw7Rlj6d87OdKliYh0mYJOAHj34zLuW7GVbZ9V8PmxQ7j3mvGckdE/0mWJiHSbgs7n9hyq5kcvFvDCB3vITEvl0RvPZuaEodr4VETiRsiDzswSgXygxDk3y8zGAM8Ag4ENwFecc3WhrkOOVdvQyK/f+oRfrN5Bk3P8y6Vj+fpFp9M7uedWNRERiQbhGNHdARQAzes3/SfwoHPuGTN7FLgV+GUY6hDPqoK9fP+FDykuq+KKCafyvatzGDmoT6TLEhEJiZDuR2dmI4CrgV979w24BHjWe8pSYHYoa5CjPtlfyVefXM+tS/NJSjD+59ZzefQrZyvkRCSuhXpE9xBwN9A8q2EwUO6ca/Du7wJO3HUTMLPbgdsBRo0aFeIy41tlbQO/WLODx9/6hJSkBP7t6hxu+ocskkO0qomISDQJWdCZ2Syg1Dm3wcymn+zrnXOPAY9BYOPVHi7PF5xzrHh/Nw+8tI3PDtdww1kjuOfKbDL69450aSIiYRPKEd004FozuwroTeAc3cNAmpkleaO6EUBJCGvwrYI9h7l3xVbWf3KASZmn8Mj8szh79MBIlyUiEnYhCzrn3CJgEYA3orvLOTffzP4IzCUw8/Im4PlQ1eBH5VV1/PS1v/P0u8WckprMA3Mm8cW8kSRqVRMR8alIXEd3D/CMmf0Q2Ag8HoEa4k5jk+MPf/uUJSu3cai6nq+cN5o7L8vmlD5a1URE/C0sQeecewN4w7v9MXBuOD7XLzYUH+S+FVvZXHKIc8cM4v5rJ5AzTLtxi4iAVkaJaaUVNSx+eRvL3ivh1AG9+dm8KVwzeZhWNRERaUVBF4PqG5t4al0RD6/aTl1DE/9n+ul88+Iz6NtL/3OKiBxP/2WMMW9t38d9K7by0b5KLhmXwb/PGs+YIX0jXZaISNRS0MWITw9U8cMXP2Tl1r2MHtyHx2/KY0bO0EiXJSIS9RR0Ua6mvpFfvvERj/7lIxLMWDAzm1svGKPFl0VEgqSgi1LOOVZu/YwfvFBASXk1syYP47tX5TA8LTXSpYmIxBQFXRTaUVrBfSs+ZO2O/Yw7tT+//+fzOP/0wZEuS0QkJinookhFTT0Pv76dp94uok9KIvddM54bzxtNkhZfFhHpMgVdFGhqcizbWMLil7dRVlnLP+aNZMHMbAb36xXp0kREYp6CLsI27zrEvSu28N7OcnJHpvHEzXlMHpEW6bJEROKGgi5CDlTWsWTlNp7526cM7pvCkrmTueGsESRo8WURkR6loAuzhsYmfvvXnfzXq4VU1TVyy7Qx3HHpWAb01uLLIiKhoKALo79+XMa9K7ay7bMKpp0xmPuumcDYof07f6GIiHSZgi4M9hyq5scvbePP7+8mMy2VX84/iysmnqrFl0VEwkBBF0K1DY38+q1PeGTNDhqaHN+eMZZvXHQ6qSla1UREJFwUdCGyettevv/nDykqq+Ly8UP591njGTmoT6TLEhHxHQVdNy3fWMKSlYXsLq9meFoqN/9DFu98XMbqbaWclt6X39xyLheemR7pMkVEfEtB1w3LN5awaNlmqusbASgpr+ZHLxWQkmh896px3PwPY0hJ0qomIiKRpKDrhiUrC1tCrrVBfXtx+4WnR6AiERE5noYb3bC7vLrN43sP14S5EhERaY+Crhva2zJHW+mIiEQPBV03LJiZTdJxS3alJieyYGZ2hCoSEZHjKei6YfaUTM4ZM5AEAwMy01J5YM4kZk/JPOZ5NTU1nHvuuXzuc59jwoQJ3HvvvZEpWETEhzQZpZsSLYFJI9J4/pvT2n1Or169WL16Nf369aO+vp4LLriAK6+8kvPOOy+MlYqI+JNGdN1UVFbJ6E4uBDcz+vXrB0B9fT319fVa/ktEJEwUdN1Q19DE7vJqRg/ufMWTxsZGcnNzycjI4LLLLmPq1KlhqFBERBR03VBSXk2Tg1FBLO2VmJjIpk2b2LVrF+vXr2fLli1hqFBERBR03VBcVgnA6MF9g35NWloaF198Ma+88kqoyhIRkVYUdN2w80AVQKety3379lFeXg5AdXU1r732GuPGjQt5fSIiolmX3VJcVkXv5AQy+vfq8Hl79uzhpptuorGxkaamJr74xS8ya9asMFUpIuJvCrpuKC6rYvSgvp3OoJw8eTIbN24MU1UiItKaWpfdsPNAJaOCmHEpIiKRo6DroqYmx84DVZ1eQyciIpGloOui0opaauqbgrqGTkREIqfDoDOzRDP7SbiKiSXNlxaMOolLC0REJPw6DDrnXCNwQZhqiSnFzZcWqHUpIhLVgpl1udHMVgB/BCqbDzrnloWsqhiws6yKxAQjc6D2nhMRiWbBBF1voAy4pNUxB/g66IoPVDE8rTfJiTrNKSISzToNOufcV8NRSKzZWVbJ6EE6PyciEu06HY6Y2Qgze87MSr2fP5nZiHAUF82KD1RpxqWISAwIpu/2JLACGO79/Nk75luHquspr6pX0ImIxIBggi7dOfekc67B+3kKSA9xXVFtZ1lgxuUotS5FRKJeMEFXZmY3etfUJZrZjQQmp/hW8YHm7Xk0ohMRiXbBBN0twBeBz4A9wFyg0wkqZtbbzNab2ftmttXM7veOP2Vmn5jZJu8ntzt/gUgobhnRKehERKJdh7MuzSwRmOOcu7YL710LXOKcO2JmycBaM3vZe2yBc+7ZLrxnVCguq2RIv1707aXNH0REol0wK6PM68obu4Aj3t1k78d15b2iTXGZZlyKiMSKYFqX68zsF2b2eTM7q/knmDf3zultAkqB15xzf/Ue+pGZfWBmD5pZm7uWmtntZpZvZvn79u0L7m8TJtq1QEQkdgTTe2s+h/b9Vsccx66U0iZvRJhrZmnAc2Y2EVhE4HxfCvAYcM9x79382se8x8nLy4uakWBNfSOfHa7RPnQiIjGis3N0CcAvnXP/250Pcc6Vm9ka4ArnXPNuCLVm9iRwV3feO9x2HazCOcjSrgUiIjGhs3N0TcDdXXljM0v3RnKYWSpwGbDNzIZ5xwyYDWzpyvtHSsuMS43oRERiQjCty9fN7C7gDxy7e8GBTl43DFjqzdxMAP7XOfeCma02s3TAgE3A17tWemQ0B53O0YmIxIZggu4fvT+/2eqYA07r6EXOuQ+AKW0c7/TcXjTbeaCKfr2SGNQ3JdKliIhIEILZvWBMOAqJFcVllYwa1IdA51VERKJdu+fozOzuVre/cNxjPw5lUdFM19CJiMSWjiajfKnV7UXHPXZFCGqJeo1Njk8PVmkiiohIDOko6Kyd223d94U9h6qpb3TacFVEJIZ0FHSundtt3feF5u151LoUEYkdHU1G+ZyZHSYwekv1buPd7x3yyqJQ8QEFnYhIrGk36JxzieEsJBYUl1WRnGgMOyU10qWIiEiQglnUWTw7D1QycmAfEhN8eYpSRCQmKehOQnGZZlyKiMQaBV2QnHPsLNP2PCIisUZBF6SDVfVU1DYwSrsWiIjElHYno5hZBW1fRmAENhAfELKqolBRWWA9a43oRERiS0ezLvuHs5Bop2voRERiUzC7FwBgZhm0un7OObczJBVFqebteUZqRCciElM6PUdnZtea2XbgE+AvQBHwcojrijrFByo5dUBveifr8kIRkVgSzGSUHwDnAX/3tuyZAbwb0qqi0E7tWiAiEpOCCbp651wZkGBmCc65NUBeiOuKOsUHFHQiIrEomHN05WbWD3gT+K2ZlQKVoS0rulTVNbCvopbRurRARCTmBDOiuw6oAv4VeAX4CLgmlEVFm53eYs6jNBFFRCTmdDiiM7NE4AXn3MVAE7A0LFVFmWJdWiAiErM6HNE55xqBJjM7JUz1RKXilovF1boUEYk1wZyjOwJsNrPXaHVuzjn37ZBVFWWKy6o4JTWZU/okR7oUERE5ScEE3TLvx7d2asaliEjM6jTonHNLzSwVGOWcKwxDTVGnuKyKySN83b0VEYlZwayMcg2wicCMS8ws18xWhLqwaFHf2ERJeTVZurRARCQmBXN5wX3AuUA5gHNuE3BaCGuKKrvLq2lsctpwVUQkRgW7Msqh4441haKYaNRyaYGuoRMRiUnBTEbZamZfBhLNbCzwbeDt0JYVPYoPNF9Dp9aliEgsCmZE93+BCUAt8DvgEHBHKIuKJjvLKumVlEBG/16RLkVERLogmBHd1c657wHfaz5gZl8A/hiyqqJIcVkVowb1ISHBIl2KiIh0QTAjukVBHotLxdqeR0QkprU7ojOzK4GrgEwz+1mrhwYADaEuLBo459h5oIppZwyJdCkiItJFHbUudwP5wLXAhlbHKwjsZBD39lXUUl3fqBGdiEgMazfonHPvA++b2e+cc/VhrClqHJ1xqaATEYlVwUxGyTKzB4DxQO/mg865uL9o/Oj2PLq0QEQkVgUzGeVJ4JcEzstdDPwGeDqURUWLnWWVJBhkpqVGuhQREemiYIIu1Tm3CjDnXLFz7j7g6tCWFR2KD1QxPC2VlKRgviYREYlGwbQua80sAdhuZt8CSoB+oS0rOujSAhGR2BfMUOUOoA+Bpb/OBr4C3BTKoqLFzgNVjNKu4iIiMS2Y/ej+5t08Anw1tOVEj8M19RyorNOITkQkxnV0wfifAdfe4865a0NSUZTYqV0LREQLmxiWAAAaBElEQVTiQkcjup+ErYoo1HxpgfahExGJbR1dMP6X7ryxmfUG3gR6eZ/zrHPuXjMbAzwDDCaw4spXnHN13fmsUCg+UAnoGjoRkVjXUetyM223Lg1wzrnJnbx3LXCJc+6ImSUDa83sZeBO4EHn3DNm9ihwK4Hr9KLKzrIqhvRLoV+vYCamiohItOrov+KzuvPGzjlHYAILQLL344BLgC97x5cC9xGFQde8PY+IiMS2di8v8C4OL3bOFQM1wCTvp9o71ikzSzSzTUAp8BrwEVDunGve/WAXkNmdv0Co7DxQpbaliEgc6PQ6OjP7IrAe+ALwReCvZjY3mDd3zjU653KBEcC5wLhgCzOz280s38zy9+3bF+zLekRtQyO7D1VrRCciEgeCOQH1PeAc51wpgJmlA68Dzwb7Ic65cjNbA5wPpJlZkjeqG0FgpZW2XvMY8BhAXl5eu5c5hMKug9U4p10LRETiQTAroyQ0h5ynLJjXmVm6maV5t1OBy4ACYA3QPCK8CXj+pCoOg5Zr6BR0IiIxL5gR3StmthL4vXf/H4GXg3jdMGCpmSUSCMb/dc69YGYfAs+Y2Q+BjcDjXag7pIrLApcWaPkvEZHYF8wSYAvMbA5wgXfoMefcc0G87gNgShvHPyZwvi5qFZVV0SclkSH9UiJdioiIdFNH19GdAQx1zq1zzi0DlnnHLzCz051zH4WryHALLObcBzOLdCkiItJNHZ1rewg43MbxQ95jcau4rJIsXVogIhIXOgq6oc65zccf9I5lhayiCGtqcnx6sFoTUURE4kRHQZfWwWOpPV1ItPjscA11DU1azFlEJE50FHT5ZvbPxx80s9sILMYcl4pbtudR61JEJB50NOvyX4DnzGw+R4MtD0gBrg91YZGys2XXAo3oRETiQUfb9OwF/sHMLgYmeodfdM6tDktlEVJcVkVSgjHslN6RLkVERHpAMNfRrSGwmokvFB+oYsTAVJISg1k0RkREop3+a36c4rJKRunSAhGRuKGga8U5R3FZFaO1a4GISNxQ0LVSXlVPRU2DJqKIiMQRBV0rxQeady1Q61JEJF4o6Fpp3rVAIzoRkfgRzDY9cW/5xhKWrCykpLwagPc/LefMof0jXJWIiPQE3wfd8o0lLFq2mer6xpZj//H8VpITE5g9JTOClYmISE/wfetyycrCY0IOoLq+kSUrCyNUkYiI9CTfB91ur10Z7HEREYktvg+64Wltb8TQ3nEREYktvg+6BTOz6ZV07NeQmpzIgpnZJzw3KyuLSZMmkZubS15eXrhKFBGRbvD9ZJTZUzIpKqvkode3A5Derxffuzqn3Ykoa9asYciQIeEsUUREusH3QQeQM2xAy+3X7ryQtD4pEaxGRER6ku9blwD7KmpbbncUcmbG5Zdfztlnn81jjz0WjtJERKSbNKLj2KDryNq1a8nMzKS0tJTLLruMcePGceGFF4a4OhER6Q6N6ID9R4ILuszMwHm7jIwMrr/+etavXx/KskREpAco6IDPDtV0+pzKykoqKipabr/66qtMnDixk1eJiEikqXUJvL/rEAAJ1v5z9u7dy/XXXw9AQ0MDX/7yl7niiivCUZ6IiHSDgo6jrcusDrbnOe2003j//ffDVZKIiPQQ37cunXMtt7U9j4hI/PF90B2pbWi5feopvSNYiYiIhILvg671pQUJ1sFJOhERiUkKulZBp5wTEYk/CrpW19AZSjoRkXjj+6Dbtqei5bZGdCIi8ceXlxcs31jCkpWF7C6vJrnVFj3KORGR+OO7oFu+sYRFyzZTXd8IQF1DU8tjpiGdiEjc8V3rcsnKwpaQExGR+Oe7oNtdXt3uYxrQiYjEH98F3fC01HYf+7j0SBgrERGRcPBd0C2YmU1qcmKbj639qIzlG0vCXJGIiISS74Ju9pRMHpgzqc3HGpscS1YWttwvLCwkNze35WfAgAE89NBD4SpVRER6gO+CDgJh157W5/Cys7PZtGkTmzZtYsOGDfTp06dlqx4REYkNvgy62ob2Z122dw5v1apVnH766YwePTpUZYmISAj4Mugqa9sOuqQEY8HM7DYfe+aZZ5g3b14oyxIRkRAIWdCZ2UgzW2NmH5rZVjO7wzt+n5mVmNkm7+eqUNXQnkpva56BfZIDtXrHLzozvc22Zl1dHStWrODRRx9l1qxZ4SpTRER6QChHdA3Ad5xz44HzgG+a2XjvsQedc7nez0shrKFNlXWBoJt2xhAAXvj2BSQnGmee2r/N57/88ssMHjyYyZMnh61GERHpGSELOufcHufce97tCqAAaH8WSBg1j+hyR6YBsKP0CM61v9bl448/TkJCArfddluYKhQRkZ4SlnN0ZpYFTAH+6h36lpl9YGZPmNnAcNTQWvM5uvHDB5CYYIGgo+2VUSorK1m5ciWPPvooCQm+PKUpIhLTQv5fbjPrB/wJ+Bfn3GHgl8DpQC6wB/ivdl53u5nlm1n+vn37erSmo+foUhg9uA/b9x7BOdfmfnRr1qzh1ltv5aKLLurRGkREJDxCGnRmlkwg5H7rnFsG4Jzb65xrdM41Ab8Czm3rtc65x5xzec65vPT09B6t64gXdP16JXFGej+2l1a0O6Jbt24dK1asICsriy996UusXr2aG2+8sUfrERGR0AnlrEsDHgcKnHM/bXV8WKunXQ9sCVUN7amqC7Qu+/ZKYuzQfhSXVbV7ju6BBx5g165dFBUV8cwzz3DJJZfw9NNPh7dgERHpslDuRzcN+Aqw2cw2ece+C8wzs1zAAUXA10JYQ5uaR3R9UhIZm9GfhiYXeEDbF4iIxJ2QBZ1zbi1tD5LCfjnB8SprG0hKMHolJXBGRr+W453F3PTp05k+fXpIaxMRkZ7ly2mEVXWN9O2VhJlxenq/loGcBnQiIvHHl0F3pLaBvimBrXpSUxLJ9Na3bGvWpYiIxDZfBl1VXQN9ex3t2o712pca0YmIxB9fBt2R2sZjg25oYOkv5ZyISPzxZdBV1jbQt9fRXcYPV9cD8F+v/Z1pi1drl3ERkTji36BLCYzolm8s4blWwVZSXs2iZZsVdiIiccKfQdfqHN2SlYXUNjQd83h1fSNLVhZGojQREelh/gy62saW1uXu8uo2n9PecRERiS0+DbqjI7rh3qUFx2vvuIiIxBbfBV1DYxO1DU0t5+gWzMwmNTnxmOekJieyYGb2Ca8tLy9n7ty5jBs3jpycHN55552w1CwiIl0XyrUuo1LzXnTNI7rZUwJ7wS5ZWcju8mqGp6WyYGZ2y/HW7rjjDq644gqeffZZ6urqqKqqCl/hIiLSJf4LurrmLXqOjuJmT8lsM9haO3ToEG+++SZPPfUUACkpKaSkpISsThER6Rm+a11WtuxccHIZ/8knn5Cens5Xv/pVpkyZwm233UZlZWUoShQRkR7ku6BrvenqyWhoaOC9997jG9/4Bhs3bqRv374sXrw4FCWKiEgP8lXQLd9Ywj//Jh+ABc++f1IXhY8YMYIRI0YwdepUAObOnct7770XkjpFRKTn+Cbolm8sYdGyzew/UgfA/iN1J7UCyqmnnsrIkSMpLAxcSL5q1SrGjx8fsnpFRKRn+GYyypKVhVTXNx5zrHkFlM4mojT7+c9/zvz586mrq+O0007jySefDEWpIiLSg3wTdD2xAkpubi75+fk9VZKIiISBb1qXWgFFRMSffBN0J7MCioiIxA/fBN3sKZk8MGcSvZICf+XMtFQemDOp3fNzDz74IBMmTGDixInMmzePmpqacJYrIiI9xDdBB4GwGzu0H9Oz01m38JJ2Q66kpISf/exn5Ofns2XLFhobG3nmmWfCXK2IiPQEXwUdwP6KOob069Xp8xoaGqiurqahoYGqqiqGDx8ehupERKSn+SronHOUVdZ2GnSZmZncddddjBo1imHDhnHKKadw+eWXh6lKERHpSb4KukPV9dQ3Oob063gx5oMHD/L888/zySefsHv3biorK3n66afDVKWIiPQkXwXd/iO1AKT373hE9/rrrzNmzBjS09NJTk5mzpw5vP322+EoUUREepivgm5fRWD5r85al6NGjeLdd9+lqqoK5xyrVq0iJycnHCWKiEgP81XQNY/oOgu6qVOnMnfuXM466ywmTZpEU1MTt99+ezhKFBGRHuabJcCgddB1vmHq/fffz/333x/qkkREJMR8N6JLTDAG9tHO4CIifuGvoKuoY1DfFBISLNKliIhImPgr6I50fg2diIjEFx8GndqWIiJ+4rOgqyNdIzoREV/xTdA559h3pJYhnVwsLiIi8cU3QVdR20BdQ5NalyIiPuOboNtfEdzF4iIiEl/8E3RHglv+S0RE4ouPgk4jOhERP/Jf0PXXOToRET/xT9BV1GIGg7T8l4iIr/gm6PYdqWNQnxSSEn3zVxYREXwUdFr+S0TEn0IWdGY20szWmNmHZrbVzO7wjg8ys9fMbLv358BQ1dDa/iO1Oj8nIuJDoRzRNQDfcc6NB84Dvmlm44GFwCrn3FhglXc/pJZvLOGDXYdYt6OMaYtXs3xjSag/UkREokTIgs45t8c59553uwIoADKB64Cl3tOWArNDVQMEQm7Rss00NjkASsqrWbRss8JORMQnwnKOzsyygCnAX4Ghzrk93kOfAUPbec3tZpZvZvn79u3r8mcvWVlIdX3jMceq6xtZsrKwy+8pIiKxI+RBZ2b9gD8B/+KcO9z6MeecA1xbr3POPeacy3PO5aWnp3f583eXV5/UcRERiS8hDTozSyYQcr91zi3zDu81s2He48OA0lDWMDwt9aSOi4hIfAnlrEsDHgcKnHM/bfXQCuAm7/ZNwPOhqgFgwcxsUpMTjzmWmpzIgpnZJzz34YcfZuLEiUyYMIGHHnoolGWJiEiYhHJENw34CnCJmW3yfq4CFgOXmdl24FLvfsjMnpLJA3MmYd79zLRUHpgzidlTMo953pYtW/jVr37F+vXref/993nhhRfYsWNHKEsTEZEwSArVGzvn1kJLvhxvRqg+ty2zp2Tywxc/5PIJp/Lj6ye1+ZyCggKmTp1Knz59ALjoootYtmwZd999dzhLFRGRHuablVFSEhOorW9q9/GJEyfy1ltvUVZWRlVVFS+99BKffvppGCsUEZFQCNmILtqkJCVQ19h+0OXk5HDPPfdw+eWX07dvX3Jzc0lMTGz3+SIiEhv8M6JLSqCuobHD59x6661s2LCBN998k4EDB3LmmWeGqToREQkV34zoeiUlUtfQ/ogOoLS0lIyMDHbu3MmyZct49913w1SdiIiEim+CrrPWJcANN9xAWVkZycnJPPLII6SlpYWpOhERCRX/BF0nk1EA3nrrrTBVIyIi4eKvc3SdjOhERCT++CvoOjlHJyIi8cc3QddLQSci4ku+CbqUpARqFXQiIr7jm6DrpaATEfEl3wRdSmLnF4yLiEj88U/QadaliIgv+SboglkZRURE4o9vgi4lKYEmBw0a1YmI+Iqvgg7QhBQREZ/xT9AlBv6qal+KiPiLb4KuV7IXdGpdioj4im+CTiM6ERF/8k/Q6RydiIgv+SboerUEnS4aFxHxE98EXfOITq1LERF/8U3Q9UpKBBR0IiJ+45ugaxnRadaliIiv+CfoNOtSRMSX/BN0OkcnIuJLvgs6XV4gIuIvvgm6XhrRiYj4km+CrmVEp8koIiK+4pug65WoywtERPzIN0GnySgiIv7ku6DTEmAiIv7im6BLTDCSEkwjOhERn/FN0EFgVKegExHxF98E3fKNJVTXN/LrtZ8wbfFqlm8siXRJIiISBr4IuuUbS1i0bDPOBe6XlFezaNlmhZ2IiA/4IuiWrCykuv7YSSjV9Y0sWVkYoYpERCRcfBF0u8urT+q4iIjED18E3fC01JM6LiIi8cMXQbdgZjapyYnHHEtNTmTBzOwTnnvLLbeQkZHBxIkTW44dOHCAyy67jLFjx3LZZZdx8ODBkNcsIiI9wxdBN3tKJg/MmURmWioGZKal8sCcScyeknnCc2+++WZeeeWVY44tXryYGTNmsH37dmbMmMHixYvDVLmIiHSXueapiFEsLy/P5efnh+3zioqKmDVrFlu2bAEgOzubN954g2HDhrFnzx6mT59OYaEmsoiIRJKZbXDO5XX2PF+M6Lpr7969DBs2DIBTTz2VvXv3RrgiEREJVsiCzsyeMLNSM9vS6th9ZlZiZpu8n6tC9fmhYmaYWaTLEBGRIIVyRPcUcEUbxx90zuV6Py+F8PN7zNChQ9mzZw8Ae/bsISMjI8IViYhIsEIWdM65N4EDoXr/cLr22mtZunQpAEuXLuW6666LcEUiIhKsSJyj+5aZfeC1Nge29yQzu93M8s0sf9++fWErbt68eZx//vkUFhYyYsQIHn/8cRYuXMhrr73G2LFjef3111m4cGHY6hERke4J6axLM8sCXnDOTfTuDwX2Aw74ATDMOXdLZ+8T7lmXIiIS/aJy1qVzbq9zrtE51wT8Cjg3nJ8vIiL+E9agM7Nhre5eD2xp77kiIiI9ISlUb2xmvwemA0PMbBdwLzDdzHIJtC6LgK+F6vNFREQghEHnnJvXxuHHQ/V5IiIibdHKKCIiEtcUdCIiEtdiYlFnM9sHFAfx1CEELl+QAH0fR+m7OJa+j6P0XRwrlr6P0c659M6eFBNBFywzyw/mmgq/0PdxlL6LY+n7OErfxbHi8ftQ61JEROKagk5EROJavAXdY5EuIMro+zhK38Wx9H0cpe/iWHH3fcTVOToREZHjxduITkRE5BhxE3RmdoWZFZrZDjPz1T467ezmPsjMXjOz7d6f7W6JFG/MbKSZrTGzD81sq5nd4R333XdiZr3NbL2Zve99F/d7x8eY2V+935c/mFlKpGsNFzNLNLONZvaCd9/P30WRmW02s01mlu8di7vfk7gIOjNLBB4BrgTGA/PMbHxkqwqrpzhxN/eFwCrn3FhglXffLxqA7zjnxgPnAd/0/v/gx++kFrjEOfc5IBe4wszOA/4TeNA5dwZwELg1gjWG2x1AQav7fv4uAC52zuW2uqQg7n5P4iLoCGz3s8M597Fzrg54BvDNNuDt7OZ+HbDUu70UmB3WoiLIObfHOfeed7uCwH/UMvHhd+ICjnh3k70fB1wCPOsd98V3AWBmI4CrgV979w2ffhcdiLvfk3gJukzg01b3d3nH/Gyoc26Pd/szYGgki4kUb/PfKcBf8el34rXqNgGlwGvAR0C5c67Be4qffl8eAu4Gmrz7g/HvdwGBf/S8amYbzOx271jc/Z6EbPcCiR7OOWdmvptea2b9gD8B/+KcOxz4x3uAn74T51wjkGtmacBzwLgIlxQRZjYLKHXObTCz6ZGuJ0pc4JwrMbMM4DUz29b6wXj5PYmXEV0JMLLV/RHeMT/b27zRrfdnaYTrCSszSyYQcr91zi3zDvv6O3HOlQNrgPOBNDNr/oeuX35fpgHXmlkRgdMblwAP48/vAgDnXIn3ZymBfwSdSxz+nsRL0P0NGOvNnkoBvgSsiHBNkbYCuMm7fRPwfARrCSvvvMvjQIFz7qetHvLdd2Jm6d5IDjNLBS4jcM5yDTDXe5ovvgvn3CLn3AjnXBaB/0asds7Nx4ffBYCZ9TWz/s23gcuBLcTh70ncXDBuZlcR6L8nAk84534U4ZLCpvVu7sBeAru5Lwf+FxhFYOeHLzrnjp+wEpfM7ALgLWAzR8/FfJfAeTpffSdmNpnAhIJEAv+w/V/n3PfN7DQCo5pBwEbgRudcbeQqDS+vdXmXc26WX78L7+/9nHc3Cfidc+5HZjaYOPs9iZugExERaUu8tC5FRETapKATEZG4pqATEZG4pqATEZG4pqATEZG4pqATaYOZOTN7utX9JDPb17zifQevyzOzn53E5/zVWzl+p/f+m7yfrJOs92kz63BNQu85n7T6jLdO5jNEYpWWABNpWyUw0cxSnXPVBC607nTFDOdcPpAf7Ic456YCmNnNQJ5z7ltdKzdo/+qcW97eg2aW1GrdxxPuB/s6kWiiEZ1I+14isNI9wDzg980PmNm5ZvaOt6/Z22aW7R2f3mqfs/sssFfgG2b2sZl9+2Q+3MweM7N8bx+5/2h1fIkF9tr7wMz+s43XPWBmj5tZUL/fZvZDM/uNma0DnjKz28xsuZmtAVaaWYKZ/dTMtnh7l831Xnep93d7gcDF+SJRSSM6kfY9A/yH9x/yycATwOe9x7YBn3fONZjZpcCPgRvaeI9xwMVAf6DQzH7pnKsP8vMXOucOeOswrjGzZ4Ey4CpggrfgblrrF5jZg0AKcJtrezWIB83sPu/2B865f2pV54XOuRozu43Ajg+5zrmDZvaPQA7wOSAd+JuZvem9Lg8Y75zbGeTfSSTsFHQi7XDOfeCdK5tHYHTX2inAUjMbS2Crk+R23uZFbzmpWjMrJbDlya4gS5hnZrcS+D0dTmBT4ecJLGv2KzN7EWh9zvB+YJ1z7v908J7ttS6fd87VtLr/qnPuoHf7AuD33i4In5nZWgIBVwe8o5CTaKfWpUjHVgA/oVXb0vMDYI1zbiJwDdC7nde3XjOxkSD/cekF6B0EdgefDLwC9PZGg3kE1jKdDbzY6mXrgXPMbGAwn3Gcyk7uB/s6kaijoBPp2BPA/c65489BncLRySk3h+BzBwAVwGFvq5SZAN5q8wOccy8A/0qgxdjsReC/gBe8vfh6ylvAl7xzdUMJbHcT9IQbkUhT61KkA865XUBblwv8fwRal//GsaOqnvIe8CGBc4HFwDrv+CnAMjPrReAfqnceV+8zXhg+b2ZXH9eOhGPP0QGcHUQtzwLnAR8QaNPe6ZwrtVYb2YpEM+1eICIicU2tSxERiWsKOhERiWsKOhERiWsKOhERiWsKOhERiWsKOhERiWsKOhERiWsKOhERiWv/P6uVt4BZojc0AAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<Figure size 504x504 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "draw_pareto(main_results, collateral_results)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "What we observe here is interesting, the main task does not present significant weakness until we get at size 3 where we drop to 97.5% which is still very good although 1% under the other accuracies. In return, the collateral accuracy starts significantly to decrease for output size 5, and keeps decreasing. At size 4, it is only 67% on average so 15% less than the baseline.\n",
    "\n",
    "As a conclusion, depending on how crucial the main task is, we might keep an output size of 3 or 4."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 4.3 When the collateral model is a CNN"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We perform the same experiment but with the collateral model being a CNN, to see is we observ similar behaviours."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "metadata": {},
   "outputs": [],
   "source": [
    "class CollateralNet(nn.Module):\n",
    "    def __init__(self):\n",
    "        super(CollateralNet, self).__init__()\n",
    "        self.proj1 = nn.Linear(784, 50)\n",
    "        self.diag1 = nn.Linear(50, 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",
    "    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",
    "        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\n",
    "    \n",
    "    "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      " ----- 10\n",
      "Training set 60000 items\n",
      "Testing set  10000 items\n",
      "Train Epoch: 1 [0/60000 (0%)]\tLoss Char: 2.344008 Loss Font: 0.686329\n",
      "Train Epoch: 1 [19200/60000 (32%)]\tLoss Char: 1.546081 Loss Font: 0.689847\n",
      "Train Epoch: 1 [38400/60000 (64%)]\tLoss Char: 0.467052 Loss Font: 0.563169\n",
      "Train Epoch: 1 [57600/60000 (96%)]\tLoss Char: 0.429006 Loss Font: 0.467837\n",
      "\n",
      "Test set: Accuracy Char : 8995/10000 (89.95%)\n",
      "          Accuracy Font : 7667/10000 (76.67%)\n",
      "Train Epoch: 2 [0/60000 (0%)]\tLoss Char: 0.267551 Loss Font: 0.528971\n",
      "Train Epoch: 2 [19200/60000 (32%)]\tLoss Char: 0.172447 Loss Font: 0.408802\n",
      "Train Epoch: 2 [38400/60000 (64%)]\tLoss Char: 0.158899 Loss Font: 0.373531\n",
      "Train Epoch: 2 [57600/60000 (96%)]\tLoss Char: 0.042128 Loss Font: 0.346760\n",
      "\n",
      "Test set: Accuracy Char : 9606/10000 (96.06%)\n",
      "          Accuracy Font : 8309/10000 (83.09%)\n",
      "Train Epoch: 3 [0/60000 (0%)]\tLoss Char: 0.160753 Loss Font: 0.377356\n",
      "Train Epoch: 3 [19200/60000 (32%)]\tLoss Char: 0.177379 Loss Font: 0.313664\n",
      "Train Epoch: 3 [38400/60000 (64%)]\tLoss Char: 0.059074 Loss Font: 0.263489\n",
