nb_ex9_2_pretrained_method.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# 예제 9-2 미리 학습한 모델을 사용하는 방법"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "The autoreload extension is already loaded. To reload it, use:\n",
      "  %reload_ext autoreload\n"
     ]
    }
   ],
   "source": [
    "# %load /home/sjkim/.jupyter/head.py\n",
    "%matplotlib inline\n",
    "%load_ext autoreload \n",
    "%autoreload 2\n",
    "from importlib import reload\n",
    "\n",
    "import matplotlib.pyplot as plt\n",
    "import numpy as np\n",
    "\n",
    "import pandas as pd\n",
    "import os\n",
    "#os.environ[\"CUDA_VISIBLE_DEVICES\"]=\"0\"\n",
    "\n",
    "# seaborn\n",
    "#import seaborn as sns\n",
    "#sns.set( style = 'white', font_scale = 1.7)\n",
    "#sns.set_style('ticks')\n",
    "#plt.rcParams['savefig.dpi'] = 200\n",
    "\n",
    "# font for matplotlib\n",
    "#import matplotlib\n",
    "#import matplotlib.font_manager as fm\n",
    "#fm.get_fontconfig_fonts()\n",
    "#font_location = '/usr/share/fonts/truetype/nanum/NanumGothicBold.ttf'\n",
    "#font_name = fm.FontProperties(fname=font_location).get_name()\n",
    "#matplotlib.rc('font', family=font_name)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Using TensorFlow backend.\n"
     ]
    }
   ],
   "source": [
    "import ex9_2_pretarined_method as example"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(800, 32, 32, 3) (800, 1)\n",
      "X_train shape: (800, 32, 32, 3)\n",
      "800 train samples\n",
      "200 test samples\n",
      "(800, 32, 32, 3)\n",
      "Epoch 1/100\n",
      "10/10 [==============================] - 1s - loss: 13.1209 - acc: 0.0889 - val_loss: 11.5896 - val_acc: 0.0950\n",
      "Epoch 2/100\n",
      "10/10 [==============================] - 0s - loss: 12.1266 - acc: 0.1315 - val_loss: 10.3677 - val_acc: 0.1600\n",
      "Epoch 3/100\n",
      "10/10 [==============================] - 0s - loss: 11.4561 - acc: 0.1547 - val_loss: 9.8880 - val_acc: 0.1950\n",
      "Epoch 4/100\n",
      "10/10 [==============================] - 0s - loss: 10.7811 - acc: 0.1833 - val_loss: 8.8622 - val_acc: 0.2050\n",
      "Epoch 5/100\n",
      "10/10 [==============================] - 0s - loss: 10.1624 - acc: 0.2161 - val_loss: 8.1952 - val_acc: 0.2550\n",
      "Epoch 6/100\n",
      "10/10 [==============================] - 0s - loss: 9.9038 - acc: 0.2226 - val_loss: 7.6528 - val_acc: 0.2950\n",
      "Epoch 7/100\n",
      "10/10 [==============================] - 0s - loss: 8.7929 - acc: 0.2752 - val_loss: 7.2329 - val_acc: 0.3300\n",
      "Epoch 8/100\n",
      "10/10 [==============================] - 0s - loss: 8.4259 - acc: 0.3001 - val_loss: 6.7457 - val_acc: 0.3450\n",
      "Epoch 9/100\n",
      "10/10 [==============================] - 0s - loss: 7.9649 - acc: 0.3224 - val_loss: 6.2460 - val_acc: 0.3800\n",
      "Epoch 10/100\n",
      "10/10 [==============================] - 0s - loss: 7.7023 - acc: 0.3302 - val_loss: 6.0524 - val_acc: 0.4250\n",
      "Epoch 11/100\n",
