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1397. 1397
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1500. 1500
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1600. 1600
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1641. 1641
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1791. 1791
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1793. 1793
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1799. 1799
1800. 1800
1801. 1801
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1827. 1827
1828. 1828
1829. 1829
1830. 1830
1831. 1831
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1833. 1833
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1835. 1835
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1837. 1837
1838. 1838
1839. 1839
1840. 1840
1841. 1841
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1848. 1848
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1850. 1850
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1860. 1860
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1862. 1862
1863. 1863
1864. 1864
1865. 1865
1866. 1866
1867. 1867
1868. 1868
1869. 1869
1870. 1870
1871. 1871
1872. 1872
1873. 1873
1874. 1874
1875. 1875
1876. 1876
1877. 1877
1878. 1878
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1880. 1880
1881. 1881
1882. 1882
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1884. 1884
1885. 1885
1886. 1886
1887. 1887
1888. 1888
1889. 1889
1890. 1890
1891. 1891
1892. 1892
1893. 1893
1894. 1894
1895. 1895
1896. 1896
1897. 1897
1898. 1898
1899. 1899
1900. 1900
1901. 1901
1902. 1902
1903. 1903
1904. 1904
1905. 1905
1906. 1906
1907. 1907
1908. 1908
1909. 1909
1910. 1910
1911. 1911
1912. 1912
1913. 1913
1914. 1914
1915. 1915
1916. 1916
1917. 1917
1918. 1918
1919. 1919
1920. 1920
1921. 1921
1922. 1922
1923. 1923
1924. 1924
1925. 1925
1926. 1926
1927. 1927
1928. 1928
1929. 1929
1930. 1930
1931. 1931
1932. 1932
1933. 1933
1934. 1934
1935. 1935
1936. 1936
1937. 1937
1938. 1938
1939. 1939
1940. 1940
1941. 1941
1942. 1942
1943. 1943
1944. 1944
1945. 1945
1946. 1946
1947. 1947
1948. 1948
1949. 1949
1950. 1950
1951. 1951
1952. 1952
1953. 1953
1954. 1954
1955. 1955
1956. 1956
1957. 1957
1958. 1958
1959. 1959
1960. 1960
1961. 1961
1962. 1962
1963. 1963
1964. 1964
1965. 1965
1966. 1966
1967. 1967
1968. 1968
1969. 1969
1970. 1970
1971. 1971
1972. 1972
1973. 1973
1974. 1974
1975. 1975
1976. 1976
1977. 1977
1978. 1978
1979. 1979
1980. 1980
1981. 1981
1982. 1982
1983. 1983
1984. 1984
1985. 1985
1986. 1986
1987. 1987
1988. 1988
1989. 1989
1990. 1990
1991. 1991
1992. 1992
1993. 1993
1994. 1994
1995. 1995
1996. 1996
1997. 1997
1998. 1998
1999. 1999
2000. 2000
2001. 2001
2002. 2002
2003. 2003
2004. 2004
2005. 2005
2006. 2006
2007. 2007
2008. 2008
2009. 2009
2010. 2010
2011. 2011
2012. 2012
2013. 2013
2014. 2014
2015. 2015
2016. 2016
2017. 2017
2018. 2018
2019. 2019
2020. 2020
2021. 2021
2022. 2022
2023. 2023
2024. 2024
2025. 2025
2026. 2026
2027. 2027
2028. 2028
2029. 2029
2030. 2030
2031. 2031
2032. 2032
2033. 2033
2034. 2034
2035. 2035
2036. 2036
2037. 2037
2038. 2038
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2091. 2091
2092. 2092
2093. 2093
2094. 2094
2095. 2095
2096. 2096
2097. 2097
2098. 2098
2099. 2099
2100. 2100
2101. 2101
2102. 2102
2103. 2103
2104. 2104
2105. 2105
2106. 2106
2107. 2107
2108. 2108
2109. 2109
2110. 2110
2111. 2111
2112. 2112
2113. 2113
2114. 2114
2115. 2115
2116. 2116
2117. 2117
2118. 2118
2119. 2119
2120. 2120
2121. 2121
2122. 2122
2123. 2123
2124. 2124
2125. 2125
2126. 2126
2127. 2127
2128. 2128
2129. 2129
2130. 2130
2131. 2131
2132. 2132
2133. 2133
2134. 2134
2135. 2135
2136. 2136
2137. 2137
2138. 2138
2139. 2139
2140. 2140
2141. 2141
2142. 2142
2143. 2143
2144. 2144
2145. 2145
2146. 2146
2147. 2147
2148. 2148
2149. 2149
2150. 2150
2151. 2151
2152. 2152
2153. 2153
2154. 2154
2155. 2155
2156. 2156
2157. 2157
2158. 2158
2159. 2159
2160. 2160
2161. 2161
2162. 2162
2163. 2163
2164. 2164
2165. 2165
2166. 2166
2167. 2167
2168. 2168
2169. 2169
2170. 2170
2171. 2171
2172. 2172
2173. 2173
2174. 2174
2175. 2175
2176. 2176
2177. 2177
2178. 2178
2179. 2179
2180. 2180
2181. 2181
2182. 2182
2183. 2183
2184. 2184
2185. 2185
2186. 2186
2187. 2187
2188. 2188
2189. 2189
2190. 2190
2191. 2191
2192. 2192
2193. 2193
2194. 2194
2195. 2195
2196. 2196
2197. 2197
2198. 2198
2199. 2199
2200. 2200
2201. 2201
2202. 2202
2203. 2203
2204. 2204
2205. 2205
2206. 2206
2207. 2207
2208. 2208
2209. 2209
2210. 2210
2211. 2211
2212. 2212
2213. 2213
2214. 2214
2215. 2215
2216. 2216
2217. 2217
2218. 2218
2219. 2219
2220. 2220
2221. 2221
2222. 2222
2223. 2223
2224. 2224
2225. 2225
2226. 2226
2227. 2227
2228. 2228
2229. 2229
2230. 2230
2231. 2231
2232. 2232
2233. 2233
2234. 2234
2235. 2235
2236. 2236
2237. 2237
2238. 2238
2239. 2239
2240. 2240
2241. 2241
2242. 2242
2243. 2243
2244. 2244
2245. 2245
2246. 2246
2247. 2247
2248. 2248
2249. 2249
2250. 2250
2251. 2251
2252. 2252
2253. 2253
2254. 2254
2255. 2255
2256. 2256
2257. 2257
2258. 2258
2259. 2259
2260. 2260
2261. 2261
2262. 2262
2263. 2263
2264. 2264
2265. 2265
2266. 2266
2267. 2267
2268. 2268
2269. 2269
2270. 2270
2271. 2271
2272. 2272
2273. 2273
2274. 2274
2275. 2275
2276. 2276
2277. 2277
2278. 2278
2279. 2279
2280. 2280
2281. 2281
2282. 2282
2283. 2283
2284. 2284
2285. 2285
2286. 2286
2287. 2287
2288. 2288
2289. 2289
2290. 2290
2291. 2291
2292. 2292
2293. 2293
2294. 2294
2295. 2295
2296. 2296
2297. 2297
2298. 2298
2299. 2299
2300. 2300
2301. 2301
2302. 2302
2303. 2303
2304. 2304
2305. 2305
2306. 2306
2307. 2307
2308. 2308
2309. 2309
2310. 2310
2311. 2311
2312. 2312
2313. 2313
2314. 2314
2315. 2315
2316. 2316
2317. 2317
2318. 2318
2319. 2319
2320. 2320
2321. 2321
2322. 2322
2323. 2323
2324. 2324
2325. 2325
2326. 2326
2327. 2327
2328. 2328
2329. 2329
2330. 2330
2331. 2331
2332. 2332
2333. 2333
2334. 2334
2335. 2335
2336. 2336
2337. 2337
2338. 2338
2339. 2339
2340. 2340
2341. 2341
2342. 2342
2343. 2343
2344. 2344
2345. 2345
2346. 2346
2347. 2347
2348. 2348
2349. 2349
2350. 2350
2351. 2351
2352. 2352
2353. 2353
2354. 2354
2355. 2355
2356. 2356
2357. 2357
2358. 2358
2359. 2359
2360. 2360
2361. 2361
2362. 2362
2363. 2363
2364. 2364
2365. 2365
2366. 2366
2367. 2367
2368. 2368
2369. 2369
2370. 2370
2371. 2371
2372. 2372
2373. 2373
2374. 2374
2375. 2375
2376. 2376
2377. 2377
2378. 2378
2379. 2379
2380. 2380
2381. 2381
2382. 2382
2383. 2383
2384. 2384
2385. 2385
2386. 2386
2387. 2387
2388. 2388
2389. 2389
2390. 2390
2391. 2391
2392. 2392
2393. 2393
2394. 2394
2395. 2395
2396. 2396
2397. 2397
2398. 2398
2399. 2399
2400. 2400
2401. 2401
2402. 2402
2403. 2403
2404. 2404
2405. 2405
2406. 2406