      "Train Epoch: 3 [57600/60000 (96%)]\tLoss Char: 0.020948 Loss Font: 0.323242\n",
      "\n",
      "Test set: Accuracy Char : 9749/10000 (97.49%)\n",
      "          Accuracy Font : 8707/10000 (87.07%)\n",
      "Train Epoch: 4 [0/60000 (0%)]\tLoss Char: 0.080589 Loss Font: 0.268734\n",
      "Train Epoch: 4 [19200/60000 (32%)]\tLoss Char: 0.067155 Loss Font: 0.338080\n",
      "Train Epoch: 4 [38400/60000 (64%)]\tLoss Char: 0.021246 Loss Font: 0.247119\n",
      "Train Epoch: 4 [57600/60000 (96%)]\tLoss Char: 0.072788 Loss Font: 0.411383\n",
      "\n",
      "Test set: Accuracy Char : 9797/10000 (97.97%)\n",
      "          Accuracy Font : 8845/10000 (88.45%)\n",
      "Train Epoch: 5 [0/60000 (0%)]\tLoss Char: 0.049721 Loss Font: 0.243902\n",
      "Train Epoch: 5 [19200/60000 (32%)]\tLoss Char: 0.031504 Loss Font: 0.152850\n",
      "Train Epoch: 5 [38400/60000 (64%)]\tLoss Char: 0.019442 Loss Font: 0.237587\n",
      "Train Epoch: 5 [57600/60000 (96%)]\tLoss Char: 0.028912 Loss Font: 0.169623\n",
      "\n",
      "Test set: Accuracy Char : 9847/10000 (98.47%)\n",
      "          Accuracy Font : 9022/10000 (90.22%)\n",
      "Train Epoch: 6 [0/60000 (0%)]\tLoss Char: 0.067548 Loss Font: 0.225026\n",
      "Train Epoch: 6 [19200/60000 (32%)]\tLoss Char: 0.057518 Loss Font: 0.175087\n",
      "Train Epoch: 6 [38400/60000 (64%)]\tLoss Char: 0.045083 Loss Font: 0.247748\n",
      "Train Epoch: 6 [57600/60000 (96%)]\tLoss Char: 0.022090 Loss Font: 0.315431\n",
      "\n",
      "Test set: Accuracy Char : 9868/10000 (98.68%)\n",
      "          Accuracy Font : 9085/10000 (90.85%)\n",
      "Train Epoch: 7 [0/60000 (0%)]\tLoss Char: 0.017674 Loss Font: 0.187677\n",
      "Train Epoch: 7 [19200/60000 (32%)]\tLoss Char: 0.019737 Loss Font: 0.116161\n",
      "Train Epoch: 7 [38400/60000 (64%)]\tLoss Char: 0.006570 Loss Font: 0.241557\n",
      "Train Epoch: 7 [57600/60000 (96%)]\tLoss Char: 0.003086 Loss Font: 0.183721\n",
      "\n",
      "Test set: Accuracy Char : 9869/10000 (98.69%)\n",
      "          Accuracy Font : 9067/10000 (90.67%)\n",
      "Train Epoch: 8 [0/60000 (0%)]\tLoss Char: 0.018069 Loss Font: 0.335134\n",
      "Train Epoch: 8 [19200/60000 (32%)]\tLoss Char: 0.014542 Loss Font: 0.205262\n",
      "Train Epoch: 8 [38400/60000 (64%)]\tLoss Char: 0.082162 Loss Font: 0.141769\n",
      "Train Epoch: 8 [57600/60000 (96%)]\tLoss Char: 0.012933 Loss Font: 0.215234\n",
      "\n",
      "Test set: Accuracy Char : 9885/10000 (98.85%)\n",
      "          Accuracy Font : 9147/10000 (91.47%)\n",
      "Train Epoch: 9 [0/60000 (0%)]\tLoss Char: 0.003098 Loss Font: 0.222363\n",
      "Train Epoch: 9 [19200/60000 (32%)]\tLoss Char: 0.007858 Loss Font: 0.274976\n",
      "Train Epoch: 9 [38400/60000 (64%)]\tLoss Char: 0.026865 Loss Font: 0.161766\n",
      "Train Epoch: 9 [57600/60000 (96%)]\tLoss Char: 0.021226 Loss Font: 0.151798\n",
      "\n",
      "Test set: Accuracy Char : 9897/10000 (98.97%)\n",
      "          Accuracy Font : 9134/10000 (91.34%)\n",
      "Train Epoch: 10 [0/60000 (0%)]\tLoss Char: 0.006240 Loss Font: 0.268588\n",
      "Train Epoch: 10 [19200/60000 (32%)]\tLoss Char: 0.023059 Loss Font: 0.151218\n",
      "Train Epoch: 10 [38400/60000 (64%)]\tLoss Char: 0.003634 Loss Font: 0.274522\n",
      "Train Epoch: 10 [57600/60000 (96%)]\tLoss Char: 0.001447 Loss Font: 0.192749\n",
      "\n",
      "Test set: Accuracy Char : 9897/10000 (98.97%)\n",
      "          Accuracy Font : 9224/10000 (92.24%)\n",
      "\n",
      " ----- 9\n",
      "Training set 60000 items\n",
      "Testing set  10000 items\n",
      "Train Epoch: 1 [0/60000 (0%)]\tLoss Char: 2.312963 Loss Font: 0.695867\n",
      "Train Epoch: 1 [19200/60000 (32%)]\tLoss Char: 1.797019 Loss Font: 0.694090\n",
      "Train Epoch: 1 [38400/60000 (64%)]\tLoss Char: 0.842925 Loss Font: 0.619542\n",
      "Train Epoch: 1 [57600/60000 (96%)]\tLoss Char: 0.529508 Loss Font: 0.636348\n",
      "\n",
      "Test set: Accuracy Char : 8541/10000 (85.41%)\n",
      "          Accuracy Font : 7582/10000 (75.82%)\n",
      "Train Epoch: 2 [0/60000 (0%)]\tLoss Char: 0.435394 Loss Font: 0.533089\n",
      "Train Epoch: 2 [19200/60000 (32%)]\tLoss Char: 0.443677 Loss Font: 0.557858\n",
      "Train Epoch: 2 [38400/60000 (64%)]\tLoss Char: 0.211371 Loss Font: 0.350378\n",
      "Train Epoch: 2 [57600/60000 (96%)]\tLoss Char: 0.165545 Loss Font: 0.428470\n",
      "\n",
      "Test set: Accuracy Char : 9495/10000 (94.95%)\n",
      "          Accuracy Font : 8070/10000 (80.70%)\n",
      "Train Epoch: 3 [0/60000 (0%)]\tLoss Char: 0.211333 Loss Font: 0.338074\n",
      "Train Epoch: 3 [19200/60000 (32%)]\tLoss Char: 0.148430 Loss Font: 0.320975\n",
      "Train Epoch: 3 [38400/60000 (64%)]\tLoss Char: 0.066269 Loss Font: 0.250607\n",
      "Train Epoch: 3 [57600/60000 (96%)]\tLoss Char: 0.023699 Loss Font: 0.260381\n",
      "\n",
      "Test set: Accuracy Char : 9674/10000 (96.74%)\n",
      "          Accuracy Font : 8377/10000 (83.77%)\n",
      "Train Epoch: 4 [0/60000 (0%)]\tLoss Char: 0.106313 Loss Font: 0.363011\n",
      "Train Epoch: 4 [19200/60000 (32%)]\tLoss Char: 0.114666 Loss Font: 0.333586\n",
      "Train Epoch: 4 [38400/60000 (64%)]\tLoss Char: 0.016923 Loss Font: 0.227340\n",
      "Train Epoch: 4 [57600/60000 (96%)]\tLoss Char: 0.055071 Loss Font: 0.249032\n",
      "\n",
      "Test set: Accuracy Char : 9814/10000 (98.14%)\n",
      "          Accuracy Font : 8427/10000 (84.27%)\n",
      "Train Epoch: 5 [0/60000 (0%)]\tLoss Char: 0.086285 Loss Font: 0.371724\n",
      "Train Epoch: 5 [19200/60000 (32%)]\tLoss Char: 0.061162 Loss Font: 0.171782\n",
      "Train Epoch: 5 [38400/60000 (64%)]\tLoss Char: 0.079003 Loss Font: 0.298908\n",
      "Train Epoch: 5 [57600/60000 (96%)]\tLoss Char: 0.040328 Loss Font: 0.370110\n",
      "\n",
      "Test set: Accuracy Char : 9833/10000 (98.33%)\n",
      "          Accuracy Font : 8631/10000 (86.31%)\n",
      "Train Epoch: 6 [0/60000 (0%)]\tLoss Char: 0.274732 Loss Font: 0.443960\n",
      "Train Epoch: 6 [19200/60000 (32%)]\tLoss Char: 0.043467 Loss Font: 0.342063\n",
      "Train Epoch: 6 [38400/60000 (64%)]\tLoss Char: 0.023108 Loss Font: 0.293390\n",
      "Train Epoch: 6 [57600/60000 (96%)]\tLoss Char: 0.023598 Loss Font: 0.286767\n",
      "\n",
      "Test set: Accuracy Char : 9833/10000 (98.33%)\n",
      "          Accuracy Font : 8800/10000 (88.00%)\n",
      "Train Epoch: 7 [0/60000 (0%)]\tLoss Char: 0.063240 Loss Font: 0.379673\n",
      "Train Epoch: 7 [19200/60000 (32%)]\tLoss Char: 0.026676 Loss Font: 0.265215\n",
      "Train Epoch: 7 [38400/60000 (64%)]\tLoss Char: 0.007369 Loss Font: 0.256559\n",
      "Train Epoch: 7 [57600/60000 (96%)]\tLoss Char: 0.004551 Loss Font: 0.290124\n",
      "\n",
      "Test set: Accuracy Char : 9858/10000 (98.58%)\n",
      "          Accuracy Font : 8754/10000 (87.54%)\n",
      "Train Epoch: 8 [0/60000 (0%)]\tLoss Char: 0.038848 Loss Font: 0.193797\n",
      "Train Epoch: 8 [19200/60000 (32%)]\tLoss Char: 0.017413 Loss Font: 0.164147\n",
      "Train Epoch: 8 [38400/60000 (64%)]\tLoss Char: 0.107364 Loss Font: 0.267484\n",
      "Train Epoch: 8 [57600/60000 (96%)]\tLoss Char: 0.010054 Loss Font: 0.294089\n",
      "\n",
      "Test set: Accuracy Char : 9877/10000 (98.77%)\n",
      "          Accuracy Font : 8882/10000 (88.82%)\n",
      "Train Epoch: 9 [0/60000 (0%)]\tLoss Char: 0.006287 Loss Font: 0.230637\n",
      "Train Epoch: 9 [19200/60000 (32%)]\tLoss Char: 0.007123 Loss Font: 0.311401\n",
      "Train Epoch: 9 [38400/60000 (64%)]\tLoss Char: 0.017742 Loss Font: 0.338972\n",
      "Train Epoch: 9 [57600/60000 (96%)]\tLoss Char: 0.029982 Loss Font: 0.130956\n",
      "\n",
      "Test set: Accuracy Char : 9877/10000 (98.77%)\n",
      "          Accuracy Font : 8733/10000 (87.33%)\n",
      "Train Epoch: 10 [0/60000 (0%)]\tLoss Char: 0.009375 Loss Font: 0.293302\n",
      "Train Epoch: 10 [19200/60000 (32%)]\tLoss Char: 0.001413 Loss Font: 0.239374\n",
      "Train Epoch: 10 [38400/60000 (64%)]\tLoss Char: 0.001099 Loss Font: 0.266978\n",
      "Train Epoch: 10 [57600/60000 (96%)]\tLoss Char: 0.072559 Loss Font: 0.286337\n",
      "\n",
      "Test set: Accuracy Char : 9896/10000 (98.96%)\n",