      "10/10 [==============================] - 0s - loss: 6.9466 - acc: 0.3666 - val_loss: 5.8366 - val_acc: 0.4250\n",
      "Epoch 12/100\n",
      "10/10 [==============================] - 0s - loss: 7.1256 - acc: 0.3331 - val_loss: 5.7109 - val_acc: 0.4250\n",
      "Epoch 13/100\n",
      "10/10 [==============================] - 0s - loss: 6.1787 - acc: 0.4057 - val_loss: 5.6455 - val_acc: 0.4300\n",
      "Epoch 14/100\n",
      "10/10 [==============================] - 0s - loss: 6.1382 - acc: 0.4386 - val_loss: 5.3549 - val_acc: 0.4350\n",
      "Epoch 15/100\n",
      "10/10 [==============================] - 0s - loss: 5.9543 - acc: 0.4277 - val_loss: 5.2512 - val_acc: 0.4350\n",
      "Epoch 16/100\n",
      "10/10 [==============================] - 0s - loss: 5.2100 - acc: 0.4673 - val_loss: 5.0852 - val_acc: 0.4550\n",
      "Epoch 17/100\n",
      "10/10 [==============================] - 0s - loss: 5.3330 - acc: 0.4443 - val_loss: 5.0204 - val_acc: 0.4550\n",
      "Epoch 18/100\n",
      "10/10 [==============================] - 0s - loss: 4.9509 - acc: 0.4713 - val_loss: 4.9465 - val_acc: 0.4450\n",
      "Epoch 19/100\n",
      "10/10 [==============================] - 0s - loss: 4.7398 - acc: 0.4987 - val_loss: 4.8894 - val_acc: 0.4400\n",
      "Epoch 20/100\n",
      "10/10 [==============================] - 0s - loss: 4.6859 - acc: 0.5102 - val_loss: 4.8214 - val_acc: 0.4450\n",
      "Epoch 21/100\n",
      "10/10 [==============================] - 0s - loss: 4.5677 - acc: 0.5058 - val_loss: 4.8035 - val_acc: 0.4500\n",
      "Epoch 22/100\n",
      "10/10 [==============================] - 0s - loss: 3.9868 - acc: 0.5372 - val_loss: 4.7284 - val_acc: 0.4500\n",
      "Epoch 23/100\n",
      "10/10 [==============================] - 0s - loss: 4.0218 - acc: 0.5456 - val_loss: 4.7045 - val_acc: 0.4500\n",
      "Epoch 24/100\n",
      "10/10 [==============================] - 0s - loss: 3.7893 - acc: 0.5376 - val_loss: 4.6348 - val_acc: 0.4500\n",
      "Epoch 25/100\n",
      "10/10 [==============================] - 0s - loss: 3.8261 - acc: 0.5455 - val_loss: 4.6014 - val_acc: 0.4600\n",
      "Epoch 26/100\n",
      "10/10 [==============================] - 0s - loss: 3.4687 - acc: 0.5814 - val_loss: 4.5546 - val_acc: 0.4600\n",
      "Epoch 27/100\n",
      "10/10 [==============================] - 0s - loss: 3.2467 - acc: 0.5751 - val_loss: 4.4888 - val_acc: 0.4550\n",
      "Epoch 28/100\n",
      "10/10 [==============================] - 0s - loss: 3.2480 - acc: 0.5888 - val_loss: 4.4371 - val_acc: 0.4650\n",
      "Epoch 29/100\n",
      "10/10 [==============================] - 0s - loss: 3.1168 - acc: 0.5902 - val_loss: 4.3219 - val_acc: 0.4600\n",
      "Epoch 30/100\n",
      "10/10 [==============================] - 0s - loss: 2.8626 - acc: 0.6082 - val_loss: 4.3235 - val_acc: 0.4600\n",
      "Epoch 31/100\n",
      "10/10 [==============================] - 0s - loss: 2.8111 - acc: 0.5983 - val_loss: 4.2617 - val_acc: 0.4550\n",
      "Epoch 32/100\n",