2407. 2407
2408. 2408
2409. 2409
2410. 2410
2411. 2411
2412. 2412
2413. 2413
2414. 2414
2415. 2415
2416. 2416
2417. 2417
2418. 2418
2419. 2419
2420. 2420
2421. 2421
2422. 2422
2423. 2423
2424. 2424
2425. 2425
2426. 2426
2427. 2427
2428. 2428
2429. 2429
2430. 2430
2431. 2431
2432. 2432
2433. 2433
2434. 2434
2435. 2435
2436. 2436
2437. 2437
2438. 2438
2439. 2439
2440. 2440
2441. 2441
2442. 2442
2443. 2443
2444. 2444
2445. 2445
2446. 2446
2447. 2447
2448. 2448
2449. 2449
2450. 2450
2451. 2451
2452. 2452
2453. 2453
2454. 2454
2455. 2455
2456. 2456
2457. 2457
2458. 2458
2459. 2459
2460. 2460
2461. 2461
2462. 2462
2463. 2463
2464. 2464
2465. 2465
2466. 2466
2467. 2467
2468. 2468
2469. 2469
2470. 2470
2471. 2471
2472. 2472
2473. 2473
2474. 2474
2475. 2475
2476. 2476
2477. 2477
2478. 2478
2479. 2479
2480. 2480
2481. 2481
2482. 2482
2483. 2483
2484. 2484
2485. 2485
2486. 2486
2487. 2487
2488. 2488
2489. 2489
2490. 2490
2491. 2491
2492. 2492
2493. 2493
2494. 2494
2495. 2495
2496. 2496
2497. 2497
2498. 2498
2499. 2499
2500. 2500
2501. 2501
2502. 2502
2503. 2503
2504. 2504
2505. 2505
2506. 2506
2507. 2507
2508. 2508
2509. 2509
2510. 2510
2511. 2511
2512. 2512
2513. 2513
2514. 2514
2515. 2515
2516. 2516
2517. 2517
2518. 2518
2519. 2519
2520. 2520
2521. 2521
2522. 2522
2523. 2523
2524. 2524
2525. 2525
2526. 2526
2527. 2527
2528. 2528
2529. 2529
2530. 2530
2531. 2531
2532. 2532
2533. 2533
2534. 2534
2535. 2535
2536. 2536
2537. 2537
2538. 2538
2539. 2539
2540. 2540
2541. 2541
2542. 2542
2543. 2543
2544. 2544
2545. 2545
2546. 2546
2547. 2547
2548. 2548
2549. 2549
2550. 2550
2551. 2551
2552. 2552
2553. 2553
2554. 2554
2555. 2555
2556. 2556
2557. 2557
2558. 2558
2559. 2559
2560. 2560
2561. 2561
2562. 2562
2563. 2563
2564. 2564
2565. 2565
2566. 2566
2567. 2567
2568. 2568
2569. 2569
2570. 2570
2571. 2571
2572. 2572
2573. 2573
2574. 2574
2575. 2575
2576. 2576
2577. 2577
2578. 2578
2579. 2579
2580. 2580
2581. 2581
2582. 2582
2583. 2583
2584. 2584
2585. 2585
2586. 2586
2587. 2587
2588. 2588
2589. 2589
2590. 2590
2591. 2591
2592. 2592
2593. 2593
2594. 2594
2595. 2595
2596. 2596
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2800. 2800
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2990. 2990
2991. 2991
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2993. 2993
2994. 2994
2995. 2995
2996. 2996
2997. 2997
2998. 2998
2999. 2999
3000. 3000
3001. 3001
3002. 3002
3003. 3003
3004. 3004
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3007. 3007
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3110. 3110
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3119. 3119
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3141. 3141
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3200. 3200
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3202. 3202
3203. 3203
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3207. 3207
3208. 3208
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3210. 3210
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3260. 3260
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3300. 3300
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3400. 3400
3401. 3401
3402. 3402
3403. 3403
3404. 3404
3405. 3405
3406. 3406
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3408. 3408
3409. 3409
3410. 3410
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3491. 3491
3492. 3492
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3500. 3500
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3600. 3600
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3641. 3641
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3650. 3650
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3760. 3760
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{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "LPphBnKR-aWF"
   },
   "source": [
    "# Step 0: Imports"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "execution_state": "idle",
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "ge5QvElvhCOw",
    "outputId": "c7cdaefa-d6dc-44ad-c258-e4fb2aca97a5"
   },
   "outputs": [],
   "source": [
    "# using tqdm.auto glitches out collaborative editing\n",
    "from tqdm import tqdm\n",
    "import torch\n",
    "import torch.nn as nn\n",
    "from torch.utils.data import DataLoader, TensorDataset\n",
    "import matplotlib as mpl\n",
    "import matplotlib.pyplot as plt, mpld3\n",
    "\n",
    "from math import sqrt\n",
    "from collections import deque\n",
    "import os\n",
    "import random\n",
    "from concurrent.futures import ProcessPoolExecutor\n",
    "import pickle\n",
    "\n",
    "torch.manual_seed(42)\n",
    "random.seed(42)\n",
    "\n",
    "device = torch.device(\"cuda\" if torch.cuda.is_available() else \"cpu\")\n",
    "assert device.type == \"cuda\", \"CUDA is not available. Please check your GPU setup.\""
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "execution_state": "idle",
   "metadata": {
    "id": "lylOX2POPwFL"
   },
   "outputs": [],
   "source": [
    "# VTXS numbers here are inclusive\n",
    "MIN_VTXS = 3\n",
    "MAX_VTXS = 15\n",
    "MAX_TUNE_VTXS = 7\n",
    "AVG_DEG = 2\n",
    "SEQ_LEN = MAX_VTXS + 1 # means 32 edges, final token is the target vertex\n",
    "PAD_TOKEN = 0\n",
    "EPOCH_SIZE = 2**20\n",
    "# vertices are labelled 1,2,...,MAX_VTXS\n",
    "# we also have a padding token which is 0."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "gKt-yIpDebF1"
   },
   "source": [
    "# Step 1: Generate synthetic data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "execution_state": "idle",
   "metadata": {},
   "outputs": [],
   "source": [
    "# original task data\n",
    "# the data will be edge lists\n",
    "# like this: [1 3 1 5 2 4 0 0 0 0 2]\n",
    "# this represents edges (1,3), (1,5) (2,4)\n",
    "# (the zeros are just padding tokens)\n",
    "# the final 2 means which vertex we're going to \n",
    "\n",
    "# the label is the shortest distance from vtx 1 to vtx 2\n",
    "# or \"number of vertices\" if no path exists\n",
    "\n",
    "def random_graph(n):\n",
    "    edge_list = []\n",
    "    adjacencies = [set() for _ in range(n+1)]\n",
    "    indices = [random.randint(1, n) for _ in range(AVG_DEG * n)]\n",
    "    for i in range(0, len(indices), 2):\n",
    "        u = indices[i]\n",
    "        v = indices[i + 1]\n",
    "        if u != v:\n",
    "            if u < v:\n",
    "                edge_list += [u, v]\n",
    "            else:\n",
    "                edge_list += [v, u]\n",
    "            adjacencies[u].add(v)\n",
    "            adjacencies[v].add(u)\n",
