VGG19.ipynb

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      "── \u001b[1mAttaching core tidyverse packages\u001b[22m ──────────────────────── tidyverse 2.0.0 ──\n",
      "\u001b[32m✔\u001b[39m \u001b[34mdplyr    \u001b[39m 1.1.0     \u001b[32m✔\u001b[39m \u001b[34mreadr    \u001b[39m 2.1.4\n",
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      "── \u001b[1mConflicts\u001b[22m ────────────────────────────────────────── tidyverse_conflicts() ──\n",
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      "\u001b[36mℹ\u001b[39m Use the conflicted package (\u001b[3m\u001b[34m<http://conflicted.r-lib.org/>\u001b[39m\u001b[23m) to force all conflicts to become errors\n",
      "Loading required package: lattice\n",
      "\n",
      "\n",
      "Attaching package: ‘caret’\n",
      "\n",
      "\n",
      "The following object is masked from ‘package:purrr’:\n",
      "\n",
      "    lift\n",
      "\n",
      "\n",
      "The following object is masked from ‘package:httr’:\n",
      "\n",
      "    progress\n",
      "\n",
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     "data": {
      "text/html": [
       "<style>\n",
       ".list-inline {list-style: none; margin:0; padding: 0}\n",
       ".list-inline>li {display: inline-block}\n",
       ".list-inline>li:not(:last-child)::after {content: \"\\00b7\"; padding: 0 .5ex}\n",
       "</style>\n",
       "<ol class=list-inline><li>'brain-tumor-classification-mri'</li><li>'keras-pretrained-models'</li></ol>\n"
      ],
      "text/latex": [
       "\\begin{enumerate*}\n",
       "\\item 'brain-tumor-classification-mri'\n",
       "\\item 'keras-pretrained-models'\n",
       "\\end{enumerate*}\n"
      ],
      "text/markdown": [
       "1. 'brain-tumor-classification-mri'\n",
       "2. 'keras-pretrained-models'\n",
       "\n",
       "\n"
      ],
      "text/plain": [
       "[1] \"brain-tumor-classification-mri\" \"keras-pretrained-models\"       "
      ]
     },
     "metadata": {},
     "output_type": "display_data"
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   ],
   "source": [
    "# This R environment comes with many helpful analytics packages installed\n",
    "# It is defined by the kaggle/rstats Docker image: https://github.com/kaggle/docker-rstats\n",
    "# For example, here's a helpful package to load\n",
    "library(keras)\n",
    "library(magrittr)\n",
    "library(reticulate)\n",
    "library(tidyverse) # metapackage of all tidyverse packages\n",
    "library(e1071)\n",
    "library(caTools)\n",
    "library(caret)\n",
    "\n",
    "# Input data files are available in the read-only \"../input/\" directory\n",
    "# For example, running this (by clicking run or pressing Shift+Enter) will list all files under the input directory\n",
    "\n",
    "list.files(path = \"../input\")\n",
    "\n",
    "# You can write up to 20GB to the current directory (/kaggle/working/) that gets preserved as output when you create a version using \"Save & Run All\" \n",
    "# You can also write temporary files to /kaggle/temp/, but they won't be saved outside of the current session"
   ]
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     "status": "completed"
    },
    "tags": []
   },
   "source": [
    "# Data Preparation and Organisation"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "b3629a42",
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   },
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   "source": [
    "set.seed(1212)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "d2a81324",
   "metadata": {
    "execution": {
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    },
    "tags": []
   },
   "outputs": [],
   "source": [
    "train_data_glioma <- file.path(\"/kaggle/input/brain-tumor-classification-mri/Training/glioma_tumor\")\n",
    "train_data_meningioma <- file.path(\"/kaggle/input/brain-tumor-classification-mri/Training/meningioma_tumor\")\n",
    "train_data_pituitary <- file.path(\"/kaggle/input/brain-tumor-classification-mri/Training/pituitary_tumor\")\n",
    "train_data_notmr <- file.path(\"/kaggle/input/brain-tumor-classification-mri/Training/no_tumor\");"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "ae1d4f17",
   "metadata": {
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     "start_time": "2023-04-04T17:16:38.215297",
     "status": "completed"
    },
    "tags": []
   },
   "source": [
    "**Volume of data in each class**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "44e56e75",
   "metadata": {
    "execution": {
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   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[1] \"Glioma ::  826\"\n",
      "[1] \"Meningioma ::  822\"\n",
      "[1] \"No_tumour ::  395\"\n",
      "[1] \"Pituitary ::  827\"\n"
     ]
    }
   ],
   "source": [
    "print(paste(\"Glioma :: \",length(list.files(train_data_glioma))))\n",
    "print(paste(\"Meningioma :: \",length(list.files(train_data_meningioma))))\n",
    "print(paste(\"No_tumour :: \",length(list.files(train_data_notmr))))\n",
    "print(paste(\"Pituitary :: \",length(list.files(train_data_pituitary))))"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "1578b2a6",
   "metadata": {
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     "start_time": "2023-04-04T17:16:38.867421",
     "status": "completed"
    },
    "tags": []
   },
   "source": [
    "**Creating various working directories**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "7ca7fbe9",
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   "outputs": [],
   "source": [
    "train_dir<-'./train';dir.create(train_dir)\n",
    "test_dir<-'./test';dir.create(test_dir)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "0d02449a",
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   "outputs": [],
   "source": [
    "train_dir_glioma<-file.path(train_dir,\"glioma\");dir.create(train_dir_glioma)\n",
    "train_dir_meningioma<-file.path(train_dir,\"meningioma\");dir.create(train_dir_meningioma)\n",
    "train_dir_notmr<-file.path(train_dir,\"notmr\");dir.create(train_dir_notmr)\n",
    "train_dir_pituitary<-file.path(train_dir,\"pituitary\");dir.create(train_dir_pituitary)\n",
    "\n",
    "test_dir_glioma<-file.path(test_dir,\"glioma\");dir.create(test_dir_glioma)\n",
    "test_dir_meningioma<-file.path(test_dir,\"meningioma\");dir.create(test_dir_meningioma)\n",
    "test_dir_notmr<-file.path(test_dir,\"notmr\");dir.create(test_dir_notmr)\n",
    "test_dir_pituitary<-file.path(test_dir,\"pituitary\");dir.create(test_dir_pituitary)"
   ]
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   "id": "a3ca359d",
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       "\\item TRUE\n",
       "\\item TRUE\n",
       "\\item TRUE\n",
       "\\item TRUE\n",
       "\\item TRUE\n",
       "\\item TRUE\n",
       "\\item TRUE\n",
       "\\item TRUE\n",
       "\\item TRUE\n",
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       "\\item TRUE\n",
       "\\item TRUE\n",
       "\\item TRUE\n",
       "\\item TRUE\n",
       "\\item TRUE\n",
       "\\item ⋯\n",
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       "\\item TRUE\n",
       "\\item TRUE\n",
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       "\\item TRUE\n",
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       "\\item TRUE\n",
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       "\\item TRUE\n",
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       "\\item TRUE\n",
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       "\\item TRUE\n",
       "\\item TRUE\n",
       "\\item TRUE\n",
       "\\item TRUE\n",
       "\\item TRUE\n",
       "\\item TRUE\n",
       "\\item TRUE\n",
       "\\item TRUE\n",
       "\\item TRUE\n",
       "\\item TRUE\n",
       "\\item TRUE\n",
       "\\item TRUE\n",
       "\\item TRUE\n",
       "\\item TRUE\n",
       "\\item TRUE\n",
       "\\item TRUE\n",
       "\\item TRUE\n",
       "\\item TRUE\n",
       "\\item TRUE\n",
       "\\item TRUE\n",
       "\\item TRUE\n",
       "\\item TRUE\n",
       "\\item TRUE\n",
       "\\item TRUE\n",
       "\\item TRUE\n",
       "\\item TRUE\n",
       "\\item TRUE\n",
       "\\item TRUE\n",
       "\\item TRUE\n",
       "\\item TRUE\n",
       "\\item TRUE\n",
       "\\item TRUE\n",
       "\\item TRUE\n",
       "\\item TRUE\n",
       "\\item TRUE\n",
       "\\item TRUE\n",
       "\\item TRUE\n",
       "\\item TRUE\n",
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       "\\item TRUE\n",
       "\\item TRUE\n",
       "\\item TRUE\n",
       "\\item TRUE\n",
       "\\item TRUE\n",
       "\\item TRUE\n",
       "\\item TRUE\n",
       "\\item TRUE\n",
       "\\item TRUE\n",
       "\\item TRUE\n",
       "\\item TRUE\n",
       "\\item TRUE\n",
       "\\item TRUE\n",
       "\\item TRUE\n",
       "\\item TRUE\n",
       "\\item TRUE\n",
       "\\item TRUE\n",
       "\\item TRUE\n",
       "\\item TRUE\n",
       "\\item TRUE\n",
       "\\item TRUE\n",
       "\\item TRUE\n",
       "\\item TRUE\n",
       "\\item TRUE\n",
       "\\item TRUE\n",
       "\\item TRUE\n",
       "\\item TRUE\n",
       "\\item TRUE\n",
       "\\item TRUE\n",
       "\\item TRUE\n",
       "\\item TRUE\n",
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       "\\item TRUE\n",
       "\\item TRUE\n",
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       "\\item TRUE\n",
       "\\item TRUE\n",
       "\\item TRUE\n",
       "\\item TRUE\n",
       "\\item TRUE\n",
       "\\item TRUE\n",
       "\\item TRUE\n",
       "\\item TRUE\n",
       "\\item TRUE\n",
       "\\item TRUE\n",
       "\\item TRUE\n",
       "\\item TRUE\n",
       "\\item TRUE\n",
       "\\item TRUE\n",
       "\\item TRUE\n",
       "\\item TRUE\n",
       "\\item TRUE\n",
       "\\item TRUE\n",
       "\\item TRUE\n",
       "\\item TRUE\n",
       "\\item TRUE\n",
       "\\item TRUE\n",
       "\\item TRUE\n",
       "\\end{enumerate*}\n"
      ],
      "text/markdown": [
       "1. TRUE\n",
       "2. TRUE\n",
       "3. TRUE\n",
       "4. TRUE\n",
       "5. TRUE\n",
       "6. TRUE\n",
       "7. TRUE\n",
       "8. TRUE\n",
       "9. TRUE\n",
       "10. TRUE\n",
       "11. TRUE\n",
       "12. TRUE\n",
       "13. TRUE\n",
       "14. TRUE\n",
       "15. TRUE\n",
       "16. TRUE\n",
       "17. TRUE\n",
       "18. TRUE\n",
       "19. TRUE\n",
       "20. TRUE\n",
       "21. TRUE\n",
       "22. TRUE\n",
       "23. TRUE\n",
       "24. TRUE\n",
       "25. TRUE\n",
       "26. TRUE\n",
       "27. TRUE\n",
       "28. TRUE\n",
       "29. TRUE\n",
       "30. TRUE\n",
       "31. TRUE\n",
       "32. TRUE\n",
       "33. TRUE\n",
       "34. TRUE\n",
       "35. TRUE\n",
       "36. TRUE\n",
       "37. TRUE\n",
       "38. TRUE\n",
       "39. TRUE\n",
       "40. TRUE\n",
       "41. TRUE\n",
       "42. TRUE\n",
       "43. TRUE\n",
       "44. TRUE\n",
       "45. TRUE\n",
       "46. TRUE\n",
       "47. TRUE\n",
       "48. TRUE\n",
       "49. TRUE\n",
       "50. TRUE\n",
       "51. TRUE\n",
       "52. TRUE\n",
       "53. TRUE\n",
       "54. TRUE\n",
       "55. TRUE\n",
       "56. TRUE\n",
       "57. TRUE\n",
       "58. TRUE\n",
       "59. TRUE\n",
       "60. TRUE\n",
       "61. TRUE\n",
       "62. TRUE\n",
       "63. TRUE\n",
       "64. TRUE\n",
       "65. TRUE\n",
       "66. TRUE\n",
       "67. TRUE\n",
       "68. TRUE\n",
       "69. TRUE\n",
       "70. TRUE\n",
       "71. TRUE\n",
       "72. TRUE\n",
       "73. TRUE\n",
       "74. TRUE\n",
       "75. TRUE\n",
       "76. TRUE\n",
       "77. TRUE\n",
       "78. TRUE\n",
       "79. TRUE\n",
       "80. TRUE\n",
       "81. TRUE\n",
       "82. TRUE\n",
       "83. TRUE\n",
       "84. TRUE\n",
       "85. TRUE\n",
       "86. TRUE\n",
       "87. TRUE\n",
       "88. TRUE\n",
       "89. TRUE\n",
       "90. TRUE\n",
       "91. TRUE\n",
       "92. TRUE\n",
       "93. TRUE\n",
       "94. TRUE\n",
       "95. TRUE\n",
       "96. TRUE\n",
       "97. TRUE\n",
       "98. TRUE\n",
       "99. TRUE\n",
       "100. TRUE\n",
       "101. TRUE\n",
       "102. TRUE\n",
       "103. TRUE\n",
       "104. TRUE\n",
       "105. TRUE\n",
       "106. TRUE\n",
       "107. TRUE\n",
       "108. TRUE\n",
       "109. TRUE\n",
       "110. TRUE\n",
       "111. TRUE\n",
       "112. TRUE\n",
       "113. TRUE\n",
       "114. TRUE\n",
       "115. TRUE\n",
       "116. TRUE\n",
       "117. TRUE\n",
       "118. TRUE\n",
       "119. TRUE\n",
       "120. TRUE\n",
       "121. TRUE\n",
       "122. TRUE\n",
       "123. TRUE\n",
       "124. TRUE\n",
       "125. TRUE\n",
       "126. TRUE\n",
       "127. TRUE\n",
       "128. TRUE\n",
       "129. TRUE\n",
       "130. TRUE\n",
       "131. TRUE\n",
       "132. TRUE\n",
       "133. TRUE\n",
       "134. TRUE\n",
       "135. TRUE\n",
       "136. TRUE\n",
       "137. TRUE\n",
       "138. TRUE\n",
       "139. TRUE\n",
       "140. TRUE\n",
       "141. TRUE\n",
       "142. TRUE\n",
       "143. TRUE\n",
       "144. TRUE\n",
       "145. TRUE\n",
       "146. TRUE\n",
       "147. TRUE\n",
       "148. TRUE\n",
       "149. TRUE\n",
       "150. TRUE\n",
       "151. TRUE\n",
       "152. TRUE\n",
       "153. TRUE\n",
       "154. TRUE\n",
       "155. TRUE\n",
       "156. TRUE\n",
       "157. TRUE\n",
       "158. TRUE\n",
       "159. TRUE\n",
       "160. TRUE\n",
       "161. TRUE\n",
       "162. TRUE\n",
       "163. TRUE\n",
       "164. TRUE\n",
       "165. TRUE\n",
       "166. TRUE\n",
       "167. TRUE\n",
       "168. TRUE\n",
       "169. TRUE\n",
       "170. TRUE\n",
       "171. TRUE\n",
       "172. TRUE\n",
       "173. TRUE\n",
       "174. TRUE\n",
       "175. TRUE\n",
       "176. TRUE\n",
       "177. TRUE\n",
       "178. TRUE\n",
       "179. TRUE\n",
       "180. TRUE\n",
       "181. TRUE\n",
       "182. TRUE\n",
       "183. TRUE\n",
       "184. TRUE\n",
       "185. TRUE\n",
       "186. TRUE\n",
       "187. TRUE\n",
       "188. TRUE\n",
       "189. TRUE\n",
       "190. TRUE\n",
       "191. TRUE\n",
       "192. TRUE\n",
       "193. TRUE\n",
       "194. TRUE\n",
       "195. TRUE\n",
       "196. TRUE\n",
       "197. TRUE\n",
       "198. TRUE\n",
       "199. TRUE\n",
       "200. TRUE\n",
       "201. ⋯\n",
       "202. TRUE\n",
       "203. TRUE\n",
       "204. TRUE\n",
       "205. TRUE\n",
       "206. TRUE\n",
       "207. TRUE\n",
       "208. TRUE\n",
       "209. TRUE\n",
       "210. TRUE\n",
       "211. TRUE\n",
       "212. TRUE\n",
       "213. TRUE\n",
       "214. TRUE\n",
       "215. TRUE\n",
       "216. TRUE\n",
       "217. TRUE\n",
       "218. TRUE\n",
       "219. TRUE\n",
       "220. TRUE\n",
       "221. TRUE\n",
       "222. TRUE\n",
       "223. TRUE\n",
       "224. TRUE\n",
       "225. TRUE\n",
       "226. TRUE\n",
       "227. TRUE\n",
       "228. TRUE\n",
       "229. TRUE\n",
       "230. TRUE\n",
       "231. TRUE\n",
       "232. TRUE\n",
       "233. TRUE\n",
       "234. TRUE\n",
       "235. TRUE\n",
       "236. TRUE\n",
       "237. TRUE\n",
       "238. TRUE\n",
       "239. TRUE\n",
       "240. TRUE\n",
       "241. TRUE\n",
       "242. TRUE\n",
       "243. TRUE\n",
       "244. TRUE\n",
       "245. TRUE\n",
       "246. TRUE\n",
       "247. TRUE\n",
       "248. TRUE\n",
       "249. TRUE\n",
       "250. TRUE\n",
       "251. TRUE\n",
       "252. TRUE\n",
       "253. TRUE\n",
       "254. TRUE\n",
       "255. TRUE\n",
       "256. TRUE\n",
       "257. TRUE\n",
       "258. TRUE\n",
       "259. TRUE\n",
       "260. TRUE\n",
       "261. TRUE\n",
       "262. TRUE\n",
       "263. TRUE\n",
       "264. TRUE\n",
       "265. TRUE\n",
       "266. TRUE\n",
       "267. TRUE\n",
       "268. TRUE\n",
       "269. TRUE\n",
       "270. TRUE\n",
       "271. TRUE\n",
       "272. TRUE\n",
       "273. TRUE\n",
       "274. TRUE\n",
       "275. TRUE\n",
       "276. TRUE\n",
       "277. TRUE\n",
       "278. TRUE\n",
       "279. TRUE\n",
       "280. TRUE\n",
       "281. TRUE\n",
       "282. TRUE\n",
       "283. TRUE\n",
       "284. TRUE\n",
       "285. TRUE\n",
       "286. TRUE\n",
       "287. TRUE\n",
       "288. TRUE\n",
       "289. TRUE\n",
       "290. TRUE\n",
       "291. TRUE\n",
       "292. TRUE\n",
       "293. TRUE\n",
       "294. TRUE\n",
       "295. TRUE\n",
       "296. TRUE\n",
       "297. TRUE\n",
       "298. TRUE\n",
       "299. TRUE\n",
       "300. TRUE\n",
       "301. TRUE\n",
       "302. TRUE\n",
       "303. TRUE\n",
       "304. TRUE\n",
       "305. TRUE\n",
       "306. TRUE\n",
       "307. TRUE\n",
       "308. TRUE\n",
       "309. TRUE\n",
       "310. TRUE\n",
       "311. TRUE\n",
       "312. TRUE\n",
       "313. TRUE\n",
       "314. TRUE\n",
       "315. TRUE\n",
       "316. TRUE\n",
       "317. TRUE\n",
       "318. TRUE\n",
       "319. TRUE\n",
       "320. TRUE\n",
       "321. TRUE\n",
       "322. TRUE\n",
       "323. TRUE\n",
       "324. TRUE\n",
       "325. TRUE\n",
       "326. TRUE\n",
       "327. TRUE\n",
       "328. TRUE\n",
       "329. TRUE\n",
       "330. TRUE\n",
       "331. TRUE\n",
       "332. TRUE\n",
       "333. TRUE\n",
       "334. TRUE\n",
       "335. TRUE\n",
       "336. TRUE\n",
       "337. TRUE\n",
       "338. TRUE\n",
       "339. TRUE\n",
       "340. TRUE\n",
       "341. TRUE\n",
       "342. TRUE\n",
       "343. TRUE\n",
       "344. TRUE\n",
       "345. TRUE\n",
       "346. TRUE\n",
       "347. TRUE\n",
       "348. TRUE\n",
       "349. TRUE\n",
       "350. TRUE\n",
       "351. TRUE\n",
       "352. TRUE\n",
       "353. TRUE\n",
       "354. TRUE\n",
       "355. TRUE\n",
       "356. TRUE\n",
       "357. TRUE\n",
       "358. TRUE\n",
       "359. TRUE\n",
       "360. TRUE\n",
       "361. TRUE\n",
       "362. TRUE\n",
       "363. TRUE\n",
       "364. TRUE\n",
       "365. TRUE\n",
       "366. TRUE\n",
       "367. TRUE\n",
       "368. TRUE\n",
       "369. TRUE\n",
       "370. TRUE\n",
       "371. TRUE\n",
       "372. TRUE\n",
       "373. TRUE\n",
       "374. TRUE\n",
       "375. TRUE\n",
       "376. TRUE\n",
       "377. TRUE\n",
       "378. TRUE\n",
       "379. TRUE\n",
       "380. TRUE\n",
       "381. TRUE\n",
       "382. TRUE\n",
       "383. TRUE\n",
       "384. TRUE\n",
       "385. TRUE\n",
       "386. TRUE\n",
       "387. TRUE\n",
       "388. TRUE\n",
       "389. TRUE\n",
       "390. TRUE\n",
       "391. TRUE\n",
       "392. TRUE\n",
       "393. TRUE\n",
       "394. TRUE\n",
       "395. TRUE\n",
       "396. TRUE\n",
       "397. TRUE\n",
       "398. TRUE\n",
       "399. TRUE\n",
       "400. TRUE\n",
       "401. TRUE\n",
       "\n",
       "\n"
      ],
      "text/plain": [
       "  [1] TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE\n",
       " [16] TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE\n",
       " [31] TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE\n",
       " [46] TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE\n",
       " [61] TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE\n",
       " [76] TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE\n",
       " [91] TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE\n",
       "[106] TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE\n",
       "[121] TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE\n",
       "[136] TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE\n",
       "[151] TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE\n",
       "[166] TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE\n",
       "[181] TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE\n",
       "[196] TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE\n",
       "[211] TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE\n",
       "[226] TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE\n",
       "[241] TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE\n",
       "[256] TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE\n",
       "[271] TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE\n",
       "[286] TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE\n",
       "[301] TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE\n",
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       "1. TRUE\n",
       "2. TRUE\n",
       "3. TRUE\n",
       "4. TRUE\n",
       "5. TRUE\n",
       "6. TRUE\n",
       "7. TRUE\n",
       "8. TRUE\n",
       "9. TRUE\n",
       "10. TRUE\n",
       "11. TRUE\n",
       "12. TRUE\n",
       "13. TRUE\n",
       "14. TRUE\n",
       "15. TRUE\n",
       "16. TRUE\n",
       "17. TRUE\n",
       "18. TRUE\n",
       "19. TRUE\n",
       "20. TRUE\n",
       "21. TRUE\n",
       "22. TRUE\n",
       "23. TRUE\n",
       "24. TRUE\n",
       "25. TRUE\n",
       "26. TRUE\n",
       "27. TRUE\n",
       "28. TRUE\n",
       "29. TRUE\n",
       "30. TRUE\n",
       "31. TRUE\n",
       "32. TRUE\n",
       "33. TRUE\n",
       "34. TRUE\n",
       "35. TRUE\n",
       "36. TRUE\n",
       "37. TRUE\n",
       "38. TRUE\n",
       "39. TRUE\n",
       "40. TRUE\n",
       "41. TRUE\n",
       "42. TRUE\n",
       "43. TRUE\n",
       "44. TRUE\n",
       "45. TRUE\n",
       "46. TRUE\n",
       "47. TRUE\n",
       "48. TRUE\n",
       "49. TRUE\n",
       "50. TRUE\n",
       "51. TRUE\n",
       "52. TRUE\n",
       "53. TRUE\n",
       "54. TRUE\n",
       "55. TRUE\n",
       "56. TRUE\n",
       "57. TRUE\n",
       "58. TRUE\n",
       "59. TRUE\n",
       "60. TRUE\n",
       "61. TRUE\n",
       "62. TRUE\n",
       "63. TRUE\n",
       "64. TRUE\n",
       "65. TRUE\n",
       "66. TRUE\n",
       "67. TRUE\n",
       "68. TRUE\n",
       "69. TRUE\n",
       "70. TRUE\n",
       "71. TRUE\n",
       "72. TRUE\n",
       "73. TRUE\n",
       "74. TRUE\n",
       "75. TRUE\n",
       "76. TRUE\n",
       "77. TRUE\n",
       "78. TRUE\n",
       "79. TRUE\n",
       "80. TRUE\n",
       "81. TRUE\n",
       "82. TRUE\n",
       "83. TRUE\n",
       "84. TRUE\n",
       "85. TRUE\n",
       "86. TRUE\n",
       "87. TRUE\n",
       "88. TRUE\n",
       "89. TRUE\n",
       "90. TRUE\n",
       "91. TRUE\n",
       "92. TRUE\n",
       "93. TRUE\n",
       "94. TRUE\n",
       "95. TRUE\n",
       "96. TRUE\n",
       "97. TRUE\n",
       "98. TRUE\n",
       "99. TRUE\n",
       "100. TRUE\n",
       "101. TRUE\n",
       "102. TRUE\n",
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       "401. TRUE\n",
       "\n",
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      ],
      "text/plain": [
       "  [1] TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE\n",
       " [16] TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE\n",