      "10/10 [==============================] - 0s - loss: 2.8982 - acc: 0.6158 - val_loss: 4.1624 - val_acc: 0.4600\n",
      "Epoch 33/100\n",
      "10/10 [==============================] - 0s - loss: 2.8361 - acc: 0.6131 - val_loss: 4.1252 - val_acc: 0.4600\n",
      "Epoch 34/100\n",
      "10/10 [==============================] - 0s - loss: 2.3051 - acc: 0.6454 - val_loss: 4.0928 - val_acc: 0.4650\n",
      "Epoch 35/100\n",
      "10/10 [==============================] - 0s - loss: 2.4088 - acc: 0.6314 - val_loss: 4.0297 - val_acc: 0.4700\n",
      "Epoch 36/100\n",
      "10/10 [==============================] - 0s - loss: 2.5244 - acc: 0.6169 - val_loss: 3.9978 - val_acc: 0.4700\n",
      "Epoch 37/100\n",
      "10/10 [==============================] - 0s - loss: 2.3503 - acc: 0.6473 - val_loss: 3.9818 - val_acc: 0.4750\n",
      "Epoch 38/100\n",
      "10/10 [==============================] - 0s - loss: 2.2483 - acc: 0.6417 - val_loss: 3.9343 - val_acc: 0.4700\n",
      "Epoch 39/100\n",
      "10/10 [==============================] - 0s - loss: 2.2594 - acc: 0.6376 - val_loss: 3.8927 - val_acc: 0.4650\n",
      "Epoch 40/100\n",
      "10/10 [==============================] - 0s - loss: 2.3094 - acc: 0.6528 - val_loss: 3.8362 - val_acc: 0.4750\n",
      "Epoch 41/100\n",
      "10/10 [==============================] - 0s - loss: 1.9502 - acc: 0.6733 - val_loss: 3.7935 - val_acc: 0.4700\n",
      "Epoch 42/100\n",
      "10/10 [==============================] - 0s - loss: 1.9654 - acc: 0.6822 - val_loss: 3.7663 - val_acc: 0.4700\n",
      "Epoch 43/100\n",
      "10/10 [==============================] - 0s - loss: 2.0017 - acc: 0.6674 - val_loss: 3.7325 - val_acc: 0.4800\n",
      "Epoch 44/100\n",
      "10/10 [==============================] - 0s - loss: 1.7686 - acc: 0.7092 - val_loss: 3.7122 - val_acc: 0.4700\n",
      "Epoch 45/100\n",
      "10/10 [==============================] - 0s - loss: 1.7232 - acc: 0.7098 - val_loss: 3.6783 - val_acc: 0.4800\n",
      "Epoch 46/100\n",
      "10/10 [==============================] - 0s - loss: 1.5886 - acc: 0.6985 - val_loss: 3.6796 - val_acc: 0.4800\n",
      "Epoch 47/100\n",
      "10/10 [==============================] - 0s - loss: 1.6716 - acc: 0.7098 - val_loss: 3.6340 - val_acc: 0.4800\n",
      "Epoch 48/100\n",
      "10/10 [==============================] - 0s - loss: 1.5614 - acc: 0.7088 - val_loss: 3.6302 - val_acc: 0.4800\n",
      "Epoch 49/100\n",
      "10/10 [==============================] - 0s - loss: 1.7158 - acc: 0.6898 - val_loss: 3.5794 - val_acc: 0.4850\n",
      "Epoch 50/100\n",
      "10/10 [==============================] - 0s - loss: 1.5347 - acc: 0.7132 - val_loss: 3.5673 - val_acc: 0.4850\n",
      "Epoch 51/100\n",
      "10/10 [==============================] - 0s - loss: 1.3397 - acc: 0.7388 - val_loss: 3.5157 - val_acc: 0.4900\n",
      "Epoch 52/100\n",
      "10/10 [==============================] - 0s - loss: 1.2939 - acc: 0.7304 - val_loss: 3.4827 - val_acc: 0.4900\n",
      "Epoch 53/100\n",