      "          Accuracy Font : 8816/10000 (88.16%)\n",
      "\n",
      " ----- 8\n",
      "Training set 60000 items\n",
      "Testing set  10000 items\n",
      "Train Epoch: 1 [0/60000 (0%)]\tLoss Char: 2.291793 Loss Font: 0.688408\n",
      "Train Epoch: 1 [19200/60000 (32%)]\tLoss Char: 1.355415 Loss Font: 0.710610\n",
      "Train Epoch: 1 [38400/60000 (64%)]\tLoss Char: 0.664689 Loss Font: 0.637879\n",
      "Train Epoch: 1 [57600/60000 (96%)]\tLoss Char: 0.316847 Loss Font: 0.484296\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "Test set: Accuracy Char : 8880/10000 (88.80%)\n",
      "          Accuracy Font : 7389/10000 (73.89%)\n",
      "Train Epoch: 2 [0/60000 (0%)]\tLoss Char: 0.324607 Loss Font: 0.518692\n",
      "Train Epoch: 2 [19200/60000 (32%)]\tLoss Char: 0.288807 Loss Font: 0.544927\n",
      "Train Epoch: 2 [38400/60000 (64%)]\tLoss Char: 0.208885 Loss Font: 0.382508\n",
      "Train Epoch: 2 [57600/60000 (96%)]\tLoss Char: 0.063677 Loss Font: 0.293738\n",
      "\n",
      "Test set: Accuracy Char : 9620/10000 (96.20%)\n",
      "          Accuracy Font : 8389/10000 (83.89%)\n",
      "Train Epoch: 3 [0/60000 (0%)]\tLoss Char: 0.184312 Loss Font: 0.274271\n",
      "Train Epoch: 3 [19200/60000 (32%)]\tLoss Char: 0.162024 Loss Font: 0.327895\n",
      "Train Epoch: 3 [38400/60000 (64%)]\tLoss Char: 0.017418 Loss Font: 0.283681\n",
      "Train Epoch: 3 [57600/60000 (96%)]\tLoss Char: 0.026876 Loss Font: 0.241334\n",
      "\n",
      "Test set: Accuracy Char : 9754/10000 (97.54%)\n",
      "          Accuracy Font : 8758/10000 (87.58%)\n",
      "Train Epoch: 4 [0/60000 (0%)]\tLoss Char: 0.124584 Loss Font: 0.315254\n",
      "Train Epoch: 4 [19200/60000 (32%)]\tLoss Char: 0.050604 Loss Font: 0.318756\n",
      "Train Epoch: 4 [38400/60000 (64%)]\tLoss Char: 0.008207 Loss Font: 0.245464\n",
      "Train Epoch: 4 [57600/60000 (96%)]\tLoss Char: 0.049542 Loss Font: 0.291098\n",
      "\n",
      "Test set: Accuracy Char : 9805/10000 (98.05%)\n",
      "          Accuracy Font : 8691/10000 (86.91%)\n",
      "Train Epoch: 5 [0/60000 (0%)]\tLoss Char: 0.039080 Loss Font: 0.391795\n",
      "Train Epoch: 5 [19200/60000 (32%)]\tLoss Char: 0.029649 Loss Font: 0.231999\n",
      "Train Epoch: 5 [38400/60000 (64%)]\tLoss Char: 0.017353 Loss Font: 0.214053\n",
      "Train Epoch: 5 [57600/60000 (96%)]\tLoss Char: 0.017795 Loss Font: 0.234005\n",
      "\n",
      "Test set: Accuracy Char : 9849/10000 (98.49%)\n",
      "          Accuracy Font : 8926/10000 (89.26%)\n",
      "Train Epoch: 6 [0/60000 (0%)]\tLoss Char: 0.049816 Loss Font: 0.312114\n",
      "Train Epoch: 6 [19200/60000 (32%)]\tLoss Char: 0.119362 Loss Font: 0.236814\n",
      "Train Epoch: 6 [38400/60000 (64%)]\tLoss Char: 0.012125 Loss Font: 0.215715\n",
      "Train Epoch: 6 [57600/60000 (96%)]\tLoss Char: 0.007967 Loss Font: 0.172412\n",
      "\n",
      "Test set: Accuracy Char : 9859/10000 (98.59%)\n",
      "          Accuracy Font : 9012/10000 (90.12%)\n",
      "Train Epoch: 7 [0/60000 (0%)]\tLoss Char: 0.087011 Loss Font: 0.188962\n",
      "Train Epoch: 7 [19200/60000 (32%)]\tLoss Char: 0.017621 Loss Font: 0.184992\n",
      "Train Epoch: 7 [38400/60000 (64%)]\tLoss Char: 0.005289 Loss Font: 0.191740\n",
      "Train Epoch: 7 [57600/60000 (96%)]\tLoss Char: 0.006383 Loss Font: 0.358480\n",
      "\n",
      "Test set: Accuracy Char : 9808/10000 (98.08%)\n",
      "          Accuracy Font : 8574/10000 (85.74%)\n",
      "Train Epoch: 8 [0/60000 (0%)]\tLoss Char: 0.007530 Loss Font: 0.453255\n",
      "Train Epoch: 8 [19200/60000 (32%)]\tLoss Char: 0.008198 Loss Font: 0.180968\n",
      "Train Epoch: 8 [38400/60000 (64%)]\tLoss Char: 0.106433 Loss Font: 0.212128\n",
      "Train Epoch: 8 [57600/60000 (96%)]\tLoss Char: 0.033551 Loss Font: 0.191043\n",
      "\n",
      "Test set: Accuracy Char : 9870/10000 (98.70%)\n",
      "          Accuracy Font : 8974/10000 (89.74%)\n",
      "Train Epoch: 9 [0/60000 (0%)]\tLoss Char: 0.006334 Loss Font: 0.250002\n",
      "Train Epoch: 9 [19200/60000 (32%)]\tLoss Char: 0.005507 Loss Font: 0.214547\n",
      "Train Epoch: 9 [38400/60000 (64%)]\tLoss Char: 0.057282 Loss Font: 0.255658\n",
      "Train Epoch: 9 [57600/60000 (96%)]\tLoss Char: 0.012191 Loss Font: 0.219506\n",
      "\n",
      "Test set: Accuracy Char : 9892/10000 (98.92%)\n",
      "          Accuracy Font : 9079/10000 (90.79%)\n",
      "Train Epoch: 10 [0/60000 (0%)]\tLoss Char: 0.002994 Loss Font: 0.227587\n",
      "Train Epoch: 10 [19200/60000 (32%)]\tLoss Char: 0.001994 Loss Font: 0.212161\n",
      "Train Epoch: 10 [38400/60000 (64%)]\tLoss Char: 0.001465 Loss Font: 0.300013\n",
      "Train Epoch: 10 [57600/60000 (96%)]\tLoss Char: 0.019875 Loss Font: 0.246858\n",
      "\n",
      "Test set: Accuracy Char : 9891/10000 (98.91%)\n",
      "          Accuracy Font : 9056/10000 (90.56%)\n",
      "\n",
      " ----- 7\n",
      "Training set 60000 items\n",
      "Testing set  10000 items\n",
      "Train Epoch: 1 [0/60000 (0%)]\tLoss Char: 2.287862 Loss Font: 0.704765\n",
      "Train Epoch: 1 [19200/60000 (32%)]\tLoss Char: 1.301885 Loss Font: 0.749234\n",
      "Train Epoch: 1 [38400/60000 (64%)]\tLoss Char: 0.566263 Loss Font: 0.495624\n",
      "Train Epoch: 1 [57600/60000 (96%)]\tLoss Char: 0.341805 Loss Font: 0.595556\n",
      "\n",
      "Test set: Accuracy Char : 8880/10000 (88.80%)\n",
      "          Accuracy Font : 7779/10000 (77.79%)\n",
      "Train Epoch: 2 [0/60000 (0%)]\tLoss Char: 0.377171 Loss Font: 0.382284\n",
      "Train Epoch: 2 [19200/60000 (32%)]\tLoss Char: 0.235882 Loss Font: 0.442813\n",
      "Train Epoch: 2 [38400/60000 (64%)]\tLoss Char: 0.175930 Loss Font: 0.298292\n",
      "Train Epoch: 2 [57600/60000 (96%)]\tLoss Char: 0.072462 Loss Font: 0.269706\n",
      "\n",
      "Test set: Accuracy Char : 9596/10000 (95.96%)\n",
      "          Accuracy Font : 8447/10000 (84.47%)\n",
      "Train Epoch: 3 [0/60000 (0%)]\tLoss Char: 0.113572 Loss Font: 0.326008\n",
      "Train Epoch: 3 [19200/60000 (32%)]\tLoss Char: 0.130988 Loss Font: 0.263135\n",
      "Train Epoch: 3 [38400/60000 (64%)]\tLoss Char: 0.020320 Loss Font: 0.302834\n",
      "Train Epoch: 3 [57600/60000 (96%)]\tLoss Char: 0.018431 Loss Font: 0.219566\n",
      "\n",
      "Test set: Accuracy Char : 9730/10000 (97.30%)\n",
      "          Accuracy Font : 8665/10000 (86.65%)\n",
      "Train Epoch: 4 [0/60000 (0%)]\tLoss Char: 0.088020 Loss Font: 0.274146\n",
      "Train Epoch: 4 [19200/60000 (32%)]\tLoss Char: 0.065737 Loss Font: 0.344123\n",
      "Train Epoch: 4 [38400/60000 (64%)]\tLoss Char: 0.033555 Loss Font: 0.205425\n",
      "Train Epoch: 4 [57600/60000 (96%)]\tLoss Char: 0.137264 Loss Font: 0.368756\n",
      "\n",
      "Test set: Accuracy Char : 9817/10000 (98.17%)\n",
      "          Accuracy Font : 8823/10000 (88.23%)\n",
      "Train Epoch: 5 [0/60000 (0%)]\tLoss Char: 0.040833 Loss Font: 0.320855\n",
      "Train Epoch: 5 [19200/60000 (32%)]\tLoss Char: 0.028525 Loss Font: 0.224116\n",
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      "Train Epoch: 5 [57600/60000 (96%)]\tLoss Char: 0.036593 Loss Font: 0.323206\n",
      "\n",
      "Test set: Accuracy Char : 9826/10000 (98.26%)\n",
      "          Accuracy Font : 8941/10000 (89.41%)\n",
      "Train Epoch: 6 [0/60000 (0%)]\tLoss Char: 0.097057 Loss Font: 0.313098\n",
      "Train Epoch: 6 [19200/60000 (32%)]\tLoss Char: 0.144938 Loss Font: 0.211151\n",
      "Train Epoch: 6 [38400/60000 (64%)]\tLoss Char: 0.033378 Loss Font: 0.215257\n",
      "Train Epoch: 6 [57600/60000 (96%)]\tLoss Char: 0.004645 Loss Font: 0.288950\n",
      "\n",
      "Test set: Accuracy Char : 9856/10000 (98.56%)\n",
      "          Accuracy Font : 8890/10000 (88.90%)\n",
      "Train Epoch: 7 [0/60000 (0%)]\tLoss Char: 0.030743 Loss Font: 0.272632\n",
      "Train Epoch: 7 [19200/60000 (32%)]\tLoss Char: 0.009080 Loss Font: 0.227566\n",
      "Train Epoch: 7 [38400/60000 (64%)]\tLoss Char: 0.021688 Loss Font: 0.241916\n",
      "Train Epoch: 7 [57600/60000 (96%)]\tLoss Char: 0.001506 Loss Font: 0.256647\n",
      "\n",
      "Test set: Accuracy Char : 9876/10000 (98.76%)\n",