       " [31] TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE\n",
       " [46] TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE\n",
       " [61] TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE\n",
       " [76] TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE\n",
       " [91] TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE\n",
       "[106] TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE\n",
       "[121] TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE\n",
       "[136] TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE\n",
       "[151] TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE\n",
       "[166] TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE\n",
       "[181] TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE\n",
       "[196] TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE\n",
       "[211] TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE\n",
       "[226] TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE\n",
       "[241] TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE\n",
       "[256] TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE\n",
       "[271] TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE\n",
       "[286] TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE\n",
       "[301] TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE\n",
       "[316] TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE\n",
       "[331] TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE\n",
       "[346] TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE\n",
       "[361] TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE\n",
       "[376] TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE\n",
       "[391] TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE\n",
       "[406] TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE\n",
       "[421] TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE\n",
       "[436] TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE\n",
       "[451] TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE\n",
       "[466] TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE\n",
       "[481] TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE\n",
       "[496] TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE\n",
       "[511] TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE\n",
       "[526] TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE\n",
       "[541] TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE\n",
       "[556] TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE\n",
       "[571] TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE\n",
       "[586] TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE"
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       "192. TRUE\n",
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       "194. TRUE\n",
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       "196. TRUE\n",
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       "198. TRUE\n",
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       "200. TRUE\n",
       "201. TRUE\n",
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       "203. TRUE\n",
       "204. TRUE\n",
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       "244. TRUE\n",
       "245. TRUE\n",
       "246. TRUE\n",
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       "248. TRUE\n",
       "249. TRUE\n",
       "250. TRUE\n",
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       "252. TRUE\n",
       "253. TRUE\n",
       "254. TRUE\n",
       "255. TRUE\n",
       "256. TRUE\n",
       "257. TRUE\n",
       "258. TRUE\n",
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       "260. TRUE\n",
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       "264. TRUE\n",
       "265. TRUE\n",
       "266. TRUE\n",
       "267. TRUE\n",
       "268. TRUE\n",
       "269. TRUE\n",
       "270. TRUE\n",
       "271. TRUE\n",
       "272. TRUE\n",
       "273. TRUE\n",
       "274. TRUE\n",
       "275. TRUE\n",
       "276. TRUE\n",
       "277. TRUE\n",
       "278. TRUE\n",
       "279. TRUE\n",
       "280. TRUE\n",
       "281. TRUE\n",
       "282. TRUE\n",
       "283. TRUE\n",
       "284. TRUE\n",
       "285. TRUE\n",
       "286. TRUE\n",
       "287. TRUE\n",
       "288. TRUE\n",
       "289. TRUE\n",
       "290. TRUE\n",
       "291. TRUE\n",
       "292. TRUE\n",
       "293. TRUE\n",
       "294. TRUE\n",
       "295. TRUE\n",
       "296. TRUE\n",
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       "298. TRUE\n",
       "299. TRUE\n",
       "300. TRUE\n",
       "\n",
       "\n"
      ],
      "text/plain": [
       "  [1] TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE\n",
       " [16] TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE\n",
       " [31] TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE\n",
       " [46] TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE\n",
       " [61] TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE\n",
       " [76] TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE\n",
       " [91] TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE\n",
       "[106] TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE\n",
       "[121] TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE\n",
       "[136] TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE\n",
       "[151] TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE\n",
       "[166] TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE\n",
       "[181] TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE\n",
       "[196] TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE\n",
       "[211] TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE\n",
       "[226] TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE\n",
       "[241] TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE\n",
       "[256] TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE\n",
       "[271] TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE\n",
       "[286] TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE"
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       "\\begin{enumerate*}\n",
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       "\\item TRUE\n",
       "\\item TRUE\n",
       "\\item TRUE\n",
       "\\item TRUE\n",
       "\\item TRUE\n",
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       "\\item TRUE\n",
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       "\\item TRUE\n",
       "\\item TRUE\n",
       "\\item TRUE\n",
       "\\item TRUE\n",
       "\\item TRUE\n",
       "\\item TRUE\n",
       "\\item TRUE\n",
       "\\item TRUE\n",
       "\\item TRUE\n",
       "\\item TRUE\n",
       "\\item TRUE\n",
       "\\item TRUE\n",
       "\\item TRUE\n",
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       "\\item TRUE\n",
       "\\item TRUE\n",
       "\\item TRUE\n",
       "\\item TRUE\n",
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       "\\item TRUE\n",
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       "\\item TRUE\n",
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       "\\item TRUE\n",
       "\\item TRUE\n",
       "\\item TRUE\n",
       "\\item TRUE\n",
       "\\item TRUE\n",
       "\\item TRUE\n",
       "\\item TRUE\n",
       "\\item TRUE\n",
       "\\item TRUE\n",
       "\\item TRUE\n",
       "\\item TRUE\n",
       "\\item TRUE\n",
       "\\item TRUE\n",
       "\\item TRUE\n",
       "\\item TRUE\n",
       "\\item TRUE\n",
       "\\item TRUE\n",
       "\\item TRUE\n",
       "\\item TRUE\n",
       "\\item TRUE\n",
       "\\item TRUE\n",
       "\\item TRUE\n",
       "\\item TRUE\n",
       "\\item TRUE\n",
       "\\item TRUE\n",
       "\\item TRUE\n",
       "\\item TRUE\n",
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       "\\item TRUE\n",
       "\\item TRUE\n",
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       "\\item TRUE\n",
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       "\\item TRUE\n",
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       "\\item TRUE\n",
       "\\item TRUE\n",
       "\\item TRUE\n",
       "\\item TRUE\n",
       "\\item TRUE\n",
       "\\item TRUE\n",
       "\\item TRUE\n",
       "\\end{enumerate*}\n"
      ],
      "text/markdown": [
       "1. TRUE\n",
       "2. TRUE\n",
       "3. TRUE\n",
       "4. TRUE\n",
       "5. TRUE\n",
       "6. TRUE\n",
       "7. TRUE\n",
       "8. TRUE\n",
       "9. TRUE\n",
       "10. TRUE\n",
       "11. TRUE\n",
       "12. TRUE\n",
       "13. TRUE\n",
       "14. TRUE\n",
       "15. TRUE\n",
       "16. TRUE\n",
       "17. TRUE\n",
       "18. TRUE\n",
       "19. TRUE\n",
       "20. TRUE\n",
       "21. TRUE\n",
       "22. TRUE\n",
       "23. TRUE\n",
       "24. TRUE\n",
       "25. TRUE\n",
       "26. TRUE\n",
       "27. TRUE\n",
       "28. TRUE\n",
       "29. TRUE\n",
       "30. TRUE\n",
       "31. TRUE\n",
       "32. TRUE\n",
       "33. TRUE\n",
       "34. TRUE\n",
       "35. TRUE\n",
       "36. TRUE\n",
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       "38. TRUE\n",
       "39. TRUE\n",
       "40. TRUE\n",
       "41. TRUE\n",
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       "43. TRUE\n",
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       "51. TRUE\n",
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       "55. TRUE\n",
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       "64. TRUE\n",
       "65. TRUE\n",
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       "71. TRUE\n",
       "72. TRUE\n",
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       "75. TRUE\n",
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       "80. TRUE\n",
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       "88. TRUE\n",
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       "99. TRUE\n",
       "100. TRUE\n",
       "101. TRUE\n",
       "102. TRUE\n",
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       "104. TRUE\n",
       "105. TRUE\n",
       "106. TRUE\n",
       "107. TRUE\n",
       "108. TRUE\n",
       "109. TRUE\n",
       "110. TRUE\n",
       "111. TRUE\n",
       "112. TRUE\n",
       "113. TRUE\n",
       "114. TRUE\n",
       "115. TRUE\n",
       "116. TRUE\n",
       "117. TRUE\n",
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       "119. TRUE\n",
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       "156. TRUE\n",
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       "158. TRUE\n",
       "159. TRUE\n",
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       "181. TRUE\n",
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       "183. TRUE\n",
       "184. TRUE\n",
       "185. TRUE\n",
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       "187. TRUE\n",
       "188. TRUE\n",
       "189. TRUE\n",
       "190. TRUE\n",
       "191. TRUE\n",
       "192. TRUE\n",
       "193. TRUE\n",
       "194. TRUE\n",
       "195. TRUE\n",
       "196. TRUE\n",
       "197. TRUE\n",
       "198. TRUE\n",
       "199. TRUE\n",
       "200. TRUE\n",
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       "203. TRUE\n",
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       "206. TRUE\n",