      "10/10 [==============================] - 0s - loss: 1.4042 - acc: 0.7340 - val_loss: 3.4525 - val_acc: 0.4900\n",
      "Epoch 54/100\n",
      "10/10 [==============================] - 0s - loss: 1.1983 - acc: 0.7632 - val_loss: 3.4584 - val_acc: 0.4850\n",
      "Epoch 55/100\n",
      "10/10 [==============================] - 0s - loss: 1.2286 - acc: 0.7408 - val_loss: 3.4501 - val_acc: 0.4800\n",
      "Epoch 56/100\n",
      "10/10 [==============================] - 0s - loss: 1.0283 - acc: 0.7773 - val_loss: 3.4218 - val_acc: 0.4850\n",
      "Epoch 57/100\n",
      "10/10 [==============================] - 0s - loss: 1.1750 - acc: 0.7641 - val_loss: 3.4164 - val_acc: 0.4700\n",
      "Epoch 58/100\n",
      "10/10 [==============================] - 0s - loss: 1.1204 - acc: 0.7668 - val_loss: 3.4018 - val_acc: 0.4700\n",
      "Epoch 59/100\n",
      "10/10 [==============================] - 0s - loss: 0.9608 - acc: 0.7659 - val_loss: 3.3846 - val_acc: 0.5000\n",
      "Epoch 60/100\n",
      "10/10 [==============================] - 0s - loss: 1.0326 - acc: 0.7790 - val_loss: 3.3539 - val_acc: 0.4900\n",
      "Epoch 61/100\n",
      "10/10 [==============================] - 0s - loss: 1.0400 - acc: 0.7800 - val_loss: 3.3332 - val_acc: 0.4800\n",
      "Epoch 62/100\n",
      "10/10 [==============================] - 0s - loss: 0.8976 - acc: 0.7918 - val_loss: 3.3442 - val_acc: 0.4750\n",
      "Epoch 63/100\n",
      "10/10 [==============================] - 0s - loss: 0.8903 - acc: 0.7896 - val_loss: 3.3334 - val_acc: 0.4800\n",
      "Epoch 64/100\n",
      "10/10 [==============================] - 0s - loss: 0.9482 - acc: 0.7894 - val_loss: 3.3023 - val_acc: 0.4900\n",
      "Epoch 65/100\n",
      "10/10 [==============================] - 0s - loss: 0.6705 - acc: 0.8165 - val_loss: 3.2951 - val_acc: 0.4850\n",
      "Epoch 66/100\n",
      "10/10 [==============================] - 0s - loss: 0.8168 - acc: 0.8128 - val_loss: 3.2965 - val_acc: 0.4850\n",
      "Epoch 67/100\n",
      "10/10 [==============================] - 0s - loss: 0.8467 - acc: 0.8042 - val_loss: 3.2567 - val_acc: 0.4950\n",
      "Epoch 68/100\n",
      "10/10 [==============================] - 0s - loss: 0.6818 - acc: 0.8282 - val_loss: 3.2670 - val_acc: 0.4750\n",
      "Epoch 69/100\n",
      "10/10 [==============================] - 0s - loss: 0.9091 - acc: 0.7879 - val_loss: 3.2648 - val_acc: 0.4900\n",
      "Epoch 70/100\n",
      "10/10 [==============================] - 0s - loss: 0.6497 - acc: 0.8398 - val_loss: 3.2419 - val_acc: 0.4850\n",
      "Epoch 71/100\n",
      "10/10 [==============================] - 0s - loss: 0.7165 - acc: 0.8195 - val_loss: 3.2230 - val_acc: 0.4850\n",
      "Epoch 72/100\n",
      "10/10 [==============================] - 0s - loss: 0.6480 - acc: 0.8349 - val_loss: 3.2151 - val_acc: 0.4800\n",
      "Epoch 73/100\n",
      "10/10 [==============================] - 0s - loss: 0.5979 - acc: 0.8323 - val_loss: 3.2164 - val_acc: 0.4900\n",
      "Epoch 74/100\n",