      "          Accuracy Font : 8886/10000 (88.86%)\n",
      "Train Epoch: 8 [0/60000 (0%)]\tLoss Char: 0.008474 Loss Font: 0.239363\n",
      "Train Epoch: 8 [19200/60000 (32%)]\tLoss Char: 0.001405 Loss Font: 0.164252\n",
      "Train Epoch: 8 [38400/60000 (64%)]\tLoss Char: 0.103319 Loss Font: 0.251456\n",
      "Train Epoch: 8 [57600/60000 (96%)]\tLoss Char: 0.014281 Loss Font: 0.282770\n",
      "\n",
      "Test set: Accuracy Char : 9893/10000 (98.93%)\n",
      "          Accuracy Font : 9036/10000 (90.36%)\n",
      "Train Epoch: 9 [0/60000 (0%)]\tLoss Char: 0.003589 Loss Font: 0.210140\n",
      "Train Epoch: 9 [19200/60000 (32%)]\tLoss Char: 0.008597 Loss Font: 0.192620\n",
      "Train Epoch: 9 [38400/60000 (64%)]\tLoss Char: 0.014756 Loss Font: 0.309408\n",
      "Train Epoch: 9 [57600/60000 (96%)]\tLoss Char: 0.018272 Loss Font: 0.184615\n",
      "\n",
      "Test set: Accuracy Char : 9894/10000 (98.94%)\n",
      "          Accuracy Font : 8955/10000 (89.55%)\n",
      "Train Epoch: 10 [0/60000 (0%)]\tLoss Char: 0.003146 Loss Font: 0.196574\n",
      "Train Epoch: 10 [19200/60000 (32%)]\tLoss Char: 0.004169 Loss Font: 0.277112\n",
      "Train Epoch: 10 [38400/60000 (64%)]\tLoss Char: 0.003018 Loss Font: 0.350608\n",
      "Train Epoch: 10 [57600/60000 (96%)]\tLoss Char: 0.005241 Loss Font: 0.249708\n",
      "\n",
      "Test set: Accuracy Char : 9902/10000 (99.02%)\n",
      "          Accuracy Font : 8976/10000 (89.76%)\n",
      "\n",
      " ----- 6\n",
      "Training set 60000 items\n",
      "Testing set  10000 items\n",
      "Train Epoch: 1 [0/60000 (0%)]\tLoss Char: 2.320807 Loss Font: 0.695380\n",
      "Train Epoch: 1 [19200/60000 (32%)]\tLoss Char: 1.471353 Loss Font: 0.706520\n",
      "Train Epoch: 1 [38400/60000 (64%)]\tLoss Char: 0.786533 Loss Font: 0.567188\n",
      "Train Epoch: 1 [57600/60000 (96%)]\tLoss Char: 0.389767 Loss Font: 0.473674\n",
      "\n",
      "Test set: Accuracy Char : 8737/10000 (87.37%)\n",
      "          Accuracy Font : 7622/10000 (76.22%)\n",
      "Train Epoch: 2 [0/60000 (0%)]\tLoss Char: 0.326089 Loss Font: 0.448344\n",
      "Train Epoch: 2 [19200/60000 (32%)]\tLoss Char: 0.364758 Loss Font: 0.464343\n",
      "Train Epoch: 2 [38400/60000 (64%)]\tLoss Char: 0.173483 Loss Font: 0.375684\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Train Epoch: 2 [57600/60000 (96%)]\tLoss Char: 0.064808 Loss Font: 0.471571\n",
      "\n",
      "Test set: Accuracy Char : 9487/10000 (94.87%)\n",
      "          Accuracy Font : 8029/10000 (80.29%)\n",
      "Train Epoch: 3 [0/60000 (0%)]\tLoss Char: 0.199115 Loss Font: 0.350405\n",
      "Train Epoch: 3 [19200/60000 (32%)]\tLoss Char: 0.087475 Loss Font: 0.376717\n",
      "Train Epoch: 3 [38400/60000 (64%)]\tLoss Char: 0.030793 Loss Font: 0.376762\n",
      "Train Epoch: 3 [57600/60000 (96%)]\tLoss Char: 0.039212 Loss Font: 0.371999\n",
      "\n",
      "Test set: Accuracy Char : 9701/10000 (97.01%)\n",
      "          Accuracy Font : 8281/10000 (82.81%)\n",
      "Train Epoch: 4 [0/60000 (0%)]\tLoss Char: 0.047427 Loss Font: 0.370547\n",
      "Train Epoch: 4 [19200/60000 (32%)]\tLoss Char: 0.025770 Loss Font: 0.341691\n",
      "Train Epoch: 4 [38400/60000 (64%)]\tLoss Char: 0.084549 Loss Font: 0.419483\n",
      "Train Epoch: 4 [57600/60000 (96%)]\tLoss Char: 0.079968 Loss Font: 0.395533\n",
      "\n",
      "Test set: Accuracy Char : 9766/10000 (97.66%)\n",
      "          Accuracy Font : 8170/10000 (81.70%)\n",
      "Train Epoch: 5 [0/60000 (0%)]\tLoss Char: 0.086189 Loss Font: 0.489581\n",
      "Train Epoch: 5 [19200/60000 (32%)]\tLoss Char: 0.032427 Loss Font: 0.425835\n",
      "Train Epoch: 5 [38400/60000 (64%)]\tLoss Char: 0.025239 Loss Font: 0.461905\n",
      "Train Epoch: 5 [57600/60000 (96%)]\tLoss Char: 0.180446 Loss Font: 0.386244\n",
      "\n",
      "Test set: Accuracy Char : 9794/10000 (97.94%)\n",
      "          Accuracy Font : 8231/10000 (82.31%)\n",
      "Train Epoch: 6 [0/60000 (0%)]\tLoss Char: 0.074522 Loss Font: 0.379091\n",
      "Train Epoch: 6 [19200/60000 (32%)]\tLoss Char: 0.110719 Loss Font: 0.357108\n",
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      "Train Epoch: 6 [57600/60000 (96%)]\tLoss Char: 0.008541 Loss Font: 0.318502\n",
      "\n",
      "Test set: Accuracy Char : 9846/10000 (98.46%)\n",
      "          Accuracy Font : 8357/10000 (83.57%)\n",
      "Train Epoch: 7 [0/60000 (0%)]\tLoss Char: 0.054275 Loss Font: 0.439224\n",
      "Train Epoch: 7 [19200/60000 (32%)]\tLoss Char: 0.033812 Loss Font: 0.381006\n",
      "Train Epoch: 7 [38400/60000 (64%)]\tLoss Char: 0.026883 Loss Font: 0.383303\n",
      "Train Epoch: 7 [57600/60000 (96%)]\tLoss Char: 0.008376 Loss Font: 0.394907\n",
      "\n",
      "Test set: Accuracy Char : 9803/10000 (98.03%)\n",
      "          Accuracy Font : 8103/10000 (81.03%)\n",
      "Train Epoch: 8 [0/60000 (0%)]\tLoss Char: 0.044373 Loss Font: 0.560310\n",
      "Train Epoch: 8 [19200/60000 (32%)]\tLoss Char: 0.009199 Loss Font: 0.213942\n",
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      "Train Epoch: 8 [57600/60000 (96%)]\tLoss Char: 0.035290 Loss Font: 0.382928\n",
      "\n",
      "Test set: Accuracy Char : 9864/10000 (98.64%)\n",
      "          Accuracy Font : 8455/10000 (84.55%)\n",
      "Train Epoch: 9 [0/60000 (0%)]\tLoss Char: 0.007012 Loss Font: 0.403057\n",
      "Train Epoch: 9 [19200/60000 (32%)]\tLoss Char: 0.003225 Loss Font: 0.336029\n",
      "Train Epoch: 9 [38400/60000 (64%)]\tLoss Char: 0.138816 Loss Font: 0.390416\n",
      "Train Epoch: 9 [57600/60000 (96%)]\tLoss Char: 0.010838 Loss Font: 0.291031\n",
      "\n",
      "Test set: Accuracy Char : 9872/10000 (98.72%)\n",
      "          Accuracy Font : 8323/10000 (83.23%)\n",
      "Train Epoch: 10 [0/60000 (0%)]\tLoss Char: 0.002450 Loss Font: 0.459943\n",
      "Train Epoch: 10 [19200/60000 (32%)]\tLoss Char: 0.003459 Loss Font: 0.341524\n",
      "Train Epoch: 10 [38400/60000 (64%)]\tLoss Char: 0.009433 Loss Font: 0.414954\n",
      "Train Epoch: 10 [57600/60000 (96%)]\tLoss Char: 0.027740 Loss Font: 0.364738\n",
      "\n",
      "Test set: Accuracy Char : 9882/10000 (98.82%)\n",
      "          Accuracy Font : 8340/10000 (83.40%)\n",
      "\n",
      " ----- 5\n",
      "Training set 60000 items\n",
      "Testing set  10000 items\n",
      "Train Epoch: 1 [0/60000 (0%)]\tLoss Char: 2.324572 Loss Font: 0.692399\n",
      "Train Epoch: 1 [19200/60000 (32%)]\tLoss Char: 2.013920 Loss Font: 0.678493\n",
      "Train Epoch: 1 [38400/60000 (64%)]\tLoss Char: 0.776882 Loss Font: 0.655305\n",
      "Train Epoch: 1 [57600/60000 (96%)]\tLoss Char: 0.379477 Loss Font: 0.599863\n",
      "\n",
      "Test set: Accuracy Char : 8849/10000 (88.49%)\n",
      "          Accuracy Font : 7008/10000 (70.08%)\n",
      "Train Epoch: 2 [0/60000 (0%)]\tLoss Char: 0.326019 Loss Font: 0.553354\n",
      "Train Epoch: 2 [19200/60000 (32%)]\tLoss Char: 0.345011 Loss Font: 0.537116\n",
      "Train Epoch: 2 [38400/60000 (64%)]\tLoss Char: 0.112558 Loss Font: 0.390343\n",
      "Train Epoch: 2 [57600/60000 (96%)]\tLoss Char: 0.061734 Loss Font: 0.382606\n",
      "\n",
      "Test set: Accuracy Char : 9613/10000 (96.13%)\n",
      "          Accuracy Font : 7622/10000 (76.22%)\n",
      "Train Epoch: 3 [0/60000 (0%)]\tLoss Char: 0.125707 Loss Font: 0.429478\n",
      "Train Epoch: 3 [19200/60000 (32%)]\tLoss Char: 0.135326 Loss Font: 0.474123\n",
      "Train Epoch: 3 [38400/60000 (64%)]\tLoss Char: 0.035374 Loss Font: 0.514663\n",
      "Train Epoch: 3 [57600/60000 (96%)]\tLoss Char: 0.024391 Loss Font: 0.335117\n",
      "\n",
      "Test set: Accuracy Char : 9743/10000 (97.43%)\n",
      "          Accuracy Font : 7917/10000 (79.17%)\n",
      "Train Epoch: 4 [0/60000 (0%)]\tLoss Char: 0.053054 Loss Font: 0.433659\n",
      "Train Epoch: 4 [19200/60000 (32%)]\tLoss Char: 0.089861 Loss Font: 0.399806\n",
      "Train Epoch: 4 [38400/60000 (64%)]\tLoss Char: 0.018792 Loss Font: 0.445457\n",
      "Train Epoch: 4 [57600/60000 (96%)]\tLoss Char: 0.046401 Loss Font: 0.382148\n",
      "\n",
      "Test set: Accuracy Char : 9717/10000 (97.17%)\n",
      "          Accuracy Font : 7978/10000 (79.78%)\n",