    "    edge_list += [PAD_TOKEN]*(2*SEQ_LEN-1-len(edge_list))\n",
    "    return edge_list, adjacencies\n",
    "\n",
    "def SSSP(G, target=2, distonly=True):\n",
    "    dist = [MAX_VTXS] * len(G)\n",
    "    parent = [-1] * len(G)\n",
    "    dist[1] = 0\n",
    "    frontier = deque()\n",
    "    frontier.append(1)\n",
    "    while len(frontier) > 0:\n",
    "        vtx = frontier.popleft()\n",
    "        for x in G[vtx]:\n",
    "            if dist[x] == MAX_VTXS:\n",
    "                parent[x] = vtx\n",
    "                dist[x] = 1 + dist[vtx]\n",
    "                frontier.append(x)\n",
    "                if x == target:\n",
    "                    if distonly:\n",
    "                        return dist[x]\n",
    "                    path = [x]\n",
    "                    while parent[x] != -1:\n",
    "                        x = parent[x]\n",
    "                        path.append(x)\n",
    "                    return list(reversed(path))\n",
    "    return MAX_VTXS if distonly else []\n",
    "\n",
    "# large: size up to MAX_VTXS instead of MAX_TUNE_VTXS\n",
    "# target: 2, \"onpath\", \"any\"\n",
    "# largetarget: whether can go up to MAX_VTXS or just MAX_TUNE_VTXS\n",
    "def mkbatch(size, large=True, target=2, largetarget=True):\n",
    "    graphs = []\n",
    "    dists = []\n",
    "    \n",
    "    for i in range(size):\n",
    "        n = random.randint(MIN_VTXS, MAX_VTXS if large else MAX_TUNE_VTXS)\n",
    "        while True:\n",
    "            edge_list, adj_list = random_graph(n)\n",
    "            if target == 2:\n",
    "                # Train data\n",
    "                dist = SSSP(adj_list)\n",
    "                # Filter out some short distance graphs\n",
    "                if 6 <= dist < MAX_VTXS or (dist < 6 and 2 * random.random() < 1 / (6 - dist)) or (dist == MAX_VTXS and random.random() < 1 / MAX_VTXS):\n",
    "                    edge_list[-1] = 2\n",
    "                    break\n",
    "            elif target == \"onpath\":\n",
    "                path = SSSP(adj_list, 2, False)\n",
    "                if len(path) < 1:\n",
    "                    continue\n",
    "                dist = random.randrange(1, len(path))\n",
    "                if largetarget or path[dist] <= MAX_TUNE_VTXS:\n",
    "                    edge_list[-1] = path[dist]\n",
    "                    break\n",
    "            elif target == \"any\":\n",
    "                edge_list[-1] = random.randint(2, MAX_VTXS if largetarget else MAX_TUNE_VTXS)\n",
    "                dist = SSSP(adj_list, edge_list[-1])\n",
    "                break\n",
    "        graphs.append(edge_list)\n",
    "        dists.append(dist)\n",
    "    \n",
    "    data = torch.tensor(graphs)\n",
    "    labels = torch.tensor(dists, dtype=torch.float32)\n",
    "    padding = data == PAD_TOKEN\n",
    "    return data, labels, padding\n",
    "\n",
    "def savebatch(size, idx):\n",
    "    data, labels, padding = mkbatch(size)\n",
    "    everything = {\n",
    "        \"data\": data,\n",
    "        \"labels\": labels,\n",
    "        \"padding\": padding,\n",
    "    }\n",
    "    \n",
    "    with open(f'data-new/{idx}.pickle', 'wb') as file:\n",
    "        pickle.dump(everything, file)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "execution_state": "idle",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Only need to run this once to generate training data\n",
    "# RESTART THE KERNEL BEFORE RUNNING AND ONLY RUN THE CELLS ABOVE\n",
    "# Python is slow and awful\n",
    "\n",
    "# with ProcessPoolExecutor() as executor:\n",
    "#     for i in range(64):\n",
    "#         executor.submit(savebatch, EPOCH_SIZE, i)\n",
    "#     executor.shutdown()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "execution_state": "idle",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(tensor([[ 5, 11,  9, 10,  3,  4,  6, 13,  3,  9,  9, 13,  1, 10,  6,  8,  1,  2,\n",
       "           6, 13,  4,  5,  1,  4,  2, 10,  0,  0,  0,  0,  2],\n",
       "         [ 4, 10,  1, 10,  2,  7,  6,  9,  4,  5,  4,  6,  5,  7,  3,  5,  0,  0,\n",
       "           0,  0,  0,  0,  0,  0,  0,  0,  0,  0,  0,  0,  2],\n",
       "         [ 9, 10,  3,  7,  1,  3,  5,  6, 13, 14,  1, 15,  4,  6,  4, 11,  2, 11,\n",
       "           6, 13,  9, 15,  7, 14, 10, 12,  3, 15,  0,  0,  2]]),\n",
       " tensor([1., 5., 8.]),\n",
       " tensor([[False, False, False, False, False, False, False, False, False, False,\n",
       "          False, False, False, False, False, False, False, False, False, False,\n",
       "          False, False, False, False, False, False,  True,  True,  True,  True,\n",
       "          False],\n",
       "         [False, False, False, False, False, False, False, False, False, False,\n",
       "          False, False, False, False, False, False,  True,  True,  True,  True,\n",
       "           True,  True,  True,  True,  True,  True,  True,  True,  True,  True,\n",
       "          False],\n",
       "         [False, False, False, False, False, False, False, False, False, False,\n",
       "          False, False, False, False, False, False, False, False, False, False,\n",
       "          False, False, False, False, False, False, False, False,  True,  True,\n",
       "          False]]))"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "mkbatch(3)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "execution_state": "idle",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "bins = torch.bincount(mkbatch(2**15)[1].to(torch.uint8).cpu())\n",
    "plt.bar(range(len(bins)), bins)\n",
    "plt.xlabel(\"Label\")\n",
    "plt.ylabel(\"Count\")\n",
    "with open(\"plots/train-dist.html\", \"w\") as f:\n",
    "    mpld3.save_html(plt.gcf(), f)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "execution_state": "idle",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(0, 0.5, 'Count')"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "bins = torch.bincount(mkbatch(2**15, large=False, target=\"onpath\")[1].to(torch.uint8).cpu())\n",
    "plt.bar(range(len(bins)), bins)\n",
    "plt.xlabel(\"Label\")\n",