      "10/10 [==============================] - 0s - loss: 0.5971 - acc: 0.8433 - val_loss: 3.2153 - val_acc: 0.4600\n",
      "Epoch 75/100\n",
      "10/10 [==============================] - 0s - loss: 0.5538 - acc: 0.8418 - val_loss: 3.2177 - val_acc: 0.4850\n",
      "Epoch 76/100\n",
      "10/10 [==============================] - 0s - loss: 0.5999 - acc: 0.8358 - val_loss: 3.2198 - val_acc: 0.4700\n",
      "Epoch 77/100\n",
      "10/10 [==============================] - 0s - loss: 0.5605 - acc: 0.8422 - val_loss: 3.2480 - val_acc: 0.4900\n",
      "Epoch 78/100\n",
      "10/10 [==============================] - 0s - loss: 0.5145 - acc: 0.8638 - val_loss: 3.2627 - val_acc: 0.4900\n",
      "Epoch 79/100\n",
      "10/10 [==============================] - 0s - loss: 0.5333 - acc: 0.8456 - val_loss: 3.2647 - val_acc: 0.4850\n",
      "Epoch 80/100\n",
      "10/10 [==============================] - 0s - loss: 0.4909 - acc: 0.8567 - val_loss: 3.2619 - val_acc: 0.4950\n",
      "Epoch 81/100\n",
      "10/10 [==============================] - 0s - loss: 0.4917 - acc: 0.8663 - val_loss: 3.2388 - val_acc: 0.5000\n",
      "Epoch 82/100\n",
      "10/10 [==============================] - 0s - loss: 0.5272 - acc: 0.8392 - val_loss: 3.2498 - val_acc: 0.5000\n",
      "Epoch 83/100\n",
      "10/10 [==============================] - 0s - loss: 0.4136 - acc: 0.8706 - val_loss: 3.2486 - val_acc: 0.4900\n",
      "Epoch 84/100\n",
      "10/10 [==============================] - 0s - loss: 0.5809 - acc: 0.8302 - val_loss: 3.2475 - val_acc: 0.4850\n",
      "Epoch 85/100\n",
      "10/10 [==============================] - 0s - loss: 0.4741 - acc: 0.8759 - val_loss: 3.2433 - val_acc: 0.5000\n",
      "Epoch 86/100\n",
      "10/10 [==============================] - 0s - loss: 0.4661 - acc: 0.8761 - val_loss: 3.2433 - val_acc: 0.5050\n",
      "Epoch 87/100\n",
      "10/10 [==============================] - 0s - loss: 0.3839 - acc: 0.8885 - val_loss: 3.2371 - val_acc: 0.5050\n",
      "Epoch 88/100\n",
      "10/10 [==============================] - 0s - loss: 0.4196 - acc: 0.8652 - val_loss: 3.2329 - val_acc: 0.5000\n",
      "Epoch 89/100\n",
      "10/10 [==============================] - 0s - loss: 0.4234 - acc: 0.8716 - val_loss: 3.2356 - val_acc: 0.4900\n",
      "Epoch 90/100\n",
      "10/10 [==============================] - 0s - loss: 0.4075 - acc: 0.8863 - val_loss: 3.2170 - val_acc: 0.4950\n",
      "Epoch 91/100\n",
      "10/10 [==============================] - 0s - loss: 0.3557 - acc: 0.8874 - val_loss: 3.2388 - val_acc: 0.5000\n",
      "Epoch 92/100\n",
      "10/10 [==============================] - 0s - loss: 0.3918 - acc: 0.8858 - val_loss: 3.2193 - val_acc: 0.5100\n",
      "Epoch 93/100\n",
      "10/10 [==============================] - 0s - loss: 0.2882 - acc: 0.9147 - val_loss: 3.2299 - val_acc: 0.5000\n",
      "Epoch 94/100\n",
      "10/10 [==============================] - 0s - loss: 0.4053 - acc: 0.8866 - val_loss: 3.2068 - val_acc: 0.5050\n",
      "Epoch 95/100\n",