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       "208. TRUE\n",
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       "213. TRUE\n",
       "214. TRUE\n",
       "215. TRUE\n",
       "216. TRUE\n",
       "217. TRUE\n",
       "218. TRUE\n",
       "219. TRUE\n",
       "220. TRUE\n",
       "221. TRUE\n",
       "222. TRUE\n",
       "223. TRUE\n",
       "224. TRUE\n",
       "225. TRUE\n",
       "226. TRUE\n",
       "227. TRUE\n",
       "228. TRUE\n",
       "229. TRUE\n",
       "230. TRUE\n",
       "231. TRUE\n",
       "232. TRUE\n",
       "233. TRUE\n",
       "234. TRUE\n",
       "235. TRUE\n",
       "236. TRUE\n",
       "237. TRUE\n",
       "238. TRUE\n",
       "239. TRUE\n",
       "240. TRUE\n",
       "241. TRUE\n",
       "242. TRUE\n",
       "243. TRUE\n",
       "244. TRUE\n",
       "245. TRUE\n",
       "246. TRUE\n",
       "247. TRUE\n",
       "248. TRUE\n",
       "249. TRUE\n",
       "250. TRUE\n",
       "251. TRUE\n",
       "252. TRUE\n",
       "253. TRUE\n",
       "254. TRUE\n",
       "255. TRUE\n",
       "256. TRUE\n",
       "257. TRUE\n",
       "258. TRUE\n",
       "259. TRUE\n",
       "260. TRUE\n",
       "261. TRUE\n",
       "262. TRUE\n",
       "263. TRUE\n",
       "264. TRUE\n",
       "265. TRUE\n",
       "266. TRUE\n",
       "267. TRUE\n",
       "268. TRUE\n",
       "269. TRUE\n",
       "270. TRUE\n",
       "271. TRUE\n",
       "272. TRUE\n",
       "273. TRUE\n",
       "274. TRUE\n",
       "275. TRUE\n",
       "276. TRUE\n",
       "277. TRUE\n",
       "278. TRUE\n",
       "279. TRUE\n",
       "280. TRUE\n",
       "281. TRUE\n",
       "282. TRUE\n",
       "283. TRUE\n",
       "284. TRUE\n",
       "285. TRUE\n",
       "286. TRUE\n",
       "287. TRUE\n",
       "288. TRUE\n",
       "289. TRUE\n",
       "290. TRUE\n",
       "291. TRUE\n",
       "292. TRUE\n",
       "293. TRUE\n",
       "294. TRUE\n",
       "295. TRUE\n",
       "296. TRUE\n",
       "297. TRUE\n",
       "298. TRUE\n",
       "299. TRUE\n",
       "300. TRUE\n",
       "301. TRUE\n",
       "302. TRUE\n",
       "303. TRUE\n",
       "304. TRUE\n",
       "305. TRUE\n",
       "306. TRUE\n",
       "307. TRUE\n",
       "308. TRUE\n",
       "309. TRUE\n",
       "310. TRUE\n",
       "311. TRUE\n",
       "312. TRUE\n",
       "313. TRUE\n",
       "314. TRUE\n",
       "315. TRUE\n",
       "316. TRUE\n",
       "317. TRUE\n",
       "318. TRUE\n",
       "319. TRUE\n",
       "320. TRUE\n",
       "321. TRUE\n",
       "322. TRUE\n",
       "323. TRUE\n",
       "324. TRUE\n",
       "325. TRUE\n",
       "326. TRUE\n",
       "327. TRUE\n",
       "328. TRUE\n",
       "329. TRUE\n",
       "330. TRUE\n",
       "331. TRUE\n",
       "332. TRUE\n",
       "333. TRUE\n",
       "334. TRUE\n",
       "335. TRUE\n",
       "336. TRUE\n",
       "337. TRUE\n",
       "338. TRUE\n",
       "339. TRUE\n",
       "340. TRUE\n",
       "341. TRUE\n",
       "342. TRUE\n",
       "343. TRUE\n",
       "344. TRUE\n",
       "345. TRUE\n",
       "346. TRUE\n",
       "347. TRUE\n",
       "348. TRUE\n",
       "349. TRUE\n",
       "350. TRUE\n",
       "351. TRUE\n",
       "352. TRUE\n",
       "353. TRUE\n",
       "354. TRUE\n",
       "355. TRUE\n",
       "356. TRUE\n",
       "357. TRUE\n",
       "358. TRUE\n",
       "359. TRUE\n",
       "360. TRUE\n",
       "361. TRUE\n",
       "362. TRUE\n",
       "363. TRUE\n",
       "364. TRUE\n",
       "365. TRUE\n",
       "366. TRUE\n",
       "367. TRUE\n",
       "368. TRUE\n",
       "369. TRUE\n",
       "370. TRUE\n",
       "371. TRUE\n",
       "372. TRUE\n",
       "373. TRUE\n",
       "374. TRUE\n",
       "375. TRUE\n",
       "376. TRUE\n",
       "377. TRUE\n",
       "378. TRUE\n",
       "379. TRUE\n",
       "380. TRUE\n",
       "381. TRUE\n",
       "382. TRUE\n",
       "383. TRUE\n",
       "384. TRUE\n",
       "385. TRUE\n",
       "386. TRUE\n",
       "387. TRUE\n",
       "388. TRUE\n",
       "389. TRUE\n",
       "390. TRUE\n",
       "391. TRUE\n",
       "392. TRUE\n",
       "393. TRUE\n",
       "394. TRUE\n",
       "395. TRUE\n",
       "396. TRUE\n",
       "397. TRUE\n",
       "398. TRUE\n",
       "399. TRUE\n",
       "400. TRUE\n",
       "401. TRUE\n",
       "\n",
       "\n"
      ],
      "text/plain": [
       "  [1] TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE\n",
       " [16] TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE\n",
       " [31] TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE\n",
       " [46] TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE\n",
       " [61] TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE\n",
       " [76] TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE\n",
       " [91] TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE\n",
       "[106] TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE\n",
       "[121] TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE\n",
       "[136] TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE\n",
       "[151] TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE\n",
       "[166] TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE\n",
       "[181] TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE\n",
       "[196] TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE\n",
       "[211] TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE\n",
       "[226] TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE\n",
       "[241] TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE\n",
       "[256] TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE\n",
       "[271] TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE\n",
       "[286] TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE\n",
       "[301] TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE\n",
       "[316] TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE\n",
       "[331] TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE\n",
       "[346] TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE\n",
       "[361] TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE\n",
       "[376] TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE\n",
       "[391] TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE\n",
       "[406] TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE\n",
       "[421] TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE\n",
       "[436] TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE\n",
       "[451] TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE\n",
       "[466] TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE\n",
       "[481] TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE\n",
       "[496] TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE\n",
       "[511] TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE\n",
       "[526] TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE\n",
       "[541] TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE\n",
       "[556] TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE\n",
       "[571] TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE\n",
       "[586] TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE"
      ]
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       "<ol class=list-inline><li>TRUE</li><li>TRUE</li><li>TRUE</li><li>TRUE</li><li>TRUE</li><li>TRUE</li><li>TRUE</li><li>TRUE</li><li>TRUE</li><li>TRUE</li><li>TRUE</li><li>TRUE</li><li>TRUE</li><li>TRUE</li><li>TRUE</li><li>TRUE</li><li>TRUE</li><li>TRUE</li><li>TRUE</li><li>TRUE</li><li>TRUE</li><li>TRUE</li><li>TRUE</li><li>TRUE</li><li>TRUE</li><li>TRUE</li><li>TRUE</li><li>TRUE</li><li>TRUE</li><li>TRUE</li><li>TRUE</li><li>TRUE</li><li>TRUE</li><li>TRUE</li><li>TRUE</li><li>TRUE</li><li>TRUE</li><li>TRUE</li><li>TRUE</li><li>TRUE</li><li>TRUE</li><li>TRUE</li><li>TRUE</li><li>TRUE</li><li>TRUE</li><li>TRUE</li><li>TRUE</li><li>TRUE</li><li>TRUE</li><li>TRUE</li><li>TRUE</li><li>TRUE</li><li>TRUE</li><li>TRUE</li><li>TRUE</li><li>TRUE</li><li>TRUE</li><li>TRUE</li><li>TRUE</li><li>TRUE</li><li>TRUE</li><li>TRUE</li><li>TRUE</li><li>TRUE</li><li>TRUE</li><li>TRUE</li><li>TRUE</li><li>TRUE</li><li>TRUE</li><li>TRUE</li><li>TRUE</li><li>TRUE</li><li>TRUE</li><li>TRUE</li><li>TRUE</li><li>TRUE</li><li>TRUE</li><li>TRUE</li><li>TRUE</li><li>TRUE</li><li>TRUE</li><li>TRUE</li><li>TRUE</li><li>TRUE</li><li>TRUE</li><li>TRUE</li><li>TRUE</li><li>TRUE</li><li>TRUE</li><li>TRUE</li><li>TRUE</li><li>TRUE</li><li>TRUE</li><li>TRUE</li><li>TRUE</li><li>TRUE</li><li>TRUE</li><li>TRUE</li><li>TRUE</li><li>TRUE</li><li>TRUE</li><li>TRUE</li><li>TRUE</li><li>TRUE</li><li>TRUE</li><li>TRUE</li><li>TRUE</li><li>TRUE</li><li>TRUE</li><li>TRUE</li><li>TRUE</li><li>TRUE</li><li>TRUE</li><li>TRUE</li><li>TRUE</li><li>TRUE</li><li>TRUE</li><li>TRUE</li><li>TRUE</li><li>TRUE</li><li>TRUE</li><li>TRUE</li><li>TRUE</li><li>TRUE</li><li>TRUE</li><li>TRUE</li><li>TRUE</li><li>TRUE</li><li>TRUE</li><li>TRUE</li><li>TRUE</li><li>TRUE</li><li>TRUE</li><li>TRUE</li><li>TRUE</li><li>TRUE</li><li>TRUE</li><li>TRUE</li><li>TRUE</li><li>TRUE</li><li>TRUE</li><li>TRUE</li><li>TRUE</li><li>TRUE</li><li>TRUE</li><li>TRUE</li><li>TRUE</li><li>TRUE</li><li>TRUE</li><li>TRUE</li><li>TRUE</li><li>TRUE</li><li>TRUE</li><li>TRUE</li><li>TRUE</li><li>TRUE</li><li>TRUE</li><li>TRUE</li><li>TRUE</li><li>TRUE</li><li>TRUE</li><li>TRUE</li><li>TRUE</li><li>TRUE</li><li>TRUE</li><li>TRUE</li><li>TRUE</li><li>TRUE</li><li>TRUE</li><li>TRUE</li><li>TRUE</li><li>TRUE</li><li>TRUE</li><li>TRUE</li><li>TRUE</li><li>TRUE</li><li>TRUE</li><li>TRUE</li><li>TRUE</li><li>TRUE</li><li>TRUE</li><li>TRUE</li><li>TRUE</li><li>TRUE</li><li>TRUE</li><li>TRUE</li><li>TRUE</li><li>TRUE</li><li>TRUE</li><li>TRUE</li><li>TRUE</li><li>TRUE</li><li>TRUE</li><li>TRUE</li><li>TRUE</li><li>TRUE</li><li>TRUE</li><li>TRUE</li><li>TRUE</li><li>TRUE</li><li>TRUE</li><li>TRUE</li><li>TRUE</li><li>TRUE</li><li>TRUE</li><li>TRUE</li><li>TRUE</li><li>TRUE</li><li>TRUE</li><li>TRUE</li><li>TRUE</li><li>TRUE</li><li>TRUE</li><li>TRUE</li><li>TRUE</li><li>TRUE</li><li>TRUE</li><li>TRUE</li><li>TRUE</li><li>TRUE</li><li>TRUE</li><li>TRUE</li><li>TRUE</li><li>TRUE</li><li>TRUE</li><li>TRUE</li></ol>\n"
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       "\\begin{enumerate*}\n",
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      ],
      "text/markdown": [
       "1. TRUE\n",
       "2. TRUE\n",
       "3. TRUE\n",
       "4. TRUE\n",
       "5. TRUE\n",
       "6. TRUE\n",
       "7. TRUE\n",
       "8. TRUE\n",
       "9. TRUE\n",
       "10. TRUE\n",
       "11. TRUE\n",
       "12. TRUE\n",
       "13. TRUE\n",
       "14. TRUE\n",
       "15. TRUE\n",
       "16. TRUE\n",
       "17. TRUE\n",
       "18. TRUE\n",
       "19. TRUE\n",
       "20. TRUE\n",
       "21. TRUE\n",
       "22. TRUE\n",
       "23. TRUE\n",
       "24. TRUE\n",
       "25. TRUE\n",
       "26. TRUE\n",
       "27. TRUE\n",
       "28. TRUE\n",
       "29. TRUE\n",
       "30. TRUE\n",
       "31. TRUE\n",
       "32. TRUE\n",
       "33. TRUE\n",
       "34. TRUE\n",
       "35. TRUE\n",
       "36. TRUE\n",
       "37. TRUE\n",
       "38. TRUE\n",
       "39. TRUE\n",
       "40. TRUE\n",
       "41. TRUE\n",
       "42. TRUE\n",
       "43. TRUE\n",