    "plt.ylabel(\"Count\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "execution_state": "idle",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(0, 0.5, 'Count')"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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NmzZGTU5Ojst7ZWdnKzo6+gadGQAA8HRuDU3nzp1TYWGhCgsLJUnHjh1TYWGhTpw4oXPnzmn69Ol67733dPz4ceXk5Og//uM/dNttt8lms0mSunbtqmHDhmnChAnat2+f9u7dq6SkJI0cOVKhoaGSpNGjR8vHx0cJCQk6evSo1q1bp2XLlik5OdnoY8qUKdq6davS09NVXFys1NRUHThwQElJSW66MgAAwNO4NTQdOHBAvXv3Vu/evSVJycnJ6t27t+bMmSNvb28dOXJEv/71r3XHHXcoISFBffv21bvvvuuylmjNmjXq0qWLhg4dqnvuuUc///nPXZ7BZLVatX37dh07dkx9+/bVk08+qTlz5rg8y+nOO+/U2rVrtXr1avXs2VN//etftWnTJnXv3v0GXxEAAOCpPOY5TQ3dtTznoSnhOU0AAE/WqJ7TBAAA4AkITQAAACYQmgAAAEwgNAEAAJhAaAIAADCB0AQAAGACoQkAAMAEQhMAAIAJhCYAAAATCE0AAAAmEJoAAABMIDQBAACYQGgCAAAwgdAEAABgAqEJAADABEITAACACYQmAAAAEwhNAAAAJhCaAAAATCA0AQAAmEBoAgAAMIHQBAAAYAKhCQAAwARCEwAAgAmEJgAAABMITQAAACYQmgAAAEwgNAEAAJhAaAIAADCB0AQAAGACoQkAAMAEQhMAAIAJbg1Nubm5uu+++xQaGiqLxaJNmzYZc5cuXdLMmTMVGRmpVq1aKTQ0VGPHjtWpU6dcjtGxY0dZLBaXbcGCBS41R44c0d133y0/Pz+FhYVp4cKFtXrZsGGDunTpIj8/P0VGRmrLli31eOYAAKChcWtoOn/+vHr27KkVK1bUmrtw4YIOHjyop59+WgcPHtSbb76pkpIS/frXv65VO2/ePJ0+fdrYJk+ebMw5HA7FxsaqQ4cOKigo0KJFi5SamqrVq1cbNXl5eRo1apQSEhJ06NAhxcfHKz4+XkVFRfV49gAAoCFp5s43Hz58uIYPH37FOavVquzsbJex5cuXa8CAATpx4oRuueUWY9zf31/BwcFXPM6aNWt08eJFvfLKK/Lx8VG3bt1UWFioxYsXa+LEiZKkZcuWadiwYZo+fbokaf78+crOztby5cuVkZFRh2cMAAAaqga1pqm8vFwWi0WtW7d2GV+wYIHatWun3r17a9GiRaqsrDTm8vPzNWjQIPn4+BhjNptNJSUl+vrrr42amJgYl2PabDbl5+dftZeKigo5HA6XDQAANF5uvdN0Lb777jvNnDlTo0aNUkBAgDH+xBNPqE+fPmrbtq3y8vKUkpKi06dPa/HixZIku92u8PBwl2MFBQUZc23atJHdbjfGLq+x2+1X7SctLU1z586t47MEAACeqkGEpkuXLum3v/2tnE6nVq1a5TKXnJxs/NyjRw/5+Pjod7/7ndLS0uTr61tvPaWkpLi8t8PhUFhYWL29HwAAcC+PD001gemf//yndu7c6XKX6UqioqJUWVmp48ePq3PnzgoODlZpaalLTc1+zTqoq9VcbZ2UJPn6+tZrKAMAAJ7Fo9c01QSmjz/+WDt27FC7du1+8jWFhYXy8vJSYGCgJCk6Olq5ubm6dOmSUZOdna3OnTurTZs2Rk1OTo7LcbKzsxUdHV3n5wQAABomt95pOnfunD755BNj/9ixYyosLFTbtm0VEhKiBx54QAcPHlRWVpaqqqqMNUZt27aVj4+P8vPz9f7772vIkCHy9/dXfn6+pk2bpoceesgIRKNHj9bcuXOVkJCgmTNnqqioSMuWLdOSJUuM950yZYp+8YtfKD09XXFxcXrjjTd04MABl8cSAACAps3idDqd7nrzXbt2aciQIbXGx40bp9TU1FoLuGu88847Gjx4sA4ePKjf//73Ki4uVkVFhcLDw/Xwww8rOTnZ5aOzI0eOKDExUfv371f79u01efJkzZw50+WYGzZs0OzZs3X8+HHdfvvtWrhwoe655x7T5+JwOGS1WlVeXv6THyE2JR1nbXZ3C7UcXxDn7hYAAB7iWn5/uzU0NSaEpisjNAEAPNm1/P726DVNAAAAnoLQBAAAYAKhCQAAwARCEwAAgAmEJgAAABMITQAAACYQmgAAAEwgNAEAAJhwXaHp1ltv1VdffVVrvKysTLfeemtd9AUAAOBRris0HT9+XFVVVbXGKyoq9MUXX9RFXwAAAB7lmv5g79tvv238vG3bNlmtVmO/qqpKOTk56tixY912CAAA4AGuKTTFx8dLkiwWi8aNG+cy17x5c3Xs2FHp6el12yEAAIAHuKbQVF1dLUkKDw/X/v371b59+/rqCwAAwKNcU2iqcezYsbrvBAAAwINdV2iSpJycHOXk5OjMmTPGHagar7zySl30BgAA4DGuKzTNnTtX8+bNU79+/RQSEiKLxVL3nQEAAHiQ6wpNGRkZyszM1MMPP1z3HQEAAHig63pO08WLF3XnnXfWfTcAAAAe6rpC02OPPaa1a9fWfTcAAAAe6ro+nvvuu++0evVq7dixQz169FDz5s1d5hcvXlxX/QEAAHiE6wpNR44cUa9evSRJRUVFLnMsCgcAAI3RdYWmd955p+47AQAA8GDXtaYJAACgqbmuO01Dhgz50Y/hdu7c+e/0BAAA4HGuKzTVrGeqcenSJRUWFqqoqKjWH/IFAABoDK4rNC1ZsuSK46mpqTp37ty/2xMAAIDHqdM1TQ899BB/dw4AADRKdRqa8vPz5efnV5eHBAAA8AjX9fHc/fff77LvdDp1+vRpHThwQE8//XRd9QYAAOAxris0Wa1Wl30vLy917txZ8+bNU2xsbF31BgAA4DGuKzS9+uqrdd8JAACAB7uu0FSjoKBAH330kSSpW7du6t27d131BQAA4FGuKzSdOXNGI0eO1K5du9S6dWtJUllZmYYMGaI33nhDN910U133CQAA4FbX9e25yZMn65tvvtHRo0d19uxZnT17VkVFRXI4HHriiSdMHyc3N1f33XefQkNDZbFYtGnTJpd5p9OpOXPmKCQkRC1atFBMTIw+/vhjl5qzZ89qzJgxCggIUOvWrZWQkFDrWVFHjhzR3XffLT8/P4WFhWnhwoW1etmwYYO6dOkiPz8/RUZGasuWLdd8XQAAQON1XaFp69atWrlypbp27WqMRUREaMWKFfr73/9u+jjnz59Xz549tWLFiivOL1y4UC+++KIyMjL0/vvvq1WrVrLZbPruu++MmjFjxujo0aPKzs5WVlaWcnNzNXHiRGPe4XAoNjZWHTp0UEFBgRYtWqTU1FStXr3aqMnLy9OoUaOUkJCgQ4cOKT4+XvHx8SoqKrqOqwMAABoji9PpdF7ri/z9/fXuu+/W+nMqhw4d0i9+8Qs5HI5rb8Ri0caNGxUfHy99f5cpNDRUTz75pP7whz9IksrLyxUUFKTMzEyNHDlSH330kSIiIrR//37169dP+j7Q3XPPPfr8888VGhqqVatW6Y9//KPsdrt8fHwkSbNmzdKmTZtUXFwsSXrwwQd1/vx5ZWVlGf0MHDhQvXr1UkZGxhX7raioUEVFhbHvcDgUFham8vJyBQQEXPP5N1YdZ212dwu1HF8Q5+4WAAAewuFwyGq1mvr9fV13mn75y19qypQpOnXqlDH2xRdfaNq0aRo6dOj1HLKWY8eOyW63KyYmxhizWq2KiopSfn6+9P3DNFu3bm0EJkmKiYmRl5eX3n//faNm0KBBRmCSJJvNppKSEn399ddGzeXvU1NT8z5XkpaWJqvVamxhYWF1ct4AAMAzXVdoWr58uRwOhzp27KhOnTqpU6dOCg8Pl8Ph0J/+9Kc6acxut0uSgoKCXMaDgoKMObvdrsDAQJf5Zs2aqW3bti41VzrG5e9xtZqa+StJSUlReXm5sZ08efLfOFsAAODpruvbc2FhYTp48KB27NhhfMTVtWvXWndrGjNfX1/5+vq6uw0AAHCDXNOdpp07dyoiIkIOh0MWi0W/+tWvNHnyZE2ePFn9+/dXt27d9O6779ZJY8HBwZKk0tJSl/HS0lJjLjg4WGfOnHGZr6ys1NmzZ11qrnSMy9/jajU18wAAANcUmpYuXaoJEyZccaGU1WrV7373Oy1evLhOGgsPD1dwcLBycnKMMYfDoffff1/R0dGSpOjoaJWVlamgoMCo2blzp6qrqxUVFWXU5Obm6tKlS0ZNdna2OnfurDZt2hg1l79PTU3N+wAAAFxTaDp8+LCGDRt21fnY2FiXAPNTzp07p8LCQhUWFkrfL/4uLCzUiRMnZLFYNHXqVD377LN6++239cEHH2js2LEKDQ01vmHXtWtXDRs2TBMmTNC+ffu0d+9eJSUlaeTIkQoNDZUkjR49Wj4+PkpISNDRo0e1bt06LVu2TMnJyUYfU6ZM0datW5Wenq7i4mKlpqbqwIEDSkpKupbLAwAAGrFrWtNUWlqq5s2bX/1gzZrpyy+/NH28AwcOaMiQIcZ+TZAZN26cMjMzNWPGDJ0/f14TJ05UWVmZfv7zn2vr1q3y8/MzXrNmzRolJSVp6NCh8vLy0ogRI/Tiiy8a81arVdu3b1diYqL69u2r9u3ba86cOS7Pcrrzzju1du1azZ49W0899ZRuv/12bdq0Sd27d7+WywMAABqxa3pOU6dOnZSenm7c6fmhN998U3/4wx/02Wef1WWPDcK1POehKeE5TQAAT1Zvz2m655579PTTT7s8kbvGt99+q2eeeUb33nvvtXcMAADg4a7p47nZs2frzTff1B133KGkpCR17txZklRcXKwVK1aoqqpKf/zjH+urVwAAALe5ptAUFBSkvLw8Pf7440pJSVHNJ3sWi0U2m00rVqyo9ZBIAACAxuCaH27ZoUMHbdmyRV9//bU++eQTOZ1O3X777cbX9wEAABqj63oiuCS1adNG/fv3r9tuAAAAPNR1/e05AACApobQBAAAYAKhCQAAwARCEwAAgAmEJgAAABMITQAAACYQmgAAAEwgNAEAAJhAaAIAADCB0AQAAGACoQkAAMAEQhMAAIAJhCYAAAATCE0AAAAmEJoAAABMIDQBAACYQGgCAAAwgdAEAABgAqEJAADABEITAACACYQmAAAAEwhNAAAAJhCaAAAATGjm7gYAT9Rx1mZ3t1DL8QVx7m4BAJo07jQBAACYQGgCAAAwgdAEAABggseHpo4dO8pisdTaEhMTJUmDBw+uNTdp0iSXY5w4cUJxcXFq2bKlAgMDNX36dFVWVrrU7Nq1S3369JGvr69uu+02ZWZm3tDzBAAAns3jF4Lv379fVVVVxn5RUZF+9atf6Te/+Y0xNmHCBM2bN8/Yb9mypfFzVVWV4uLiFBwcrLy8PJ0+fVpjx45V8+bN9fzzz0uSjh07pri4OE2aNElr1qxRTk6OHnvsMYWEhMhms92wcwUAAJ7L40PTTTfd5LK/YMECderUSb/4xS+MsZYtWyo4OPiKr9++fbs+/PBD7dixQ0FBQerVq5fmz5+vmTNnKjU1VT4+PsrIyFB4eLjS09MlSV27dtWePXu0ZMkSQhMAAJAawsdzl7t48aJef/11Pfroo7JYLMb4mjVr1L59e3Xv3l0pKSm6cOGCMZefn6/IyEgFBQUZYzabTQ6HQ0ePHjVqYmJiXN7LZrMpPz//qr1UVFTI4XC4bAAAoPHy+DtNl9u0aZPKyso0fvx4Y2z06NHq0KGDQkNDdeTIEc2cOVMlJSV68803JUl2u90lMEky9u12+4/WOBwOffvtt2rRokWtXtLS0jR37tx6OU8AAOB5GlRoevnllzV8+HCFhoYaYxMnTjR+joyMVEhIiIYOHapPP/1UnTp1qrdeUlJSlJycbOw7HA6FhYXV2/sBAAD3ajCh6Z///Kd27Nhh3EG6mqioKEnSJ598ok6dOik4OFj79u1zqSktLZUkYx1UcHCwMXZ5TUBAwBXvMkmSr6+vfH19/61zAgAADUeDWdP06quvKjAwUHFxP/6nJAoLCyVJISEhkqTo6Gh98MEHOnPmjFGTnZ2tgIAARUREGDU5OTkux8nOzlZ0dHQ9nAkAAGiIGkRoqq6u1quvvqpx48apWbP/uzn26aefav78+SooKNDx48f19ttva+zYsRo0aJB69OghSYqNjVVERIQefvhhHT58WNu2bdPs2bOVmJho3CmaNGmSPvvsM82YMUPFxcVauXKl1q9fr2nTprntnAEAgGdpEKFpx44dOnHihB599FGXcR8fH+3YsUOxsbHq0qWLnnzySY0YMUJ/+9vfjBpvb29lZWXJ29tb0dHReuihhzR27FiX5zqFh4dr8+bNys7OVs+ePZWenq6XXnqJxw0AAABDg1jTFBsbK6fTWWs8LCxMu3fv/snXd+jQQVu2bPnRmsGDB+vQoUP/Vp8AAKDxahB3mgAAANyN0AQAAGACoQkAAMAEQhMAAIAJhCYAAAATCE0AAAAmEJoAAABMIDQBAACYQGgCAAAwgdAEAABgAqEJAADABEITAACACYQmAAAAEwhNAAAAJhCaAAAATCA0AQAAmEBoAgAAMIHQBAAAYAKhCQAAwARCEwAAgAmEJgAAABMITQAAACYQmgAAAEwgNAEAAJhAaAIAADCB0AQAAGACoQkAAMAEQhMAAIAJhCYAAAATCE0AAAAmEJoAAABMIDQBAACYQGgCAAAwwaNDU2pqqiwWi8vWpUsXY/67775TYmKi2rVrp5/97GcaMWKESktLXY5x4sQJxcXFqWXLlgoMDNT06dNVWVnpUrNr1y716dNHvr6+uu2225SZmXnDzhEAADQMHh2aJKlbt246ffq0se3Zs8eYmzZtmv72t79pw4YN2r17t06dOqX777/fmK+qqlJcXJwuXryovLw8vfbaa8rMzNScOXOMmmPHjikuLk5DhgxRYWGhpk6dqscee0zbtm274ecKAAA8VzN3N/BTmjVrpuDg4Frj5eXlevnll7V27Vr98pe/lCS9+uqr6tq1q9577z0NHDhQ27dv14cffqgdO3YoKChIvXr10vz58zVz5kylpqbKx8dHGRkZCg8PV3p6uiSpa9eu2rNnj5YsWSKbzXbDzxcAAHgmj7/T9PHHHys0NFS33nqrxowZoxMnTkiSCgoKdOnSJcXExBi1Xbp00S233KL8/HxJUn5+viIjIxUUFGTU2Gw2ORwOHT161Ki5/Bg1NTXHuJqKigo5HA6XDQAANF4eHZqioqKUmZmprVu3atWqVTp27JjuvvtuffPNN7Lb7fLx8VHr1q1dXhMUFCS73S5JstvtLoGpZr5m7sdqHA6Hvv3226v2lpaWJqvVamxhYWF1dt4AAMDzePTHc8OHDzd+7tGjh6KiotShQwetX79eLVq0cGtvKSkpSk5ONvYdDgfBCQCARsyj7zT9UOvWrXXHHXfok08+UXBwsC5evKiysjKXmtLSUmMNVHBwcK1v09Xs/1RNQEDAjwYzX19fBQQEuGwAAKDxalCh6dy5c/r0008VEhKivn37qnnz5srJyTHmS0pKdOLECUVHR0uSoqOj9cEHH+jMmTNGTXZ2tgICAhQREWHUXH6MmpqaYwAAAMjTQ9Mf/vAH7d69W8ePH1deXp7+8z//U97e3ho1apSsVqsSEhKUnJysd955RwUFBXrkkUcUHR2tgQMHSpJiY2MVERGhhx9+WIcPH9a2bds0e/ZsJSYmytfXV5I0adIkffbZZ5oxY4aKi4u1cuVKrV+/XtOmTXPz2QMAAE/i0WuaPv/8c40aNUpfffWVbrrpJv385z/Xe++9p5tuukmStGTJEnl5eWnEiBGqqKiQzWbTypUrjdd7e3srKytLjz/+uKKjo9WqVSuNGzdO8+bNM2rCw8O1efNmTZs2TcuWLdPNN9+sl156iccNAAAAFxan0+l0dxONgcPhkNVqVXl5OeubLtNx1mZ3t1DL8QVxP1nTUPsGAFyba/n97dEfzwEAAHgKQhMAAIAJhCYAAAATCE0AAAAmEJoAAABMIDQBAACYQGgCAAAwgdAEAABgAqEJAADABEITAACACYQmAAAAEwhNAAAAJhCaAAAATCA0AQAAmEBoAgAAMIHQBAAAYAKhCQAAwARCEwAAgAmEJgAAABMITQAAACYQmgAAAEwgNAEAAJhAaAIAADCB0AQAAGACoQkAAMCEZu5uAEDd6Thrs7tbqOX4gjh3twAAdYI7TQAAACYQmgAAAEwgNAEAAJhAaAIAADCB0AQAAGACoQkAAMAEQhMAAIAJHh2a0tLS1L9/f/n7+yswMFDx8fEqKSlxqRk8eLAsFovLNmnSJJeaEydOKC4uTi1btlRgYKCmT5+uyspKl5pdu3apT58+8vX11W233abMzMwbco4AAKBh8OjQtHv3biUmJuq9995Tdna2Ll26pNjYWJ0/f96lbsKECTp9+rSxLVy40JirqqpSXFycLl68qLy8PL322mvKzMzUnDlzjJpjx44pLi5OQ4YMUWFhoaZOnarHHntM27Ztu6HnCwAAPJdHPxF869atLvuZmZkKDAxUQUGBBg0aZIy3bNlSwcHBVzzG9u3b9eGHH2rHjh0KCgpSr169NH/+fM2cOVOpqany8fFRRkaGwsPDlZ6eLknq2rWr9uzZoyVLlshms13xuBUVFaqoqDD2HQ5HHZ01AADwRB59p+mHysvLJUlt27Z1GV+zZo3at2+v7t27KyUlRRcuXDDm8vPzFRkZqaCgIGPMZrPJ4XDo6NGjRk1MTIzLMW02m/Lz86/aS1pamqxWq7GFhYXV2XkCAADP49F3mi5XXV2tqVOn6q677lL37t2N8dGjR6tDhw4KDQ3VkSNHNHPmTJWUlOjNN9+UJNntdpfAJMnYt9vtP1rjcDj07bffqkWLFrX6SUlJUXJysrHvcDgITgAANGINJjQlJiaqqKhIe/bscRmfOHGi8XNkZKRCQkI0dOhQffrpp+rUqVO99ePr6ytfX996Oz4AAPAsDeLjuaSkJGVlZemdd97RzTff/KO1UVFRkqRPPvlEkhQcHKzS0lKXmpr9mnVQV6sJCAi44l0mAADQ9Hh0aHI6nUpKStLGjRu1c+dOhYeH/+RrCgsLJUkhISGSpOjoaH3wwQc6c+aMUZOdna2AgABFREQYNTk5OS7Hyc7OVnR0dB2fEQAAaKg8OjQlJibq9ddf19q1a+Xv7y+73S673a5vv/1WkvTpp59q/vz5Kigo0PHjx/X2229r7NixGjRokHr06CFJio2NVUREhB5++GEdPnxY27Zt0+zZs5WYmGh8vDZp0iR99tlnmjFjhoqLi7Vy5UqtX79e06ZNc+v5AwAAz+HRoWnVqlUqLy/X4MGDFRISYmzr1q2TJPn4+GjHjh2KjY1Vly5d9OSTT2rEiBH629/+ZhzD29tbWVlZ8vb2VnR0tB566CGNHTtW8+bNM2rCw8O1efNmZWdnq2fPnkpPT9dLL7101ccNAACApsejF4I7nc4fnQ8LC9Pu3bt/8jgdOnTQli1bfrRm8ODBOnTo0DX3CAAAmgaPvtMEAADgKQhNAAAAJhCaAAAATCA0AQAAmEBoAgAAMIHQBAAAYAKhCQAAwARCEwAAgAmEJgAAABMITQAAACYQmgAAAEwgNAEAAJhAaAIAADCB0AQAAGACoQkAAMAEQhMAAIAJhCYAAAATCE0AAAAmNHN3AwDQcdZmd7dQy/EFce5uAYCH4U4TAACACYQmAAAAEwhNAAAAJhCaAAAATCA0AQAAmEBoAgAAMIHQBAAAYAKhCQAAwARCEwAAgAmEJgAAABP4MyoAcJ348y9A08KdJgAAABMITQAAACYQmn5gxYoV6tixo/z8/BQVFaV9+/a5uyUAAOABCE2XWbdunZKTk/XMM8/o4MGD6tmzp2w2m86cOePu1gAAgJuxEPwyixcv1oQJE/TII49IkjIyMrR582a98sormjVrlrvbA4A6wQJ24PoQmr538eJFFRQUKCUlxRjz8vJSTEyM8vPza9VXVFSooqLC2C8vL5ckORyOG9Rxw1BdccHdLdRi5r8Rfdcd+r6xGnPf3Z/ZdkN6uRZFc23ubgH/ppp/e06n86eLnXA6nU7nF1984ZTkzMvLcxmfPn26c8CAAbXqn3nmGackNjY2NjY2tkawnTx58iezAnearlNKSoqSk5ON/erqap09e1bt2rWTxWJxa29X43A4FBYWppMnTyogIMDd7TR6XO8bi+t9Y3G9byyud/1xOp365ptvFBoa+pO1hKbvtW/fXt7e3iotLXUZLy0tVXBwcK16X19f+fr6uoy1bt263vusCwEBAfxPdwNxvW8srveNxfW+sbje9cNqtZqq49tz3/Px8VHfvn2Vk5NjjFVXVysnJ0fR0dFu7Q0AALgfd5ouk5ycrHHjxqlfv34aMGCAli5dqvPnzxvfpgMAAE0XoekyDz74oL788kvNmTNHdrtdvXr10tatWxUUFOTu1uqEr6+vnnnmmVofK6J+cL1vLK73jcX1vrG43p7B4jT1HTsAAICmjTVNAAAAJhCaAAAATCA0AQAAmEBoAgAAMIHQ1ISsWLFCHTt2lJ+fn6KiorRv3z53t9QopaWlqX///vL391dgYKDi4+NVUlLi7raahAULFshisWjq1KnubqVR++KLL/TQQw+pXbt2atGihSIjI3XgwAF3t9UoVVVV6emnn1Z4eLhatGihTp06af78+eb+ThrqHKGpiVi3bp2Sk5P1zDPP6ODBg+rZs6dsNpvOnDnj7tYand27dysxMVHvvfeesrOzdenSJcXGxur8+fPubq1R279/v/785z+rR48e7m6lUfv666911113qXnz5vr73/+uDz/8UOnp6WrTpo27W2uUXnjhBa1atUrLly/XRx99pBdeeEELFy7Un/70J3e31iTxyIEmIioqSv3799fy5cul7592HhYWpsmTJ2vWrFnubq9R+/LLLxUYGKjdu3dr0KBB7m6nUTp37pz69OmjlStX6tlnn1WvXr20dOlSd7fVKM2aNUt79+7Vu+++6+5WmoR7771XQUFBevnll42xESNGqEWLFnr99dfd2ltTxJ2mJuDixYsqKChQTEyMMebl5aWYmBjl5+e7tbemoLy8XJLUtm1bd7fSaCUmJiouLs7l3zjqx9tvv61+/frpN7/5jQIDA9W7d2/913/9l7vbarTuvPNO5eTk6B//+Ick6fDhw9qzZ4+GDx/u7taaJJ4I3gT861//UlVVVa0nmwcFBam4uNhtfTUF1dXVmjp1qu666y51797d3e00Sm+88YYOHjyo/fv3u7uVJuGzzz7TqlWrlJycrKeeekr79+/XE088IR8fH40bN87d7TU6s2bNksPhUJcuXeTt7a2qqio999xzGjNmjLtba5IITUA9SkxMVFFRkfbs2ePuVhqlkydPasqUKcrOzpafn5+722kSqqur1a9fPz3//POSpN69e6uoqEgZGRmEpnqwfv16rVmzRmvXrlW3bt1UWFioqVOnKjQ0lOvtBoSmJqB9+/by9vZWaWmpy3hpaamCg4Pd1ldjl5SUpKysLOXm5urmm292dzuNUkFBgc6cOaM+ffoYY1VVVcrNzdXy5ctVUVEhb29vt/bY2ISEhCgiIsJlrGvXrvrf//1ft/XUmE2fPl2zZs3SyJEjJUmRkZH65z//qbS0NEKTG7CmqQnw8fFR3759lZOTY4xVV1crJydH0dHRbu2tMXI6nUpKStLGjRu1c+dOhYeHu7ulRmvo0KH64IMPVFhYaGz9+vXTmDFjVFhYSGCqB3fddVetR2j84x//UIcOHdzWU2N24cIFeXm5/qr29vZWdXW123pqyrjT1EQkJydr3Lhx6tevnwYMGKClS5fq/PnzeuSRR9zdWqOTmJiotWvX6q233pK/v7/sdrskyWq1qkWLFu5ur1Hx9/evtVasVatWateuHWvI6sm0adN055136vnnn9dvf/tb7du3T6tXr9bq1avd3VqjdN999+m5557TLbfcom7duunQoUNavHixHn30UXe31iTxyIEmZPny5Vq0aJHsdrt69eqlF198UVFRUe5uq9GxWCxXHH/11Vc1fvz4G95PUzN48GAeOVDPsrKylJKSoo8//ljh4eFKTk7WhAkT3N1Wo/TNN9/o6aef1saNG3XmzBmFhoZq1KhRmjNnjnx8fNzdXpNDaAIAADCBNU0AAAAmEJoAAABMIDQBAACYQGgCAAAwgdAEAABgAqEJAADABEITAACACYQmAAAAEwhNAPAjMjMz1bp163/7OBaLRZs2baqTngC4B6EJQKM3fvx4xcfHu7sNAA0coQkAAMAEQhOAJm3x4sWKjIxUq1atFBYWpt///vc6d+5crbpNmzbp9ttvl5+fn2w2m06ePOky/9Zbb6lPnz7y8/PTrbfeqrlz56qysvIGngmA+kZoAtCkeXl56cUXX9TRo0f12muvaefOnZoxY4ZLzYULF/Tcc8/pL3/5i/bu3auysjKNHDnSmH/33Xc1duxYTZkyRR9++KH+/Oc/KzMzU88995wbzghAfbE4nU6nu5sAgPo0fvx4lZWVmVqI/de//lWTJk3Sv/71L+n7heCPPPKI3nvvPUVFRUmSiouL1bVrV73//vsaMGCAYmJiNHToUKWkpBjHef311zVjxgydOnVK+n4h+MaNG1lbBTRgzdzdAAC4044dO5SWlqbi4mI5HA5VVlbqu+++04ULF9SyZUtJUrNmzdS/f3/jNV26dFHr1q310UcfacCAATp8+LD27t3rcmepqqqq1nEANGyEJgBN1vHjx3Xvvffq8ccf13PPPae2bdtqz549SkhI0MWLF02HnXPnzmnu3Lm6//77a835+fnVQ+cA3IHQBKDJKigoUHV1tdLT0+Xl9f+XeK5fv75WXWVlpQ4cOKABAwZIkkpKSlRWVqauXbtKkvr06aOSkhLddtttN/gMANxIhCYATUJ5ebkKCwtdxtq3b69Lly7pT3/6k+677z7t3btXGRkZtV7bvHlzTZ48WS+++KKaNWumpKQkDRw40AhRc+bM0b333qtbbrlFDzzwgLy8vHT48GEVFRXp2WefvWHnCKB+8e05AE3Crl271Lt3b5ftv//7v7V48WK98MIL6t69u9asWaO0tLRar23ZsqVmzpyp0aNH66677tLPfvYzrVu3zpi32WzKysrS9u3b1b9/fw0cOFBLlixRhw4dbvBZAqhPfHsOAADABO40AQAAmEBoAgAAMIHQBAAAYAKhCQAAwARCEwAAgAmEJgAAABMITQAAACYQmgAAAEwgNAEAAJhAaAIAADCB0AQAAGDC/wOGFHlNUKJeowAAAABJRU5ErkJggg==",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "bins = torch.bincount(mkbatch(2**15, large=True, target=\"onpath\", largetarget=False)[1].to(torch.uint8).cpu())\n",