      "Train Epoch: 5 [0/60000 (0%)]\tLoss Char: 0.103031 Loss Font: 0.385976\n",
      "Train Epoch: 5 [19200/60000 (32%)]\tLoss Char: 0.027604 Loss Font: 0.449220\n",
      "Train Epoch: 5 [38400/60000 (64%)]\tLoss Char: 0.051431 Loss Font: 0.471028\n",
      "Train Epoch: 5 [57600/60000 (96%)]\tLoss Char: 0.020656 Loss Font: 0.393254\n",
      "\n",
      "Test set: Accuracy Char : 9811/10000 (98.11%)\n",
      "          Accuracy Font : 8099/10000 (80.99%)\n",
      "Train Epoch: 6 [0/60000 (0%)]\tLoss Char: 0.079814 Loss Font: 0.357961\n",
      "Train Epoch: 6 [19200/60000 (32%)]\tLoss Char: 0.058150 Loss Font: 0.413775\n",
      "Train Epoch: 6 [38400/60000 (64%)]\tLoss Char: 0.067206 Loss Font: 0.405137\n",
      "Train Epoch: 6 [57600/60000 (96%)]\tLoss Char: 0.086620 Loss Font: 0.318789\n",
      "\n",
      "Test set: Accuracy Char : 9821/10000 (98.21%)\n",
      "          Accuracy Font : 8217/10000 (82.17%)\n",
      "Train Epoch: 7 [0/60000 (0%)]\tLoss Char: 0.055099 Loss Font: 0.384839\n",
      "Train Epoch: 7 [19200/60000 (32%)]\tLoss Char: 0.005322 Loss Font: 0.337239\n",
      "Train Epoch: 7 [38400/60000 (64%)]\tLoss Char: 0.014568 Loss Font: 0.402052\n",
      "Train Epoch: 7 [57600/60000 (96%)]\tLoss Char: 0.017093 Loss Font: 0.476667\n",
      "\n",
      "Test set: Accuracy Char : 9825/10000 (98.25%)\n",
      "          Accuracy Font : 8238/10000 (82.38%)\n",
      "Train Epoch: 8 [0/60000 (0%)]\tLoss Char: 0.020558 Loss Font: 0.370989\n",
      "Train Epoch: 8 [19200/60000 (32%)]\tLoss Char: 0.009783 Loss Font: 0.284603\n",
      "Train Epoch: 8 [38400/60000 (64%)]\tLoss Char: 0.008005 Loss Font: 0.368339\n",
      "Train Epoch: 8 [57600/60000 (96%)]\tLoss Char: 0.004707 Loss Font: 0.348173\n",
      "\n",
      "Test set: Accuracy Char : 9842/10000 (98.42%)\n",
      "          Accuracy Font : 8273/10000 (82.73%)\n",
      "Train Epoch: 9 [0/60000 (0%)]\tLoss Char: 0.010900 Loss Font: 0.317467\n",
      "Train Epoch: 9 [19200/60000 (32%)]\tLoss Char: 0.003562 Loss Font: 0.290354\n",
      "Train Epoch: 9 [38400/60000 (64%)]\tLoss Char: 0.044586 Loss Font: 0.337487\n",
      "Train Epoch: 9 [57600/60000 (96%)]\tLoss Char: 0.071872 Loss Font: 0.341640\n",
      "\n",
      "Test set: Accuracy Char : 9828/10000 (98.28%)\n",
      "          Accuracy Font : 8209/10000 (82.09%)\n",
      "Train Epoch: 10 [0/60000 (0%)]\tLoss Char: 0.004978 Loss Font: 0.352005\n",
      "Train Epoch: 10 [19200/60000 (32%)]\tLoss Char: 0.012035 Loss Font: 0.323269\n",
      "Train Epoch: 10 [38400/60000 (64%)]\tLoss Char: 0.002950 Loss Font: 0.455409\n",
      "Train Epoch: 10 [57600/60000 (96%)]\tLoss Char: 0.049982 Loss Font: 0.444391\n",
      "\n",
      "Test set: Accuracy Char : 9860/10000 (98.60%)\n",
      "          Accuracy Font : 8285/10000 (82.85%)\n",
      "\n",
      " ----- 4\n",
      "Training set 60000 items\n",
      "Testing set  10000 items\n",
      "Train Epoch: 1 [0/60000 (0%)]\tLoss Char: 2.293938 Loss Font: 0.688574\n",
      "Train Epoch: 1 [19200/60000 (32%)]\tLoss Char: 1.755823 Loss Font: 0.686796\n",
      "Train Epoch: 1 [38400/60000 (64%)]\tLoss Char: 1.138114 Loss Font: 0.683561\n",
      "Train Epoch: 1 [57600/60000 (96%)]\tLoss Char: 0.936151 Loss Font: 0.607000\n",
      "\n",
      "Test set: Accuracy Char : 7842/10000 (78.42%)\n",
      "          Accuracy Font : 6599/10000 (65.99%)\n",
      "Train Epoch: 2 [0/60000 (0%)]\tLoss Char: 0.668301 Loss Font: 0.634173\n",
      "Train Epoch: 2 [19200/60000 (32%)]\tLoss Char: 0.791918 Loss Font: 0.566063\n",
      "Train Epoch: 2 [38400/60000 (64%)]\tLoss Char: 0.287644 Loss Font: 0.627805\n",
      "Train Epoch: 2 [57600/60000 (96%)]\tLoss Char: 0.163138 Loss Font: 0.638232\n",
      "\n",
      "Test set: Accuracy Char : 9323/10000 (93.23%)\n",
      "          Accuracy Font : 6700/10000 (67.00%)\n",
      "Train Epoch: 3 [0/60000 (0%)]\tLoss Char: 0.244574 Loss Font: 0.527301\n",
      "Train Epoch: 3 [19200/60000 (32%)]\tLoss Char: 0.160248 Loss Font: 0.613909\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Train Epoch: 3 [38400/60000 (64%)]\tLoss Char: 0.063970 Loss Font: 0.568079\n",
      "Train Epoch: 3 [57600/60000 (96%)]\tLoss Char: 0.037935 Loss Font: 0.592763\n",
      "\n",
      "Test set: Accuracy Char : 9608/10000 (96.08%)\n",
      "          Accuracy Font : 6832/10000 (68.32%)\n",
      "Train Epoch: 4 [0/60000 (0%)]\tLoss Char: 0.037823 Loss Font: 0.533153\n",
      "Train Epoch: 4 [19200/60000 (32%)]\tLoss Char: 0.091320 Loss Font: 0.452832\n",
      "Train Epoch: 4 [38400/60000 (64%)]\tLoss Char: 0.033057 Loss Font: 0.551227\n",
      "Train Epoch: 4 [57600/60000 (96%)]\tLoss Char: 0.152801 Loss Font: 0.498049\n",
      "\n",
      "Test set: Accuracy Char : 9666/10000 (96.66%)\n",
      "          Accuracy Font : 7076/10000 (70.76%)\n",
      "Train Epoch: 5 [0/60000 (0%)]\tLoss Char: 0.053476 Loss Font: 0.567648\n",
      "Train Epoch: 5 [19200/60000 (32%)]\tLoss Char: 0.071301 Loss Font: 0.652069\n",
      "Train Epoch: 5 [38400/60000 (64%)]\tLoss Char: 0.039134 Loss Font: 0.511306\n",
      "Train Epoch: 5 [57600/60000 (96%)]\tLoss Char: 0.032307 Loss Font: 0.548362\n",
      "\n",
      "Test set: Accuracy Char : 9719/10000 (97.19%)\n",
      "          Accuracy Font : 7162/10000 (71.62%)\n",
      "Train Epoch: 6 [0/60000 (0%)]\tLoss Char: 0.100749 Loss Font: 0.431077\n",
      "Train Epoch: 6 [19200/60000 (32%)]\tLoss Char: 0.110128 Loss Font: 0.582071\n",
      "Train Epoch: 6 [38400/60000 (64%)]\tLoss Char: 0.079960 Loss Font: 0.531968\n",
      "Train Epoch: 6 [57600/60000 (96%)]\tLoss Char: 0.060287 Loss Font: 0.485837\n",
      "\n",
      "Test set: Accuracy Char : 9742/10000 (97.42%)\n",
      "          Accuracy Font : 7326/10000 (73.26%)\n",
      "Train Epoch: 7 [0/60000 (0%)]\tLoss Char: 0.032897 Loss Font: 0.567091\n",
      "Train Epoch: 7 [19200/60000 (32%)]\tLoss Char: 0.023865 Loss Font: 0.457766\n",
      "Train Epoch: 7 [38400/60000 (64%)]\tLoss Char: 0.014401 Loss Font: 0.528584\n",
      "Train Epoch: 7 [57600/60000 (96%)]\tLoss Char: 0.014788 Loss Font: 0.581030\n",
      "\n",
      "Test set: Accuracy Char : 9773/10000 (97.73%)\n",
      "          Accuracy Font : 7350/10000 (73.50%)\n",
      "Train Epoch: 8 [0/60000 (0%)]\tLoss Char: 0.117302 Loss Font: 0.645384\n",
      "Train Epoch: 8 [19200/60000 (32%)]\tLoss Char: 0.022127 Loss Font: 0.449257\n",
      "Train Epoch: 8 [38400/60000 (64%)]\tLoss Char: 0.134239 Loss Font: 0.533313\n",
      "Train Epoch: 8 [57600/60000 (96%)]\tLoss Char: 0.057697 Loss Font: 0.418475\n",
      "\n",
      "Test set: Accuracy Char : 9811/10000 (98.11%)\n",
      "          Accuracy Font : 7345/10000 (73.45%)\n",
      "Train Epoch: 9 [0/60000 (0%)]\tLoss Char: 0.015206 Loss Font: 0.616775\n",
      "Train Epoch: 9 [19200/60000 (32%)]\tLoss Char: 0.052051 Loss Font: 0.436061\n",
      "Train Epoch: 9 [38400/60000 (64%)]\tLoss Char: 0.189122 Loss Font: 0.485891\n",
      "Train Epoch: 9 [57600/60000 (96%)]\tLoss Char: 0.050213 Loss Font: 0.665032\n",
      "\n",
      "Test set: Accuracy Char : 9779/10000 (97.79%)\n",
      "          Accuracy Font : 7480/10000 (74.80%)\n",
      "Train Epoch: 10 [0/60000 (0%)]\tLoss Char: 0.014273 Loss Font: 0.467224\n",
      "Train Epoch: 10 [19200/60000 (32%)]\tLoss Char: 0.019103 Loss Font: 0.502117\n",
      "Train Epoch: 10 [38400/60000 (64%)]\tLoss Char: 0.015161 Loss Font: 0.565244\n",
      "Train Epoch: 10 [57600/60000 (96%)]\tLoss Char: 0.027077 Loss Font: 0.556537\n",
      "\n",
      "Test set: Accuracy Char : 9805/10000 (98.05%)\n",
      "          Accuracy Font : 7525/10000 (75.25%)\n",
      "\n",
      " ----- 3\n",
      "Training set 60000 items\n",
      "Testing set  10000 items\n",
      "Train Epoch: 1 [0/60000 (0%)]\tLoss Char: 2.332251 Loss Font: 0.694728\n",
      "Train Epoch: 1 [19200/60000 (32%)]\tLoss Char: 1.986715 Loss Font: 0.682920\n",
      "Train Epoch: 1 [38400/60000 (64%)]\tLoss Char: 0.940639 Loss Font: 0.629535\n",
      "Train Epoch: 1 [57600/60000 (96%)]\tLoss Char: 0.713029 Loss Font: 0.562771\n",
      "\n",
      "Test set: Accuracy Char : 8359/10000 (83.59%)\n",
      "          Accuracy Font : 6594/10000 (65.94%)\n",