       "44. TRUE\n",
       "45. TRUE\n",
       "46. TRUE\n",
       "47. TRUE\n",
       "48. TRUE\n",
       "49. TRUE\n",
       "50. TRUE\n",
       "51. TRUE\n",
       "52. TRUE\n",
       "53. TRUE\n",
       "54. TRUE\n",
       "55. TRUE\n",
       "56. TRUE\n",
       "57. TRUE\n",
       "58. TRUE\n",
       "59. TRUE\n",
       "60. TRUE\n",
       "61. TRUE\n",
       "62. TRUE\n",
       "63. TRUE\n",
       "64. TRUE\n",
       "65. TRUE\n",
       "66. TRUE\n",
       "67. TRUE\n",
       "68. TRUE\n",
       "69. TRUE\n",
       "70. TRUE\n",
       "71. TRUE\n",
       "72. TRUE\n",
       "73. TRUE\n",
       "74. TRUE\n",
       "75. TRUE\n",
       "76. TRUE\n",
       "77. TRUE\n",
       "78. TRUE\n",
       "79. TRUE\n",
       "80. TRUE\n",
       "81. TRUE\n",
       "82. TRUE\n",
       "83. TRUE\n",
       "84. TRUE\n",
       "85. TRUE\n",
       "86. TRUE\n",
       "87. TRUE\n",
       "88. TRUE\n",
       "89. TRUE\n",
       "90. TRUE\n",
       "91. TRUE\n",
       "92. TRUE\n",
       "93. TRUE\n",
       "94. TRUE\n",
       "95. TRUE\n",
       "96. TRUE\n",
       "97. TRUE\n",
       "98. TRUE\n",
       "99. TRUE\n",
       "100. TRUE\n",
       "101. TRUE\n",
       "102. TRUE\n",
       "103. TRUE\n",
       "104. TRUE\n",
       "105. TRUE\n",
       "106. TRUE\n",
       "107. TRUE\n",
       "108. TRUE\n",
       "109. TRUE\n",
       "110. TRUE\n",
       "111. TRUE\n",
       "112. TRUE\n",
       "113. TRUE\n",
       "114. TRUE\n",
       "115. TRUE\n",
       "116. TRUE\n",
       "117. TRUE\n",
       "118. TRUE\n",
       "119. TRUE\n",
       "120. TRUE\n",
       "121. TRUE\n",
       "122. TRUE\n",
       "123. TRUE\n",
       "124. TRUE\n",
       "125. TRUE\n",
       "126. TRUE\n",
       "127. TRUE\n",
       "128. TRUE\n",
       "129. TRUE\n",
       "130. TRUE\n",
       "131. TRUE\n",
       "132. TRUE\n",
       "133. TRUE\n",
       "134. TRUE\n",
       "135. TRUE\n",
       "136. TRUE\n",
       "137. TRUE\n",
       "138. TRUE\n",
       "139. TRUE\n",
       "140. TRUE\n",
       "141. TRUE\n",
       "142. TRUE\n",
       "143. TRUE\n",
       "144. TRUE\n",
       "145. TRUE\n",
       "146. TRUE\n",
       "147. TRUE\n",
       "148. TRUE\n",
       "149. TRUE\n",
       "150. TRUE\n",
       "151. TRUE\n",
       "152. TRUE\n",
       "153. TRUE\n",
       "154. TRUE\n",
       "155. TRUE\n",
       "156. TRUE\n",
       "157. TRUE\n",
       "158. TRUE\n",
       "159. TRUE\n",
       "160. TRUE\n",
       "161. TRUE\n",
       "162. TRUE\n",
       "163. TRUE\n",
       "164. TRUE\n",
       "165. TRUE\n",
       "166. TRUE\n",
       "167. TRUE\n",
       "168. TRUE\n",
       "169. TRUE\n",
       "170. TRUE\n",
       "171. TRUE\n",
       "172. TRUE\n",
       "173. TRUE\n",
       "174. TRUE\n",
       "175. TRUE\n",
       "176. TRUE\n",
       "177. TRUE\n",
       "178. TRUE\n",
       "179. TRUE\n",
       "180. TRUE\n",
       "181. TRUE\n",
       "182. TRUE\n",
       "183. TRUE\n",
       "184. TRUE\n",
       "185. TRUE\n",
       "186. TRUE\n",
       "187. TRUE\n",
       "188. TRUE\n",
       "189. TRUE\n",
       "190. TRUE\n",
       "191. TRUE\n",
       "192. TRUE\n",
       "193. TRUE\n",
       "194. TRUE\n",
       "195. TRUE\n",
       "196. TRUE\n",
       "197. TRUE\n",
       "198. TRUE\n",
       "199. TRUE\n",
       "200. TRUE\n",
       "201. TRUE\n",
       "202. TRUE\n",
       "203. TRUE\n",
       "204. TRUE\n",
       "205. TRUE\n",
       "206. TRUE\n",
       "207. TRUE\n",
       "208. TRUE\n",
       "209. TRUE\n",
       "210. TRUE\n",
       "211. TRUE\n",
       "212. TRUE\n",
       "213. TRUE\n",
       "214. TRUE\n",
       "215. TRUE\n",
       "216. TRUE\n",
       "217. TRUE\n",
       "218. TRUE\n",
       "219. TRUE\n",
       "220. TRUE\n",
       "221. TRUE\n",
       "222. TRUE\n",
       "223. TRUE\n",
       "224. TRUE\n",
       "225. TRUE\n",
       "226. TRUE\n",
       "\n",
       "\n"
      ],
      "text/plain": [
       "  [1] TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE\n",
       " [16] TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE\n",
       " [31] TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE\n",
       " [46] TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE\n",
       " [61] TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE\n",
       " [76] TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE\n",
       " [91] TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE\n",
       "[106] TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE\n",
       "[121] TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE\n",
       "[136] TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE\n",
       "[151] TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE\n",
       "[166] TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE\n",
       "[181] TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE\n",
       "[196] TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE\n",
       "[211] TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE\n",
       "[226] TRUE"
      ]
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       "\\begin{enumerate*}\n",
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       "1. TRUE\n",
       "2. TRUE\n",
       "3. TRUE\n",
       "4. TRUE\n",
       "5. TRUE\n",
       "6. TRUE\n",
       "7. TRUE\n",
       "8. TRUE\n",
       "9. TRUE\n",
       "10. TRUE\n",
       "11. TRUE\n",
       "12. TRUE\n",
       "13. TRUE\n",
       "14. TRUE\n",
       "15. TRUE\n",
       "16. TRUE\n",
       "17. TRUE\n",
       "18. TRUE\n",
       "19. TRUE\n",
       "20. TRUE\n",
       "21. TRUE\n",
       "22. TRUE\n",
       "23. TRUE\n",
       "24. TRUE\n",
       "25. TRUE\n",
       "26. TRUE\n",
       "27. TRUE\n",
       "28. TRUE\n",
       "29. TRUE\n",
       "30. TRUE\n",
       "31. TRUE\n",
       "32. TRUE\n",
       "33. TRUE\n",
       "34. TRUE\n",
       "35. TRUE\n",
       "36. TRUE\n",
       "37. TRUE\n",
       "38. TRUE\n",
       "39. TRUE\n",
       "40. TRUE\n",
       "41. TRUE\n",
       "42. TRUE\n",
       "43. TRUE\n",
       "44. TRUE\n",
       "45. TRUE\n",
       "46. TRUE\n",
       "47. TRUE\n",
       "48. TRUE\n",
       "49. TRUE\n",
       "50. TRUE\n",
       "51. TRUE\n",
       "52. TRUE\n",
       "53. TRUE\n",
       "54. TRUE\n",
       "55. TRUE\n",
       "56. TRUE\n",
       "57. TRUE\n",
       "58. TRUE\n",
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       "60. TRUE\n",
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       "78. TRUE\n",
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       "189. TRUE\n",
       "190. TRUE\n",
       "191. TRUE\n",
       "192. TRUE\n",
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       "198. TRUE\n",
       "199. TRUE\n",
       "200. TRUE\n",
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       "213. TRUE\n",
       "214. TRUE\n",
       "215. TRUE\n",
       "216. TRUE\n",
       "217. TRUE\n",
       "218. TRUE\n",
       "219. TRUE\n",
       "220. TRUE\n",
       "221. TRUE\n",
       "222. TRUE\n",
       "\n",
       "\n"
      ],
      "text/plain": [
       "  [1] TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE\n",
       " [16] TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE\n",
       " [31] TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE\n",
       " [46] TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE\n",
       " [61] TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE\n",
       " [76] TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE\n",
       " [91] TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE\n",
       "[106] TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE\n",
       "[121] TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE\n",
       "[136] TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE\n",
       "[151] TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE\n",
       "[166] TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE\n",
       "[181] TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE\n",
       "[196] TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE\n",
       "[211] TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE"
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       "<ol class=list-inline><li>TRUE</li><li>TRUE</li><li>TRUE</li><li>TRUE</li><li>TRUE</li><li>TRUE</li><li>TRUE</li><li>TRUE</li><li>TRUE</li><li>TRUE</li><li>TRUE</li><li>TRUE</li><li>TRUE</li><li>TRUE</li><li>TRUE</li><li>TRUE</li><li>TRUE</li><li>TRUE</li><li>TRUE</li><li>TRUE</li><li>TRUE</li><li>TRUE</li><li>TRUE</li><li>TRUE</li><li>TRUE</li><li>TRUE</li><li>TRUE</li><li>TRUE</li><li>TRUE</li><li>TRUE</li><li>TRUE</li><li>TRUE</li><li>TRUE</li><li>TRUE</li><li>TRUE</li><li>TRUE</li><li>TRUE</li><li>TRUE</li><li>TRUE</li><li>TRUE</li><li>TRUE</li><li>TRUE</li><li>TRUE</li><li>TRUE</li><li>TRUE</li><li>TRUE</li><li>TRUE</li><li>TRUE</li><li>TRUE</li><li>TRUE</li><li>TRUE</li><li>TRUE</li><li>TRUE</li><li>TRUE</li><li>TRUE</li><li>TRUE</li><li>TRUE</li><li>TRUE</li><li>TRUE</li><li>TRUE</li><li>TRUE</li><li>TRUE</li><li>TRUE</li><li>TRUE</li><li>TRUE</li><li>TRUE</li><li>TRUE</li><li>TRUE</li><li>TRUE</li><li>TRUE</li><li>TRUE</li><li>TRUE</li><li>TRUE</li><li>TRUE</li><li>TRUE</li><li>TRUE</li><li>TRUE</li><li>TRUE</li><li>TRUE</li><li>TRUE</li><li>TRUE</li><li>TRUE</li><li>TRUE</li><li>TRUE</li><li>TRUE</li><li>TRUE</li><li>TRUE</li><li>TRUE</li><li>TRUE</li><li>TRUE</li><li>TRUE</li><li>TRUE</li><li>TRUE</li><li>TRUE</li><li>TRUE</li></ol>\n"
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       "\\begin{enumerate*}\n",
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       "\\item TRUE\n",
       "\\item TRUE\n",
       "\\item TRUE\n",
       "\\end{enumerate*}\n"
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       "1. TRUE\n",
       "2. TRUE\n",
       "3. TRUE\n",
       "4. TRUE\n",
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       "6. TRUE\n",
       "7. TRUE\n",
       "8. TRUE\n",
       "9. TRUE\n",
       "10. TRUE\n",
       "11. TRUE\n",
       "12. TRUE\n",
       "13. TRUE\n",
       "14. TRUE\n",
       "15. TRUE\n",
       "16. TRUE\n",
       "17. TRUE\n",
       "18. TRUE\n",
       "19. TRUE\n",
       "20. TRUE\n",
       "21. TRUE\n",
       "22. TRUE\n",
       "23. TRUE\n",
       "24. TRUE\n",
       "25. TRUE\n",
       "26. TRUE\n",
       "27. TRUE\n",
       "28. TRUE\n",
       "29. TRUE\n",
       "30. TRUE\n",
       "31. TRUE\n",
       "32. TRUE\n",
       "33. TRUE\n",
       "34. TRUE\n",
       "35. TRUE\n",
       "36. TRUE\n",
       "37. TRUE\n",
       "38. TRUE\n",
       "39. TRUE\n",
       "40. TRUE\n",
       "41. TRUE\n",
       "42. TRUE\n",
       "43. TRUE\n",
       "44. TRUE\n",
       "45. TRUE\n",
       "46. TRUE\n",
       "47. TRUE\n",
       "48. TRUE\n",
       "49. TRUE\n",
       "50. TRUE\n",
       "51. TRUE\n",
       "52. TRUE\n",
       "53. TRUE\n",
       "54. TRUE\n",
       "55. TRUE\n",
       "56. TRUE\n",
       "57. TRUE\n",
       "58. TRUE\n",
       "59. TRUE\n",
       "60. TRUE\n",
       "61. TRUE\n",
       "62. TRUE\n",
       "63. TRUE\n",
       "64. TRUE\n",
       "65. TRUE\n",
       "66. TRUE\n",
       "67. TRUE\n",
       "68. TRUE\n",
       "69. TRUE\n",