    "plt.bar(range(len(bins)), bins)\n",
    "plt.xlabel(\"Label\")\n",
    "plt.ylabel(\"Count\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "Q3Cg_8UQep8g"
   },
   "source": [
    "# Step 2: Define Transformer Model"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "execution_state": "idle",
   "metadata": {
    "id": "tLOWhg_CeWzH"
   },
   "outputs": [],
   "source": [
    "class TransformerModel(nn.Module):\n",
    "    def __init__(self, input_dim, model_dim, output_dim, num_heads, num_layers, seq_len, dropout):\n",
    "        super().__init__()\n",
    "        self.model_dim = model_dim\n",
    "        self.embedding = nn.Embedding(input_dim, model_dim // 2)\n",
    "        encoder_layer = nn.TransformerEncoderLayer(d_model=model_dim, nhead=num_heads,\n",
    "                                                   dim_feedforward=model_dim*4,\n",
    "                                                   dropout=dropout, batch_first=True)\n",
    "        self.transformer_encoder = nn.TransformerEncoder(encoder_layer, num_layers)\n",
    "        self.fc_out = nn.Linear(model_dim, output_dim)\n",
    "\n",
    "    def forward(self, src, key_padding_mask):\n",
    "        batch_sz = src.size(0)\n",
    "        src = torch.cat((src, torch.full((batch_sz, 1), MAX_VTXS + 1, device=device)), dim=1)\n",
    "        embed = torch.cat((self.embedding(src[:,::2]), self.embedding(src[:,1::2])), dim=2)\n",
    "        output = self.transformer_encoder(embed, src_key_padding_mask=key_padding_mask[:, ::2])\n",
    "        return self.fc_out(output[:, -1, :])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "bpIeg86S-hBb"
   },
   "source": [
    "# Step 3: Make Model"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "execution_state": "idle",
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "kWXvJRDYgFVP",
    "outputId": "c13adb9d-6565-43b5-8437-20cef3dc0d16"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Training data: 209715K\n",
      "Trainable parameters in the model: 550433\n"
     ]
    }
   ],
   "source": [
    "# PARAMS\n",
    "VOCAB_SIZE = 1 + MAX_VTXS + 1 # pad plus max number of vertices plus target token\n",
    "MODEL_DIM = 64 # Dimension of model (embedding and transformer)\n",
    "NEPOCHS = 200\n",
    "BSZ = 2**15 # Batch size\n",
    "NHEADS = 2\n",
    "NLAYERS = 11\n",
    "DROPOUT = 0 # 0.2\n",
    "model = TransformerModel(input_dim=VOCAB_SIZE, model_dim=MODEL_DIM,\n",
    "                         output_dim=1, num_heads=NHEADS,\n",
    "                         num_layers=NLAYERS, seq_len=SEQ_LEN,\n",
    "                         dropout=DROPOUT).to(device)\n",
    "model = torch.compile(model)\n",
    "\n",
    "trainable_params = sum(p.numel() for p in model.parameters() if p.requires_grad)\n",
    "print(f\"Training data: {NEPOCHS*EPOCH_SIZE//10**3}K\")\n",
    "print(f\"Trainable parameters in the model: {trainable_params}\")\n",
    "\n",
    "train_err = []\n",
    "len1 = []\n",
    "len2 = []\n",
    "len3 = []\n",
    "leninf = []\n",
    "epoch = 0\n",
    "\n",
    "# clear loss file\n",
    "open('loss', 'w').close()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "execution_state": "idle",
   "metadata": {},
   "outputs": [],
   "source": [
    "# model = TransformerModel(input_dim=VOCAB_SIZE, model_dim=MODEL_DIM,\n",
    "#                          output_dim=1, num_heads=NHEADS,\n",
    "#                          num_layers=NLAYERS, seq_len=SEQ_LEN,\n",
    "#                          dropout=DROPOUT).to(device)\n",
    "# model = torch.compile(model)\n",
    "# model.load_state_dict(torch.load('model.pth', weights_only=True))\n",
    "\n",
    "LR = 5e-4\n",
    "\n",
    "criterion = nn.MSELoss()\n",
    "optimizer = torch.optim.Adam(model.parameters(), lr=LR)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "f8Zn33m7CxL5"
   },
   "source": [
    "# Step 4: Train the Model for the first task"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "execution_state": "running",
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 486
    },
    "id": "pvTfzGmCeXU4",
    "outputId": "0d3a20f3-23be-4c19-9eb6-46bfe11a48b1"
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/home/sipb/.venv/lib64/python3.12/site-packages/torch/nn/functional.py:6278: UserWarning: Memory Efficient attention on Navi31 GPU is still experimental. Enable it with TORCH_ROCM_AOTRITON_ENABLE_EXPERIMENTAL=1. (Triggered internally at ../aten/src/ATen/native/transformers/hip/sdp_utils.cpp:269.)\n",
      "  attn_output = scaled_dot_product_attention(\n",
      "/tmp/torchinductor_sipb/lc/clcqc3ufbzrethiy77dmsu54kurxdmh4eji2f3msm347rhmfpf4j.py:1078: UserWarning: Attempting to use hipBLASLt on an unsupported architecture! Overriding blas backend to hipblas (Triggered internally at ../aten/src/ATen/Context.cpp:296.)\n",
      "  extern_kernels.mm(reinterpret_tensor(buf1, (524288, 64), (64, 1), 0), reinterpret_tensor(primals_5, (64, 192), (1, 64), 0), out=buf2)\n",
      "\n",
      "00%|███████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████| 32/32 [00:41<00:00,  1.31s/it]"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 0/200 \t Train Err: 29.90892779827118 11.204990872880444 6.002899471088313 2.7772275000461377 119.98257398605347\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "\n",
      "00%|███████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████| 32/32 [00:30<00:00,  1.06it/s]"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 1/200 \t Train Err: 24.833097636699677 7.996231320299557 10.17519548535347 4.934848785400391 95.85787630081177\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "\n",
      "00%|███████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████| 32/32 [00:30<00:00,  1.05it/s]"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 2/200 \t Train Err: 20.868117928504944 0.06162079039495438 9.863971814513206 5.750443048775196 84.68168807029724\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "\n",
      "00%|███████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████| 32/32 [00:30<00:00,  1.04it/s]"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 3/200 \t Train Err: 16.48520603775978 0.03265308297704905 2.906232228502631 4.825529254972935 70.63315153121948\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "\n",
      "00%|███████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████| 32/32 [00:30<00:00,  1.05it/s]"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 4/200 \t Train Err: 12.373839050531387 0.012339260501903482 1.0458019012585282 5.470552921295166 52.21906542778015\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "\n",