      "Train Epoch: 2 [0/60000 (0%)]\tLoss Char: 0.498384 Loss Font: 0.576446\n",
      "Train Epoch: 2 [19200/60000 (32%)]\tLoss Char: 0.457246 Loss Font: 0.608838\n",
      "Train Epoch: 2 [38400/60000 (64%)]\tLoss Char: 0.305663 Loss Font: 0.517150\n",
      "Train Epoch: 2 [57600/60000 (96%)]\tLoss Char: 0.195623 Loss Font: 0.591650\n",
      "\n",
      "Test set: Accuracy Char : 9169/10000 (91.69%)\n",
      "          Accuracy Font : 6537/10000 (65.37%)\n",
      "Train Epoch: 3 [0/60000 (0%)]\tLoss Char: 0.284289 Loss Font: 0.581372\n",
      "Train Epoch: 3 [19200/60000 (32%)]\tLoss Char: 0.191139 Loss Font: 0.562119\n",
      "Train Epoch: 3 [38400/60000 (64%)]\tLoss Char: 0.097256 Loss Font: 0.525801\n",
      "Train Epoch: 3 [57600/60000 (96%)]\tLoss Char: 0.089071 Loss Font: 0.565049\n",
      "\n",
      "Test set: Accuracy Char : 9465/10000 (94.65%)\n",
      "          Accuracy Font : 6403/10000 (64.03%)\n",
      "Train Epoch: 4 [0/60000 (0%)]\tLoss Char: 0.235205 Loss Font: 0.583781\n",
      "Train Epoch: 4 [19200/60000 (32%)]\tLoss Char: 0.134771 Loss Font: 0.612304\n",
      "Train Epoch: 4 [38400/60000 (64%)]\tLoss Char: 0.136650 Loss Font: 0.597907\n",
      "Train Epoch: 4 [57600/60000 (96%)]\tLoss Char: 0.163844 Loss Font: 0.618205\n",
      "\n",
      "Test set: Accuracy Char : 9555/10000 (95.55%)\n",
      "          Accuracy Font : 6656/10000 (66.56%)\n",
      "Train Epoch: 5 [0/60000 (0%)]\tLoss Char: 0.172173 Loss Font: 0.633995\n",
      "Train Epoch: 5 [19200/60000 (32%)]\tLoss Char: 0.030482 Loss Font: 0.647178\n",
      "Train Epoch: 5 [38400/60000 (64%)]\tLoss Char: 0.242670 Loss Font: 0.611043\n",
      "Train Epoch: 5 [57600/60000 (96%)]\tLoss Char: 0.119255 Loss Font: 0.653697\n",
      "\n",
      "Test set: Accuracy Char : 9663/10000 (96.63%)\n",
      "          Accuracy Font : 6741/10000 (67.41%)\n",
      "Train Epoch: 6 [0/60000 (0%)]\tLoss Char: 0.122519 Loss Font: 0.616596\n",
      "Train Epoch: 6 [19200/60000 (32%)]\tLoss Char: 0.365467 Loss Font: 0.551952\n",
      "Train Epoch: 6 [38400/60000 (64%)]\tLoss Char: 0.102468 Loss Font: 0.568230\n",
      "Train Epoch: 6 [57600/60000 (96%)]\tLoss Char: 0.040368 Loss Font: 0.591326\n",
      "\n",
      "Test set: Accuracy Char : 9695/10000 (96.95%)\n",
      "          Accuracy Font : 6584/10000 (65.84%)\n",
      "Train Epoch: 7 [0/60000 (0%)]\tLoss Char: 0.068828 Loss Font: 0.628946\n",
      "Train Epoch: 7 [19200/60000 (32%)]\tLoss Char: 0.088130 Loss Font: 0.551365\n",
      "Train Epoch: 7 [38400/60000 (64%)]\tLoss Char: 0.020588 Loss Font: 0.601535\n",
      "Train Epoch: 7 [57600/60000 (96%)]\tLoss Char: 0.076129 Loss Font: 0.478811\n",
      "\n",
      "Test set: Accuracy Char : 9599/10000 (95.99%)\n",
      "          Accuracy Font : 6793/10000 (67.93%)\n",
      "Train Epoch: 8 [0/60000 (0%)]\tLoss Char: 0.102276 Loss Font: 0.610328\n",
      "Train Epoch: 8 [19200/60000 (32%)]\tLoss Char: 0.040337 Loss Font: 0.553492\n",
      "Train Epoch: 8 [38400/60000 (64%)]\tLoss Char: 0.175660 Loss Font: 0.545046\n",
      "Train Epoch: 8 [57600/60000 (96%)]\tLoss Char: 0.024314 Loss Font: 0.632149\n",
      "\n",
      "Test set: Accuracy Char : 9727/10000 (97.27%)\n",
      "          Accuracy Font : 6744/10000 (67.44%)\n",
      "Train Epoch: 9 [0/60000 (0%)]\tLoss Char: 0.021345 Loss Font: 0.602529\n",
      "Train Epoch: 9 [19200/60000 (32%)]\tLoss Char: 0.021692 Loss Font: 0.500533\n",
      "Train Epoch: 9 [38400/60000 (64%)]\tLoss Char: 0.071507 Loss Font: 0.543364\n",
      "Train Epoch: 9 [57600/60000 (96%)]\tLoss Char: 0.067619 Loss Font: 0.592311\n",
      "\n",
      "Test set: Accuracy Char : 9702/10000 (97.02%)\n",
      "          Accuracy Font : 6758/10000 (67.58%)\n",
      "Train Epoch: 10 [0/60000 (0%)]\tLoss Char: 0.028464 Loss Font: 0.515585\n",
      "Train Epoch: 10 [19200/60000 (32%)]\tLoss Char: 0.076834 Loss Font: 0.651846\n",
      "Train Epoch: 10 [38400/60000 (64%)]\tLoss Char: 0.034911 Loss Font: 0.612821\n",
      "Train Epoch: 10 [57600/60000 (96%)]\tLoss Char: 0.180814 Loss Font: 0.633611\n",
      "\n",
      "Test set: Accuracy Char : 9752/10000 (97.52%)\n",
      "          Accuracy Font : 6750/10000 (67.50%)\n",
      "\n",
      " ----- 2\n",
      "Training set 60000 items\n",
      "Testing set  10000 items\n",
      "Train Epoch: 1 [0/60000 (0%)]\tLoss Char: 2.314883 Loss Font: 0.695930\n",
      "Train Epoch: 1 [19200/60000 (32%)]\tLoss Char: 1.905248 Loss Font: 0.674868\n",
      "Train Epoch: 1 [38400/60000 (64%)]\tLoss Char: 1.465344 Loss Font: 0.693732\n",
      "Train Epoch: 1 [57600/60000 (96%)]\tLoss Char: 1.191716 Loss Font: 0.667831\n",
      "\n",
      "Test set: Accuracy Char : 5855/10000 (58.55%)\n",
      "          Accuracy Font : 5808/10000 (58.08%)\n",
      "Train Epoch: 2 [0/60000 (0%)]\tLoss Char: 1.078438 Loss Font: 0.702337\n",
      "Train Epoch: 2 [19200/60000 (32%)]\tLoss Char: 0.917219 Loss Font: 0.666110\n",
      "Train Epoch: 2 [38400/60000 (64%)]\tLoss Char: 0.804634 Loss Font: 0.722090\n",
      "Train Epoch: 2 [57600/60000 (96%)]\tLoss Char: 0.426058 Loss Font: 0.685087\n",
      "\n",
      "Test set: Accuracy Char : 8333/10000 (83.33%)\n",
      "          Accuracy Font : 5667/10000 (56.67%)\n",
      "Train Epoch: 3 [0/60000 (0%)]\tLoss Char: 0.556576 Loss Font: 0.651037\n",
      "Train Epoch: 3 [19200/60000 (32%)]\tLoss Char: 0.598500 Loss Font: 0.677499\n",
      "Train Epoch: 3 [38400/60000 (64%)]\tLoss Char: 0.297315 Loss Font: 0.644149\n",
      "Train Epoch: 3 [57600/60000 (96%)]\tLoss Char: 0.263153 Loss Font: 0.672377\n",
      "\n",
      "Test set: Accuracy Char : 8608/10000 (86.08%)\n",
      "          Accuracy Font : 5924/10000 (59.24%)\n",
      "Train Epoch: 4 [0/60000 (0%)]\tLoss Char: 0.612948 Loss Font: 0.652370\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Train Epoch: 4 [19200/60000 (32%)]\tLoss Char: 0.303404 Loss Font: 0.676760\n",
      "Train Epoch: 4 [38400/60000 (64%)]\tLoss Char: 0.302094 Loss Font: 0.707646\n",
      "Train Epoch: 4 [57600/60000 (96%)]\tLoss Char: 0.245415 Loss Font: 0.690120\n",
      "\n",
      "Test set: Accuracy Char : 8908/10000 (89.08%)\n",
      "          Accuracy Font : 5900/10000 (59.00%)\n",
      "Train Epoch: 5 [0/60000 (0%)]\tLoss Char: 0.432391 Loss Font: 0.658988\n",
      "Train Epoch: 5 [19200/60000 (32%)]\tLoss Char: 0.193429 Loss Font: 0.716881\n",
      "Train Epoch: 5 [38400/60000 (64%)]\tLoss Char: 0.316733 Loss Font: 0.657470\n",
      "Train Epoch: 5 [57600/60000 (96%)]\tLoss Char: 0.178666 Loss Font: 0.698835\n",
      "\n",
      "Test set: Accuracy Char : 9178/10000 (91.78%)\n",
      "          Accuracy Font : 5769/10000 (57.69%)\n",
      "Train Epoch: 6 [0/60000 (0%)]\tLoss Char: 0.242666 Loss Font: 0.629765\n",
      "Train Epoch: 6 [19200/60000 (32%)]\tLoss Char: 0.378894 Loss Font: 0.634451\n",
      "Train Epoch: 6 [38400/60000 (64%)]\tLoss Char: 0.205240 Loss Font: 0.638452\n",
      "Train Epoch: 6 [57600/60000 (96%)]\tLoss Char: 0.307186 Loss Font: 0.649678\n",
      "\n",
      "Test set: Accuracy Char : 9193/10000 (91.93%)\n",
      "          Accuracy Font : 6076/10000 (60.76%)\n",
      "Train Epoch: 7 [0/60000 (0%)]\tLoss Char: 0.230211 Loss Font: 0.632435\n",
      "Train Epoch: 7 [19200/60000 (32%)]\tLoss Char: 0.152301 Loss Font: 0.636326\n",
      "Train Epoch: 7 [38400/60000 (64%)]\tLoss Char: 0.161954 Loss Font: 0.657705\n",
      "Train Epoch: 7 [57600/60000 (96%)]\tLoss Char: 0.242895 Loss Font: 0.599496\n",
      "\n",
      "Test set: Accuracy Char : 9325/10000 (93.25%)\n",
      "          Accuracy Font : 5889/10000 (58.89%)\n",
      "Train Epoch: 8 [0/60000 (0%)]\tLoss Char: 0.169930 Loss Font: 0.698545\n",
      "Train Epoch: 8 [19200/60000 (32%)]\tLoss Char: 0.121050 Loss Font: 0.612841\n",
      "Train Epoch: 8 [38400/60000 (64%)]\tLoss Char: 0.181496 Loss Font: 0.652865\n",
      "Train Epoch: 8 [57600/60000 (96%)]\tLoss Char: 0.149344 Loss Font: 0.655155\n",
      "\n",
      "Test set: Accuracy Char : 9393/10000 (93.93%)\n",
      "          Accuracy Font : 6009/10000 (60.09%)\n",
      "Train Epoch: 9 [0/60000 (0%)]\tLoss Char: 0.178637 Loss Font: 0.630357\n",