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       "71. TRUE\n",
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       "91. TRUE\n",
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       "93. TRUE\n",
       "94. TRUE\n",
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       "\n",
       "\n"
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       " [1] TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE\n",
       "[16] TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE\n",
       "[31] TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE\n",
       "[46] TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE\n",
       "[61] TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE\n",
       "[76] TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE\n",
       "[91] TRUE TRUE TRUE TRUE TRUE"
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       "<ol class=list-inline><li>TRUE</li><li>TRUE</li><li>TRUE</li><li>TRUE</li><li>TRUE</li><li>TRUE</li><li>TRUE</li><li>TRUE</li><li>TRUE</li><li>TRUE</li><li>TRUE</li><li>TRUE</li><li>TRUE</li><li>TRUE</li><li>TRUE</li><li>TRUE</li><li>TRUE</li><li>TRUE</li><li>TRUE</li><li>TRUE</li><li>TRUE</li><li>TRUE</li><li>TRUE</li><li>TRUE</li><li>TRUE</li><li>TRUE</li><li>TRUE</li><li>TRUE</li><li>TRUE</li><li>TRUE</li><li>TRUE</li><li>TRUE</li><li>TRUE</li><li>TRUE</li><li>TRUE</li><li>TRUE</li><li>TRUE</li><li>TRUE</li><li>TRUE</li><li>TRUE</li><li>TRUE</li><li>TRUE</li><li>TRUE</li><li>TRUE</li><li>TRUE</li><li>TRUE</li><li>TRUE</li><li>TRUE</li><li>TRUE</li><li>TRUE</li><li>TRUE</li><li>TRUE</li><li>TRUE</li><li>TRUE</li><li>TRUE</li><li>TRUE</li><li>TRUE</li><li>TRUE</li><li>TRUE</li><li>TRUE</li><li>TRUE</li><li>TRUE</li><li>TRUE</li><li>TRUE</li><li>TRUE</li><li>TRUE</li><li>TRUE</li><li>TRUE</li><li>TRUE</li><li>TRUE</li><li>TRUE</li><li>TRUE</li><li>TRUE</li><li>TRUE</li><li>TRUE</li><li>TRUE</li><li>TRUE</li><li>TRUE</li><li>TRUE</li><li>TRUE</li><li>TRUE</li><li>TRUE</li><li>TRUE</li><li>TRUE</li><li>TRUE</li><li>TRUE</li><li>TRUE</li><li>TRUE</li><li>TRUE</li><li>TRUE</li><li>TRUE</li><li>TRUE</li><li>TRUE</li><li>TRUE</li><li>TRUE</li><li>TRUE</li><li>TRUE</li><li>TRUE</li><li>TRUE</li><li>TRUE</li><li>TRUE</li><li>TRUE</li><li>TRUE</li><li>TRUE</li><li>TRUE</li><li>TRUE</li><li>TRUE</li><li>TRUE</li><li>TRUE</li><li>TRUE</li><li>TRUE</li><li>TRUE</li><li>TRUE</li><li>TRUE</li><li>TRUE</li><li>TRUE</li><li>TRUE</li><li>TRUE</li><li>TRUE</li><li>TRUE</li><li>TRUE</li><li>TRUE</li><li>TRUE</li><li>TRUE</li><li>TRUE</li><li>TRUE</li><li>TRUE</li><li>TRUE</li><li>TRUE</li><li>TRUE</li><li>TRUE</li><li>TRUE</li><li>TRUE</li><li>TRUE</li><li>TRUE</li><li>TRUE</li><li>TRUE</li><li>TRUE</li><li>TRUE</li><li>TRUE</li><li>TRUE</li><li>TRUE</li><li>TRUE</li><li>TRUE</li><li>TRUE</li><li>TRUE</li><li>TRUE</li><li>TRUE</li><li>TRUE</li><li>TRUE</li><li>TRUE</li><li>TRUE</li><li>TRUE</li><li>TRUE</li><li>TRUE</li><li>TRUE</li><li>TRUE</li><li>TRUE</li><li>TRUE</li><li>TRUE</li><li>TRUE</li><li>TRUE</li><li>TRUE</li><li>TRUE</li><li>TRUE</li><li>TRUE</li><li>TRUE</li><li>TRUE</li><li>TRUE</li><li>TRUE</li><li>TRUE</li><li>TRUE</li><li>TRUE</li><li>TRUE</li><li>TRUE</li><li>TRUE</li><li>TRUE</li><li>TRUE</li><li>TRUE</li><li>TRUE</li><li>TRUE</li><li>TRUE</li><li>TRUE</li><li>TRUE</li><li>TRUE</li><li>TRUE</li><li>TRUE</li><li>TRUE</li><li>TRUE</li><li>TRUE</li><li>TRUE</li><li>TRUE</li><li>TRUE</li><li>TRUE</li><li>TRUE</li><li>TRUE</li><li>TRUE</li><li>TRUE</li><li>TRUE</li><li>TRUE</li><li>TRUE</li><li>TRUE</li><li>TRUE</li><li>TRUE</li><li>TRUE</li><li>TRUE</li><li>TRUE</li><li>TRUE</li><li>TRUE</li><li>TRUE</li><li>TRUE</li><li>TRUE</li><li>TRUE</li><li>TRUE</li><li>TRUE</li><li>TRUE</li><li>TRUE</li><li>TRUE</li><li>TRUE</li><li>TRUE</li><li>TRUE</li><li>TRUE</li><li>TRUE</li><li>TRUE</li><li>TRUE</li><li>TRUE</li><li>TRUE</li></ol>\n"
      ],
      "text/latex": [
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       "\\item TRUE\n",
       "\\item TRUE\n",
       "\\item TRUE\n",
       "\\end{enumerate*}\n"
      ],
      "text/markdown": [
       "1. TRUE\n",
       "2. TRUE\n",
       "3. TRUE\n",
       "4. TRUE\n",
       "5. TRUE\n",
       "6. TRUE\n",
       "7. TRUE\n",
       "8. TRUE\n",
       "9. TRUE\n",
       "10. TRUE\n",
       "11. TRUE\n",
       "12. TRUE\n",
       "13. TRUE\n",
       "14. TRUE\n",
       "15. TRUE\n",
       "16. TRUE\n",
       "17. TRUE\n",
       "18. TRUE\n",
       "19. TRUE\n",
       "20. TRUE\n",
       "21. TRUE\n",
       "22. TRUE\n",
       "23. TRUE\n",
       "24. TRUE\n",
       "25. TRUE\n",
       "26. TRUE\n",
       "27. TRUE\n",
       "28. TRUE\n",
       "29. TRUE\n",
       "30. TRUE\n",
       "31. TRUE\n",
       "32. TRUE\n",
       "33. TRUE\n",
       "34. TRUE\n",
       "35. TRUE\n",
       "36. TRUE\n",
       "37. TRUE\n",
       "38. TRUE\n",
       "39. TRUE\n",
       "40. TRUE\n",
       "41. TRUE\n",
       "42. TRUE\n",
       "43. TRUE\n",
       "44. TRUE\n",
       "45. TRUE\n",
       "46. TRUE\n",
       "47. TRUE\n",
       "48. TRUE\n",
       "49. TRUE\n",
       "50. TRUE\n",
       "51. TRUE\n",
       "52. TRUE\n",
       "53. TRUE\n",
       "54. TRUE\n",
       "55. TRUE\n",
       "56. TRUE\n",
       "57. TRUE\n",
       "58. TRUE\n",
       "59. TRUE\n",
       "60. TRUE\n",
       "61. TRUE\n",
       "62. TRUE\n",
       "63. TRUE\n",
       "64. TRUE\n",
       "65. TRUE\n",
       "66. TRUE\n",
       "67. TRUE\n",
       "68. TRUE\n",
       "69. TRUE\n",
       "70. TRUE\n",
       "71. TRUE\n",
       "72. TRUE\n",
       "73. TRUE\n",
       "74. TRUE\n",
       "75. TRUE\n",
       "76. TRUE\n",
       "77. TRUE\n",
       "78. TRUE\n",
       "79. TRUE\n",
       "80. TRUE\n",
       "81. TRUE\n",
       "82. TRUE\n",
       "83. TRUE\n",
       "84. TRUE\n",
       "85. TRUE\n",
       "86. TRUE\n",
       "87. TRUE\n",
       "88. TRUE\n",
       "89. TRUE\n",
       "90. TRUE\n",
       "91. TRUE\n",
       "92. TRUE\n",
       "93. TRUE\n",
       "94. TRUE\n",
       "95. TRUE\n",
       "96. TRUE\n",
       "97. TRUE\n",
       "98. TRUE\n",
       "99. TRUE\n",
       "100. TRUE\n",
       "101. TRUE\n",
       "102. TRUE\n",
       "103. TRUE\n",
       "104. TRUE\n",
       "105. TRUE\n",
       "106. TRUE\n",
       "107. TRUE\n",
       "108. TRUE\n",
       "109. TRUE\n",
       "110. TRUE\n",
       "111. TRUE\n",
       "112. TRUE\n",
       "113. TRUE\n",
       "114. TRUE\n",
       "115. TRUE\n",
       "116. TRUE\n",
       "117. TRUE\n",
       "118. TRUE\n",
       "119. TRUE\n",
       "120. TRUE\n",
       "121. TRUE\n",
       "122. TRUE\n",
       "123. TRUE\n",
       "124. TRUE\n",
       "125. TRUE\n",
       "126. TRUE\n",
       "127. TRUE\n",
       "128. TRUE\n",
       "129. TRUE\n",
       "130. TRUE\n",
       "131. TRUE\n",
       "132. TRUE\n",
       "133. TRUE\n",
       "134. TRUE\n",
       "135. TRUE\n",
       "136. TRUE\n",
       "137. TRUE\n",
       "138. TRUE\n",
       "139. TRUE\n",
       "140. TRUE\n",
       "141. TRUE\n",
       "142. TRUE\n",
       "143. TRUE\n",
       "144. TRUE\n",
       "145. TRUE\n",
       "146. TRUE\n",
       "147. TRUE\n",
       "148. TRUE\n",
       "149. TRUE\n",
       "150. TRUE\n",
       "151. TRUE\n",
       "152. TRUE\n",
       "153. TRUE\n",
       "154. TRUE\n",
       "155. TRUE\n",
       "156. TRUE\n",
       "157. TRUE\n",
       "158. TRUE\n",
       "159. TRUE\n",
       "160. TRUE\n",
       "161. TRUE\n",
       "162. TRUE\n",
       "163. TRUE\n",
       "164. TRUE\n",
       "165. TRUE\n",
       "166. TRUE\n",
       "167. TRUE\n",
       "168. TRUE\n",
       "169. TRUE\n",
       "170. TRUE\n",
       "171. TRUE\n",
       "172. TRUE\n",
       "173. TRUE\n",
       "174. TRUE\n",
       "175. TRUE\n",
       "176. TRUE\n",
       "177. TRUE\n",
       "178. TRUE\n",
       "179. TRUE\n",
       "180. TRUE\n",
       "181. TRUE\n",
       "182. TRUE\n",
       "183. TRUE\n",
       "184. TRUE\n",
       "185. TRUE\n",
       "186. TRUE\n",
       "187. TRUE\n",
       "188. TRUE\n",
       "189. TRUE\n",
       "190. TRUE\n",
       "191. TRUE\n",
       "192. TRUE\n",
       "193. TRUE\n",
       "194. TRUE\n",
       "195. TRUE\n",
       "196. TRUE\n",
       "197. TRUE\n",
       "198. TRUE\n",
       "199. TRUE\n",
       "200. TRUE\n",
       "201. TRUE\n",
       "202. TRUE\n",
       "203. TRUE\n",
       "204. TRUE\n",
       "205. TRUE\n",
       "206. TRUE\n",
       "207. TRUE\n",
       "208. TRUE\n",
       "209. TRUE\n",
       "210. TRUE\n",
       "211. TRUE\n",
       "212. TRUE\n",
       "213. TRUE\n",
       "214. TRUE\n",
       "215. TRUE\n",
       "216. TRUE\n",
       "217. TRUE\n",
       "218. TRUE\n",
       "219. TRUE\n",
       "220. TRUE\n",
       "221. TRUE\n",
       "222. TRUE\n",
       "223. TRUE\n",
       "224. TRUE\n",
       "225. TRUE\n",
       "226. TRUE\n",
       "227. TRUE\n",
       "\n",
       "\n"
      ],
      "text/plain": [
       "  [1] TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE\n",
       " [16] TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE\n",
       " [31] TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE\n",
       " [46] TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE\n",
       " [61] TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE\n",
       " [76] TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE\n",
       " [91] TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE\n",
       "[106] TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE\n",
       "[121] TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE\n",
       "[136] TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE\n",
       "[151] TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE\n",
       "[166] TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE\n",
       "[181] TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE\n",
       "[196] TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE\n",
       "[211] TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE\n",
       "[226] TRUE TRUE"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "gliomavector <- c(list.files(train_data_glioma))\n",
    "meningiomavector <- c(list.files(train_data_meningioma))\n",
    "notmrvector <- c(list.files(train_data_notmr))\n",
    "pituitaryvector <- c(list.files(train_data_pituitary))\n",
    "\n",
    "# all other dir 600->train and rest test except for notmr -> 300 train and rest test\n",
    "\n",
    "file.copy(file.path(train_data_glioma,gliomavector[1:600]),train_dir_glioma)\n",
    "file.copy(file.path(train_data_meningioma,meningiomavector[1:600]),train_dir_meningioma)\n",
    "file.copy(file.path(train_data_notmr,notmrvector[1:300]),train_dir_notmr)\n",
    "file.copy(file.path(train_data_pituitary,pituitaryvector[1:600]),train_dir_pituitary)\n",
    "\n",
    "#filling the test_dir \n",
    "file.copy(file.path(train_data_glioma,gliomavector[601:length(gliomavector)]),test_dir_glioma)\n",
    "file.copy(file.path(train_data_meningioma,meningiomavector[601:length(meningiomavector)]),test_dir_meningioma)\n",
    "file.copy(file.path(train_data_notmr,notmrvector[301:length(notmrvector)]),test_dir_notmr)\n",
    "file.copy(file.path(train_data_pituitary,pituitaryvector[601:length(pituitaryvector)]),test_dir_pituitary)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "3d56e205",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2023-04-04T17:16:57.073513Z",