      "00%|███████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████| 32/32 [00:30<00:00,  1.06it/s]"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 5/200 \t Train Err: 9.265015065670013 0.020387541531817988 0.6235579419881105 4.304356303066015 38.14229780435562\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "\n",
      "00%|███████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████| 32/32 [00:30<00:00,  1.05it/s]"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 6/200 \t Train Err: 6.847102478146553 0.00765054406656418 0.34211935847997665 3.4029701724648476 27.37058013677597\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "\n",
      "00%|███████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████| 32/32 [00:30<00:00,  1.05it/s]"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 7/200 \t Train Err: 5.120877355337143 0.006255884032725589 0.24545069271698594 2.8054168857634068 19.56860715150833\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "\n",
      "00%|███████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████| 32/32 [00:30<00:00,  1.06it/s]"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 8/200 \t Train Err: 3.8786706030368805 0.01005001129124139 0.19835278647951782 2.2635713517665863 14.35572400689125\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "\n",
      "00%|███████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████| 32/32 [00:30<00:00,  1.05it/s]"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 9/200 \t Train Err: 3.386067658662796 0.06476508973355521 0.3309611896984279 2.269792005419731 11.835063099861145\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "\n",
      "00%|███████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████| 32/32 [00:30<00:00,  1.07it/s]"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 10/200 \t Train Err: 2.6640610471367836 0.019311404510517605 0.15212045633234084 1.7280069142580032 9.439383015036583\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "\n",
      "00%|███████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████| 32/32 [00:30<00:00,  1.05it/s]"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 11/200 \t Train Err: 2.271987594664097 0.004357099162007216 0.09561244037467986 1.4371714778244495 8.072030693292618\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "\n",
      "00%|███████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████| 32/32 [00:30<00:00,  1.06it/s]"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 12/200 \t Train Err: 2.110176682472229 0.0037538947944995016 0.0762243423378095 1.3135331608355045 7.434565886855125\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "\n",
      "00%|███████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████| 32/32 [00:30<00:00,  1.06it/s]"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 13/200 \t Train Err: 1.935215126723051 0.0025575611580279656 0.06788019218947738 1.1661983076483011 6.806286051869392\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "\n",
      "00%|███████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████| 32/32 [00:30<00:00,  1.06it/s]"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 14/200 \t Train Err: 1.8060001619160175 0.0021859380358364433 0.05315158853773028 1.051108269020915 6.399133637547493\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "\n",
      "00%|███████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████| 32/32 [00:30<00:00,  1.07it/s]"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 15/200 \t Train Err: 1.7425284087657928 0.003451302447501803 0.042916826030705124 0.9607762675732374 6.2206505835056305\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "\n",
      "00%|███████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████| 32/32 [00:30<00:00,  1.06it/s]"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 16/200 \t Train Err: 1.649382572621107 0.0032472783022967633 0.039788190391846 0.9184560999274254 5.846587151288986\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "\n",
      "00%|███████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████| 32/32 [00:30<00:00,  1.05it/s]"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 17/200 \t Train Err: 1.6013295575976372 0.0026137664608540945 0.03258192975772545 0.8397737592458725 5.709988132119179\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "\n",
      "00%|███████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████| 32/32 [00:30<00:00,  1.05it/s]"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 18/200 \t Train Err: 1.5462302453815937 0.002207836727393442 0.028909130429383367 0.7771711964160204 5.481079399585724\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "\n",
      "00%|███████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████| 32/32 [00:30<00:00,  1.05it/s]"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 19/200 \t Train Err: 1.4744272604584694 0.002453724335282459 0.027229167113546282 0.7396254185587168 5.192027851939201\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "\n",
      "00%|███████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████| 32/32 [00:30<00:00,  1.05it/s]"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 20/200 \t Train Err: 1.405750896781683 0.0035180113754904596 0.025897870946209878 0.7007174119353294 4.946545287966728\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "\n",
      "00%|███████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████| 32/32 [00:30<00:00,  1.05it/s]"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 21/200 \t Train Err: 1.37083425745368 0.005473145276482683 0.026675976172555238 0.6762280324473977 4.781045973300934\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "\n",
      "00%|███████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████| 32/32 [00:30<00:00,  1.05it/s]"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 22/200 \t Train Err: 1.3181258291006088 0.003216923019863316 0.02211966845788993 0.6302088163793087 4.611788898706436\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "\n",
      "00%|███████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████| 32/32 [00:30<00:00,  1.06it/s]"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 23/200 \t Train Err: 1.270566176623106 0.0022422261081374018 0.019996988266939297 0.5934071252122521 4.4385934472084045\n"
     ]
    },
    {
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     "output_type": "stream",
     "text": [