      "Train Epoch: 9 [19200/60000 (32%)]\tLoss Char: 0.110443 Loss Font: 0.598318\n",
      "Train Epoch: 9 [38400/60000 (64%)]\tLoss Char: 0.240199 Loss Font: 0.663005\n",
      "Train Epoch: 9 [57600/60000 (96%)]\tLoss Char: 0.230638 Loss Font: 0.665255\n",
      "\n",
      "Test set: Accuracy Char : 9390/10000 (93.90%)\n",
      "          Accuracy Font : 5966/10000 (59.66%)\n",
      "Train Epoch: 10 [0/60000 (0%)]\tLoss Char: 0.161294 Loss Font: 0.646896\n",
      "Train Epoch: 10 [19200/60000 (32%)]\tLoss Char: 0.182916 Loss Font: 0.709929\n",
      "Train Epoch: 10 [38400/60000 (64%)]\tLoss Char: 0.093406 Loss Font: 0.679111\n",
      "Train Epoch: 10 [57600/60000 (96%)]\tLoss Char: 0.183811 Loss Font: 0.701063\n",
      "\n",
      "Test set: Accuracy Char : 9458/10000 (94.58%)\n",
      "          Accuracy Font : 5989/10000 (59.89%)\n",
      "\n",
      " ----- 1\n",
      "Training set 60000 items\n",
      "Testing set  10000 items\n",
      "Train Epoch: 1 [0/60000 (0%)]\tLoss Char: 2.400825 Loss Font: 0.705468\n",
      "Train Epoch: 1 [19200/60000 (32%)]\tLoss Char: 2.170417 Loss Font: 0.691969\n",
      "Train Epoch: 1 [38400/60000 (64%)]\tLoss Char: 1.873309 Loss Font: 0.689242\n",
      "Train Epoch: 1 [57600/60000 (96%)]\tLoss Char: 1.680684 Loss Font: 0.658145\n",
      "\n",
      "Test set: Accuracy Char : 2854/10000 (28.54%)\n",
      "          Accuracy Font : 5546/10000 (55.46%)\n",
      "Train Epoch: 2 [0/60000 (0%)]\tLoss Char: 1.774535 Loss Font: 0.697572\n",
      "Train Epoch: 2 [19200/60000 (32%)]\tLoss Char: 1.626880 Loss Font: 0.673818\n",
      "Train Epoch: 2 [38400/60000 (64%)]\tLoss Char: 1.571951 Loss Font: 0.676833\n",
      "Train Epoch: 2 [57600/60000 (96%)]\tLoss Char: 1.521068 Loss Font: 0.699499\n",
      "\n",
      "Test set: Accuracy Char : 3339/10000 (33.39%)\n",
      "          Accuracy Font : 5535/10000 (55.35%)\n",
      "Train Epoch: 3 [0/60000 (0%)]\tLoss Char: 1.451632 Loss Font: 0.697082\n",
      "Train Epoch: 3 [19200/60000 (32%)]\tLoss Char: 1.481169 Loss Font: 0.616733\n",
      "Train Epoch: 3 [38400/60000 (64%)]\tLoss Char: 1.460016 Loss Font: 0.654579\n",
      "Train Epoch: 3 [57600/60000 (96%)]\tLoss Char: 1.350314 Loss Font: 0.689691\n",
      "\n",
      "Test set: Accuracy Char : 3406/10000 (34.06%)\n",
      "          Accuracy Font : 5583/10000 (55.83%)\n",
      "Train Epoch: 4 [0/60000 (0%)]\tLoss Char: 1.426913 Loss Font: 0.669985\n",
      "Train Epoch: 4 [19200/60000 (32%)]\tLoss Char: 1.419763 Loss Font: 0.676764\n",
      "Train Epoch: 4 [38400/60000 (64%)]\tLoss Char: 1.440026 Loss Font: 0.699449\n",
      "Train Epoch: 4 [57600/60000 (96%)]\tLoss Char: 1.589230 Loss Font: 0.703154\n",
      "\n",
      "Test set: Accuracy Char : 3383/10000 (33.83%)\n",
      "          Accuracy Font : 5438/10000 (54.38%)\n",
      "Train Epoch: 5 [0/60000 (0%)]\tLoss Char: 1.440495 Loss Font: 0.679984\n",
      "Train Epoch: 5 [19200/60000 (32%)]\tLoss Char: 1.349621 Loss Font: 0.628319\n",
      "Train Epoch: 5 [38400/60000 (64%)]\tLoss Char: 1.448158 Loss Font: 0.697597\n",
      "Train Epoch: 5 [57600/60000 (96%)]\tLoss Char: 1.205055 Loss Font: 0.649731\n",
      "\n",
      "Test set: Accuracy Char : 4064/10000 (40.64%)\n",
      "          Accuracy Font : 5679/10000 (56.79%)\n",
      "Train Epoch: 6 [0/60000 (0%)]\tLoss Char: 1.454705 Loss Font: 0.673481\n",
      "Train Epoch: 6 [19200/60000 (32%)]\tLoss Char: 1.336531 Loss Font: 0.632644\n",
      "Train Epoch: 6 [38400/60000 (64%)]\tLoss Char: 1.318627 Loss Font: 0.653795\n",
      "Train Epoch: 6 [57600/60000 (96%)]\tLoss Char: 1.222340 Loss Font: 0.657405\n",
      "\n",
      "Test set: Accuracy Char : 3848/10000 (38.48%)\n",
      "          Accuracy Font : 5793/10000 (57.93%)\n",
      "Train Epoch: 7 [0/60000 (0%)]\tLoss Char: 1.444575 Loss Font: 0.615134\n",
      "Train Epoch: 7 [19200/60000 (32%)]\tLoss Char: 1.155037 Loss Font: 0.704312\n",
      "Train Epoch: 7 [38400/60000 (64%)]\tLoss Char: 1.312043 Loss Font: 0.626737\n",
      "Train Epoch: 7 [57600/60000 (96%)]\tLoss Char: 1.189451 Loss Font: 0.638114\n",
      "\n",
      "Test set: Accuracy Char : 4113/10000 (41.13%)\n",
      "          Accuracy Font : 5629/10000 (56.29%)\n",
      "Train Epoch: 8 [0/60000 (0%)]\tLoss Char: 1.253834 Loss Font: 0.661920\n",
      "Train Epoch: 8 [19200/60000 (32%)]\tLoss Char: 1.340438 Loss Font: 0.667480\n",
      "Train Epoch: 8 [38400/60000 (64%)]\tLoss Char: 1.180417 Loss Font: 0.674429\n",
      "Train Epoch: 8 [57600/60000 (96%)]\tLoss Char: 1.101985 Loss Font: 0.660457\n",
      "\n",
      "Test set: Accuracy Char : 4370/10000 (43.70%)\n",
      "          Accuracy Font : 5448/10000 (54.48%)\n",
      "Train Epoch: 9 [0/60000 (0%)]\tLoss Char: 1.216281 Loss Font: 0.675646\n",
      "Train Epoch: 9 [19200/60000 (32%)]\tLoss Char: 1.155544 Loss Font: 0.624179\n",
      "Train Epoch: 9 [38400/60000 (64%)]\tLoss Char: 1.258371 Loss Font: 0.671447\n",
      "Train Epoch: 9 [57600/60000 (96%)]\tLoss Char: 1.174797 Loss Font: 0.666164\n",
      "\n",
      "Test set: Accuracy Char : 4793/10000 (47.93%)\n",
      "          Accuracy Font : 5777/10000 (57.77%)\n",
      "Train Epoch: 10 [0/60000 (0%)]\tLoss Char: 1.162219 Loss Font: 0.703149\n",
      "Train Epoch: 10 [19200/60000 (32%)]\tLoss Char: 1.123342 Loss Font: 0.674131\n",
      "Train Epoch: 10 [38400/60000 (64%)]\tLoss Char: 1.053084 Loss Font: 0.662176\n",
      "Train Epoch: 10 [57600/60000 (96%)]\tLoss Char: 1.191926 Loss Font: 0.706531\n",
      "\n",
      "Test set: Accuracy Char : 4807/10000 (48.07%)\n",
      "          Accuracy Font : 5770/10000 (57.70%)\n"
     ]
    }
   ],
   "source": [
    "cnn_results = {}\n",
    "for PRIVATE_OUTPUT_SIZE in [10, 9, 8, 7, 6, 5, 4, 3, 2, 1]:\n",
    "    print('\\n -----', PRIVATE_OUTPUT_SIZE)\n",
    "    model = CollateralNet()\n",
    "    test_perfs_char_perturbate, test_perfs_font_perturbate = dual_learning_phase(model)\n",
    "    cnn_results[f\"Main task with output size {PRIVATE_OUTPUT_SIZE}\"] = test_perfs_char_perturbate\n",
    "    cnn_results[f\"Collateral task withoutput size {PRIVATE_OUTPUT_SIZE}\"] = test_perfs_font_perturbate"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 720x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "main_results = {k:v for k, v in cnn_results.items() if 'Main' in k}\n",
    "show_results(main_results, title=\"Main accuracy depending of the private output size\", ymin=87)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 46,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 720x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "collateral_results = {k:v for k, v in cnn_results.items() if 'Collateral' in k}\n",
    "show_results(collateral_results, title=\"Collateral accuracy depending of the private output size\", ymax=90)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Down to output_size 4, we observe a similar decrease in accuracy down to 74%. However, for output size 2 & 3, the collateral CNN fails at learning anything, because the output is definitely too small compared to the learning rate. We could reduce the learning rate to have those results, but what we have already is convincing: we will keep an output size between 3 and 4 depending on our needs."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 47,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 504x504 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "draw_pareto(main_results, collateral_results)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Conclusion\n",
    "\n",
    "What we observe here is that the private output size is a key factor to resist against collateral models. \n",
    "\n",
    "One interesting to notice also is that in Functional Encrytion, the execution time scales almost linearly with the the output size, so having a output size of 4 compared to 10 is great benefit!\n",
    "\n",
    "In Part 4 bis you can find a more complete execution to produce reliable figures. There's nothing new except more computations and figures."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.7.0"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}