     "iopub.status.busy": "2023-04-04T17:16:57.072281Z",
     "iopub.status.idle": "2023-04-04T17:16:57.130041Z",
     "shell.execute_reply": "2023-04-04T17:16:57.128545Z"
    },
    "papermill": {
     "duration": 0.075979,
     "end_time": "2023-04-04T17:16:57.132230",
     "exception": false,
     "start_time": "2023-04-04T17:16:57.056251",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[1] \"Train Dataset :: \"\n",
      "[1] \"Glioma train ::  600\"\n",
      "[1] \"Meningioma train ::  600\"\n",
      "[1] \"Notmr train ::  300\"\n",
      "[1] \"Pituitary train ::  600\"\n",
      "[1] \"----------\"\n",
      "[1] \"Test Dataset\"\n",
      "[1] \"Glioma test ::  226\"\n",
      "[1] \"Meningioma test ::  222\"\n",
      "[1] \"Notmr test ::  95\"\n",
      "[1] \"Pituitary test ::  227\"\n"
     ]
    }
   ],
   "source": [
    "#printing the len of the train and test data\n",
    "print(\"Train Dataset :: \")\n",
    "print(paste(\"Glioma train :: \",length(list.files(train_dir_glioma))))\n",
    "print(paste(\"Meningioma train :: \",length(list.files(train_dir_meningioma))))\n",
    "print(paste(\"Notmr train :: \",length(list.files(train_dir_notmr))))\n",
    "print(paste(\"Pituitary train :: \",length(list.files(train_dir_pituitary))))\n",
    "\n",
    "print(\"----------\")\n",
    "print(\"Test Dataset\")\n",
    "print(paste(\"Glioma test :: \",length(list.files(test_dir_glioma))))\n",
    "print(paste(\"Meningioma test :: \",length(list.files(test_dir_meningioma))))\n",
    "print(paste(\"Notmr test :: \",length(list.files(test_dir_notmr))))\n",
    "print(paste(\"Pituitary test :: \",length(list.files(test_dir_pituitary))))"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "555c0c2d",
   "metadata": {
    "papermill": {
     "duration": 0.015682,
     "end_time": "2023-04-04T17:16:57.163741",
     "exception": false,
     "start_time": "2023-04-04T17:16:57.148059",
     "status": "completed"
    },
    "tags": []
   },
   "source": [
    "# Test and Train Data Splitting"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "66b38f7d",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2023-04-04T17:16:57.198076Z",
     "iopub.status.busy": "2023-04-04T17:16:57.196831Z",
     "iopub.status.idle": "2023-04-04T17:17:05.871636Z",
     "shell.execute_reply": "2023-04-04T17:17:05.869310Z"
    },
    "papermill": {
     "duration": 8.695283,
     "end_time": "2023-04-04T17:17:05.874935",
     "exception": false,
     "start_time": "2023-04-04T17:16:57.179652",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [],
   "source": [
    "\n",
    "train_datagen<-image_data_generator(rescale=1/255,\n",
    "                                   zoom_range=0.2,\n",
    "                                   height_shift_range=0.2,\n",
    "                                   width_shift_range=0.2,\n",
    "                                   fill_mode='nearest')\n",
    "\n",
    "test_datagen<-image_data_generator(rescale = 1/255)\n",
    "\n",
    "train_generator<-flow_images_from_directory(directory=train_dir,\n",
    "                                           generator=train_datagen,\n",
    "                                           class_mode=\"categorical\",\n",
    "                                           target_size=c(224,224),\n",
    "                                           batch_size=32,\n",
    "                                           seed = 123)\n",
    "\n",
    "test_generator<-flow_images_from_directory(directory=test_dir,\n",
    "                                           generator=test_datagen,\n",
    "                                           class_mode=\"categorical\",\n",
    "                                           target_size=c(224,224),\n",
    "                                           batch_size=32,\n",
    "                                           shuffle = FALSE,\n",
    "                                           seed = 123)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "id": "2b4fd22c",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2023-04-04T17:17:05.909498Z",
     "iopub.status.busy": "2023-04-04T17:17:05.907855Z",
     "iopub.status.idle": "2023-04-04T17:17:05.931217Z",
     "shell.execute_reply": "2023-04-04T17:17:05.929243Z"
    },
    "papermill": {
     "duration": 0.043077,
     "end_time": "2023-04-04T17:17:05.933651",
     "exception": false,
     "start_time": "2023-04-04T17:17:05.890574",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [
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       "\n",
       "\n"
      ],
      "text/plain": [
       "  [1] 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0\n",
       " [38] 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0\n",
       " [75] 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0\n",
       "[112] 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0\n",
       "[149] 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0\n",
       "[186] 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0\n",
       "[223] 0 0 0 0 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1\n",
       "[260] 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1\n",
       "[297] 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1\n",
       "[334] 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1\n",
       "[371] 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1\n",
       "[408] 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1\n",
       "[445] 1 1 1 1 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2\n",
       "[482] 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2\n",
       "[519] 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 3 3 3 3 3 3 3 3 3 3 3 3\n",
       "[556] 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3\n",
       "[593] 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3\n",
       "[630] 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3\n",
       "[667] 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3\n",
       "[704] 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3\n",
       "[741] 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "test_generator$classes"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "d0b0f21d",
   "metadata": {
    "papermill": {
     "duration": 0.018428,
     "end_time": "2023-04-04T17:22:51.608437",
     "exception": false,
     "start_time": "2023-04-04T17:22:51.590009",
     "status": "completed"
    },
    "tags": []
   },
   "source": [
    "# VGG 19"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "id": "fdcb98d1",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2023-04-04T17:22:51.676743Z",
     "iopub.status.busy": "2023-04-04T17:22:51.675396Z",
     "iopub.status.idle": "2023-04-04T17:22:52.529701Z",
     "shell.execute_reply": "2023-04-04T17:22:52.527259Z"
    },
    "papermill": {
     "duration": 0.87702,
     "end_time": "2023-04-04T17:22:52.532440",
     "exception": false,
     "start_time": "2023-04-04T17:22:51.655420",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<keras.engine.functional.Functional object at 0x75e6740c8670>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Model: \"vgg19\"\n",
      "________________________________________________________________________________\n",
      "Layer (type)                        Output Shape                    Param #     \n",
      "================================================================================\n",
      "input_2 (InputLayer)                [(None, 224, 224, 3)]           0           \n",
      "________________________________________________________________________________\n",
      "block1_conv1 (Conv2D)               (None, 224, 224, 64)            1792        \n",
      "________________________________________________________________________________\n",
      "block1_conv2 (Conv2D)               (None, 224, 224, 64)            36928       \n",
      "________________________________________________________________________________\n",
      "block1_pool (MaxPooling2D)          (None, 112, 112, 64)            0           \n",
      "________________________________________________________________________________\n",
      "block2_conv1 (Conv2D)               (None, 112, 112, 128)           73856       \n",
      "________________________________________________________________________________\n",
      "block2_conv2 (Conv2D)               (None, 112, 112, 128)           147584      \n",
      "________________________________________________________________________________\n",
      "block2_pool (MaxPooling2D)          (None, 56, 56, 128)             0           \n",
      "________________________________________________________________________________\n",
      "block3_conv1 (Conv2D)               (None, 56, 56, 256)             295168      \n",
      "________________________________________________________________________________\n",
      "block3_conv2 (Conv2D)               (None, 56, 56, 256)             590080      \n",
      "________________________________________________________________________________\n",
      "block3_conv3 (Conv2D)               (None, 56, 56, 256)             590080      \n",
      "________________________________________________________________________________\n",
      "block3_conv4 (Conv2D)               (None, 56, 56, 256)             590080      \n",
      "________________________________________________________________________________\n",
      "block3_pool (MaxPooling2D)          (None, 28, 28, 256)             0           \n",
      "________________________________________________________________________________\n",
      "block4_conv1 (Conv2D)               (None, 28, 28, 512)             1180160     \n",
      "________________________________________________________________________________\n",
      "block4_conv2 (Conv2D)               (None, 28, 28, 512)             2359808     \n",
      "________________________________________________________________________________\n",
      "block4_conv3 (Conv2D)               (None, 28, 28, 512)             2359808     \n",
      "________________________________________________________________________________\n",
      "block4_conv4 (Conv2D)               (None, 28, 28, 512)             2359808     \n",
      "________________________________________________________________________________\n",
      "block4_pool (MaxPooling2D)          (None, 14, 14, 512)             0           \n",
      "________________________________________________________________________________\n",
      "block5_conv1 (Conv2D)               (None, 14, 14, 512)             2359808     \n",
      "________________________________________________________________________________\n",
      "block5_conv2 (Conv2D)               (None, 14, 14, 512)             2359808     \n",
      "________________________________________________________________________________\n",
      "block5_conv3 (Conv2D)               (None, 14, 14, 512)             2359808     \n",
      "________________________________________________________________________________\n",
      "block5_conv4 (Conv2D)               (None, 14, 14, 512)             2359808     \n",
      "________________________________________________________________________________\n",