      "10/10 [==============================] - 0s - loss: 0.3589 - acc: 0.8976 - val_loss: 3.1966 - val_acc: 0.5050\n",
      "Epoch 96/100\n",
      "10/10 [==============================] - 0s - loss: 0.3244 - acc: 0.8904 - val_loss: 3.2181 - val_acc: 0.5050\n",
      "Epoch 97/100\n",
      "10/10 [==============================] - 0s - loss: 0.3607 - acc: 0.8902 - val_loss: 3.2260 - val_acc: 0.5000\n",
      "Epoch 98/100\n",
      "10/10 [==============================] - 0s - loss: 0.3242 - acc: 0.9097 - val_loss: 3.2146 - val_acc: 0.5100\n",
      "Epoch 99/100\n",
      "10/10 [==============================] - 0s - loss: 0.3457 - acc: 0.9094 - val_loss: 3.2360 - val_acc: 0.4950\n",
      "Epoch 100/100\n",
      "10/10 [==============================] - 0s - loss: 0.4023 - acc: 0.8930 - val_loss: 3.2632 - val_acc: 0.5050\n",
      "Confusion matrix\n",
      "[[16  0  0  2  0  0  0  2  3  0]\n",
      " [ 0 11  1  1  0  0  0  0  0  5]\n",
      " [ 2  1  6  1  4  1  2  1  0  0]\n",
      " [ 1  0  0  4  0  4  4  1  0  1]\n",
      " [ 2  0  2  0  7  3  1  4  1  1]\n",
      " [ 0  1  2  5  2  8  1  1  0  1]\n",
      " [ 1  0  3  1  2  0 17  1  0  0]\n",
      " [ 2  2  1  2  2  1  0  6  0  2]\n",
      " [ 0  2  0  3  2  0  0  0 13  0]\n",
      " [ 1  4  0  1  0  1  0  0  1 13]]\n",
      "Test score: 3.26324853897\n",
      "Test accuracy: 0.505\n",
      "Output results are saved in output_2ef16725-3167-4d61-bd9d-8aa79b3e8c72\n"
     ]
    },
    {
     "data": {
      "image/png": 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bkZyezeQfD7N86yW+Hd8BhULBySuxqJQK/IrY10GUL4WhNGvBnmFRUVF07dqV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\nEERERKSz/w9KU/IYwOORYwAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7ff98d21ca58>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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A7k3CfOONN/DEE09g9erV6Ny5s8o2+vTpgwsXLjzQ+2oJTBa0yPUfPQsNCerg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\n1bOEEEII0XadJllITk4mKyurSfmBAwdQFIVevXr5ISohhBCi6+s0ycL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XI8mCEKIJjUbD\nX/7yF7p3784dd9zBzJkziY6OZtmyZQQFBfk7vGYVFxfjdrsbrQ0hhGgf8hhCCNGs0NBQXnjhBXw+\nH0Crl3gGKCwsxOFwNHusuLgYVVU5derUec8PCQkhMDCQ/Pz8887M+G979uxBo9Hgcrk4deoUiqJc\ncGVIIUTryL8iIUSLLiZJOOfuu+8mOzu7xTrXXXfdeY/94Ac/4Oabb2bmzJkX/btvuOGGhr+/9957\nDB48+KKvIYRoTJIFIUS7W7t2bbtc5/Dhw+1yHSFE28iYBSHEJTu3zsG5v0uXvxBdk6K29oGgEEII\nIa5K0rMghBBCiBZJsiCEEEKIFkmyIIQQQogWSbIghBBCiBZJsiCEEEKIFkmyIIQQQogWSbIghBBC\niBb9f5A39eLeudDpAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7ff98c9d0710>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "example.main()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python [default]",
   "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": "3.6.3"
  },
  "toc": {
   "nav_menu": {},
   "number_sections": true,
   "sideBar": true,
   "skip_h1_title": true,
   "toc_cell": false,
   "toc_position": {},
   "toc_section_display": "block",
   "toc_window_display": false
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}