      "block5_pool (MaxPooling2D)          (None, 7, 7, 512)               0           \n",
      "================================================================================\n",
      "Total params: 20,024,384\n",
      "Trainable params: 9,439,232\n",
      "Non-trainable params: 10,585,152\n",
      "________________________________________________________________________________\n"
     ]
    }
   ],
   "source": [
    "vgg19base = application_vgg19(weights='imagenet',\n",
    "                           include_top=FALSE,\n",
    "                           input_shape=c(224,224,3)) \n",
    "vgg19base\n",
    "freeze_weights(vgg19base)\n",
    "unfreeze_weights(vgg19base, from = \"block5_conv1\")\n",
    "summary(vgg19base)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "id": "101659be",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2023-04-04T17:22:52.572733Z",
     "iopub.status.busy": "2023-04-04T17:22:52.571486Z",
     "iopub.status.idle": "2023-04-04T17:22:52.685812Z",
     "shell.execute_reply": "2023-04-04T17:22:52.684213Z"
    },
    "papermill": {
     "duration": 0.137182,
     "end_time": "2023-04-04T17:22:52.688567",
     "exception": false,
     "start_time": "2023-04-04T17:22:52.551385",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [],
   "source": [
    "model19<-keras_model_sequential() %>%\n",
    "    vgg19base %>%\n",
    "    layer_global_average_pooling_2d() %>%\n",
    "    layer_dense(units=256, activation=\"relu\",kernel_regularizer=regularizer_l1(0.001)) %>%\n",
    "    layer_batch_normalization() %>%\n",
    "    layer_dense(units=4, activation=\"softmax\")\n",
    "\n",
    "model19 %>% compile(\n",
    "    metrics=\"accuracy\",\n",
    "    loss=\"categorical_crossentropy\",\n",
    "    optimizer=optimizer_rmsprop(learning_rate=1e-5) \n",
    ")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "id": "f6c485db",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2023-04-04T17:22:52.727792Z",
     "iopub.status.busy": "2023-04-04T17:22:52.726557Z",
     "iopub.status.idle": "2023-04-04T17:22:52.857481Z",
     "shell.execute_reply": "2023-04-04T17:22:52.855934Z"
    },
    "papermill": {
     "duration": 0.15306,
     "end_time": "2023-04-04T17:22:52.860055",
     "exception": false,
     "start_time": "2023-04-04T17:22:52.706995",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [],
   "source": [
    "model19<-keras_model_sequential() %>%\n",
    "    vgg19base %>%\n",
    "    layer_global_average_pooling_2d() %>%\n",
    "    layer_flatten() %>%\n",
    "    layer_dense(units=128, activation=\"relu\") %>%\n",
    "    layer_batch_normalization() %>%\n",
    "    layer_dense(units=64, activation=\"relu\") %>%\n",
    "    layer_batch_normalization() %>%    \n",
    "    layer_dense(units=4, activation=\"softmax\")\n",
    "\n",
    "model19 %>% compile(\n",
    "    metrics=\"accuracy\",\n",
    "    loss=\"categorical_crossentropy\",\n",
    "    optimizer=optimizer_rmsprop(learning_rate=1e-5) \n",
    ")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "id": "bf74c6dc",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2023-04-04T17:22:52.900036Z",
     "iopub.status.busy": "2023-04-04T17:22:52.898710Z",
     "iopub.status.idle": "2023-04-04T17:22:52.916581Z",
     "shell.execute_reply": "2023-04-04T17:22:52.914558Z"
    },
    "papermill": {
     "duration": 0.039961,
     "end_time": "2023-04-04T17:22:52.918773",
     "exception": false,
     "start_time": "2023-04-04T17:22:52.878812",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Model: \"sequential_3\"\n",
      "________________________________________________________________________________\n",
      "Layer (type)                        Output Shape                    Param #     \n",
      "================================================================================\n",
      "vgg19 (Functional)                  (None, 7, 7, 512)               20024384    \n",
      "________________________________________________________________________________\n",
      "global_average_pooling2d_3 (GlobalA (None, 512)                     0           \n",
      "________________________________________________________________________________\n",
      "flatten_1 (Flatten)                 (None, 512)                     0           \n",
      "________________________________________________________________________________\n",
      "dense_9 (Dense)                     (None, 128)                     65664       \n",
      "________________________________________________________________________________\n",
      "batch_normalization_4 (BatchNormali (None, 128)                     512         \n",
      "________________________________________________________________________________\n",
      "dense_8 (Dense)                     (None, 64)                      8256        \n",
      "________________________________________________________________________________\n",
      "batch_normalization_3 (BatchNormali (None, 64)                      256         \n",
      "________________________________________________________________________________\n",
      "dense_7 (Dense)                     (None, 4)                       260         \n",
      "================================================================================\n",
      "Total params: 20,099,332\n",
      "Trainable params: 9,513,796\n",
      "Non-trainable params: 10,585,536\n",
      "________________________________________________________________________________\n"
     ]
    }
   ],
   "source": [
    "summary(model19)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "id": "22e40f61",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2023-04-04T17:22:52.956781Z",
     "iopub.status.busy": "2023-04-04T17:22:52.955477Z",
     "iopub.status.idle": "2023-04-04T17:27:57.015340Z",
     "shell.execute_reply": "2023-04-04T17:27:57.012797Z"
    },
    "papermill": {
     "duration": 304.082547,
     "end_time": "2023-04-04T17:27:57.018827",
     "exception": false,
     "start_time": "2023-04-04T17:22:52.936280",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "\n",
       "Final epoch (plot to see history):\n",
       "        loss: 0.2749\n",
       "    accuracy: 0.9078\n",
       "    val_loss: 0.7385\n",
       "val_accuracy: 0.6953 "
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "\u001b[1m\u001b[22m`geom_smooth()` using formula = 'y ~ x'\n"
     ]
    },
    {
     "data": {
      "image/png": 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19RyQa709dQYR2LxTLx2mu/+/57ccIk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\niAQSOwAAAACR+H/I9WpP/fWF0wAAAABJRU5ErkJggg=="
     },
     "metadata": {
      "image/png": {
       "height": 420,
       "width": 420
      }
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "history<-model19 %>% fit(train_generator,\n",
    "                                steps_per_epoch=20,\n",
    "                                epoch=20,\n",
    "                                validation_data=test_generator,\n",
    "                                validation_step=20)\n",
    "history;plot(history)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "id": "13cb78e1",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2023-04-04T17:27:57.082072Z",
     "iopub.status.busy": "2023-04-04T17:27:57.080635Z",
     "iopub.status.idle": "2023-04-04T17:27:57.097828Z",
     "shell.execute_reply": "2023-04-04T17:27:57.096307Z"
    },
    "papermill": {
     "duration": 0.044739,
     "end_time": "2023-04-04T17:27:57.100092",
     "exception": false,
     "start_time": "2023-04-04T17:27:57.055353",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "\n",
       "  0   1   2   3 \n",
       "226 222  95 227 "
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "table(test_generator$classes)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "43ba2428",
   "metadata": {},
   "source": [
    "## Summary"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "id": "a41e6749",
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    "execution": {
     "iopub.execute_input": "2023-04-04T17:27:57.143615Z",
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     "start_time": "2023-04-04T17:27:57.120221",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "pred_class19\n",
       "  0   1   2   3 \n",
       "243 216  94 217 "
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "pred19 <- model19 %>% predict(test_generator)\n",
    "pred_class19 <- apply(pred19, 1, max_class)\n",
    "\n",
    "table(pred_class19)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "id": "4c859d4a",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2023-04-04T17:28:01.393449Z",
     "iopub.status.busy": "2023-04-04T17:28:01.392066Z",
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    },
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     "exception": false,
     "start_time": "2023-04-04T17:28:01.369522",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Confusion Matrix and Statistics\n",
       "\n",
       "   pred_class19\n",
       "      0   1   2   3\n",
       "  0 179  45   1   1\n",
       "  1  59 115  10  38\n",
       "  2   1  12  81   1\n",
       "  3   4  44   2 177\n",
       "\n",
       "Overall Statistics\n",
       "                                          \n",
       "               Accuracy : 0.7169          \n",
       "                 95% CI : (0.6836, 0.7485)\n",
       "    No Information Rate : 0.3156          \n",
       "    P-Value [Acc > NIR] : <2e-16          \n",
       "                                          \n",
       "                  Kappa : 0.6113          \n",
       "                                          \n",
       " Mcnemar's Test P-Value : 0.5909          \n",
       "\n",
       "Statistics by Class:\n",
       "\n",
       "                     Class: 0 Class: 1 Class: 2 Class: 3\n",
       "Sensitivity            0.7366   0.5324   0.8617   0.8157\n",
       "Specificity            0.9108   0.8069   0.9793   0.9096\n",
       "Pos Pred Value         0.7920   0.5180   0.8526   0.7797\n",
       "Neg Pred Value         0.8824   0.8157   0.9807   0.9263\n",
       "Prevalence             0.3156   0.2805   0.1221   0.2818\n",
       "Detection Rate         0.2325   0.1494   0.1052   0.2299\n",
       "Detection Prevalence   0.2935   0.2883   0.1234   0.2948\n",
       "Balanced Accuracy      0.8237   0.6696   0.9205   0.8626"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "conf_mat_19 = table(test_generator$classes, pred_class19)\n",
    "confusionMatrix(conf_mat_19)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "id": "dd405992",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2023-04-04T17:28:01.468064Z",
     "iopub.status.busy": "2023-04-04T17:28:01.466770Z",
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     "status": "completed"
    },
    "tags": []
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Test Loss: 0.7091373 \n",
      "Test Accuracy: 0.715625 \n"
     ]
    }
   ],
   "source": [
    "# Evaluate the model\n",
    "test_metrics19 <- model19 %>% evaluate(test_generator, steps = 10)\n",
    "cat(\"Test Loss:\", test_metrics19[[1]], \"\\n\")\n",
    "cat(\"Test Accuracy:\", test_metrics19[[2]], \"\\n\")"
   ]
  }
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