      "\n",
      "00%|███████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████| 32/32 [00:30<00:00,  1.06it/s]"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 24/200 \t Train Err: 1.1932538747787476 0.004239499055984197 0.020018045412143692 0.552213310264051 4.133698016405106\n"
     ]
    },
    {
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     "output_type": "stream",
     "text": [
      "\n",
      "00%|███████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████| 32/32 [00:30<00:00,  1.05it/s]"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 25/200 \t Train Err: 1.173534195870161 0.00575368078352767 0.02177350874990225 0.5586085859686136 4.002377942204475\n"
     ]
    },
    {
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     "text": [
      "\n",
      "00%|███████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████| 32/32 [00:30<00:00,  1.06it/s]"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 26/200 \t Train Err: 1.0854019112884998 0.003320175252156332 0.01748313583084382 0.5035764751955867 3.7186833322048187\n"
     ]
    },
    {
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      "00%|███████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████| 32/32 [00:30<00:00,  1.04it/s]"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 27/200 \t Train Err: 1.0489223580807447 0.002155393432985875 0.014516596915200353 0.47800380270928144 3.589349642395973\n"
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    },
    {
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     ]
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    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 28/200 \t Train Err: 1.0285752080380917 0.003717334211614798 0.01597064675297588 0.4826665371656418 3.4730969667434692\n"
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    },
    {
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      "Epoch 29/200 \t Train Err: 0.9720142241567373 0.004084829584826366 0.014119503815891221 0.43038015346974134 3.3248252645134926\n"
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      "Epoch 30/200 \t Train Err: 0.9608706310391426 0.002451939526508795 0.011319156386889517 0.4139528265222907 3.2747417390346527\n"
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      "Epoch 31/200 \t Train Err: 0.9179007802158594 0.002311610933247721 0.01079355452384334 0.3930521896108985 3.1119269728660583\n"
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      "Epoch 32/200 \t Train Err: 0.8977434728294611 0.0021589329044218175 0.00950733212812338 0.3691776511259377 3.040615402162075\n"
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      "Epoch 33/200 \t Train Err: 0.881706677377224 0.0020159517880529165 0.009437998596695252 0.35962088825181127 2.982758790254593\n"
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     "text": [
      "Epoch 34/200 \t Train Err: 0.8403967656195164 0.001898838694614824 0.00869621682795696 0.3381345858797431 2.8688452169299126\n"
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     "text": [
      "Epoch 35/200 \t Train Err: 0.8310248833149672 0.0013434199390758295 0.007526626766775735 0.32297050580382347 2.818771593272686\n"
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     "output_type": "stream",
     "text": [
      "Epoch 36/200 \t Train Err: 0.8045722767710686 0.0018811499339790316 0.0069304521166486666 0.30397533625364304 2.722321219742298\n"
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     "output_type": "stream",
     "text": [
      "Epoch 37/200 \t Train Err: 0.807910643517971 0.0022051451851439197 0.007862750302592758 0.30017248587682843 2.739243097603321\n"
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     "output_type": "stream",
     "text": [
      "Epoch 38/200 \t Train Err: 0.7853260803967714 0.005179399433473009 0.008613282887381501 0.2864332143217325 2.6513936147093773\n"
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     "output_type": "stream",
     "text": [
      "Epoch 39/200 \t Train Err: 0.7792632356286049 0.004665168027713662 0.009170589764835313 0.277913779951632 2.6232368499040604\n"
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     "output_type": "stream",
     "text": [
      "Epoch 40/200 \t Train Err: 0.7583489026874304 0.00292711379006505 0.007873743350501172 0.27461743634194136 2.522021509706974\n"
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     "output_type": "stream",
     "text": [
      "Epoch 41/200 \t Train Err: 0.7282119914889336 0.0017558942890900653 0.006133111986855511 0.23886349285021424 2.4373623058199883\n"
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     "output_type": "stream",
     "text": [
      "Epoch 42/200 \t Train Err: 0.7180026862770319 0.0023761796346661868 0.006810532162489835 0.23614320810884237 2.375364724546671\n"
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     "output_type": "stream",
     "text": [
      "Epoch 43/200 \t Train Err: 0.7019325699657202 0.005046901515015634 0.008944688888732344 0.22622147155925632 2.30900501832366\n"
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     "output_type": "stream",
     "text": [
      "Epoch 44/200 \t Train Err: 0.6817822623997927 0.0017093055594159523 0.005646904020977672 0.21380048524588346 2.223499771207571\n"
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    {
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     "output_type": "stream",
     "text": [
      "Epoch 45/200 \t Train Err: 0.6641208492219448 0.0038460963405668736 0.0067629652403411455 0.21014394168742 2.1612670943140984\n"
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     "output_type": "stream",
     "text": [
      "Epoch 46/200 \t Train Err: 0.6523595433682203 0.0018703954374359455 0.006019242704496719 0.20436032535508275 2.1252111680805683\n"
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     "output_type": "stream",
     "text": [
      "Epoch 47/200 \t Train Err: 0.617787566035986 0.0030503157668135827 0.0069770159825566225 0.1832384041044861 1.9817177206277847\n"
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     "output_type": "stream",
     "text": [
      "Epoch 48/200 \t Train Err: 0.600332060828805 0.002064528713162872 0.0051435344576020725 0.17475251341238618 1.9067389331758022\n"
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     "output_type": "stream",
     "text": [
      "Epoch 49/200 \t Train Err: 0.5904311630874872 0.004617106849764241 0.006938040380191524 0.16196146188303828 1.8893518187105656\n"
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     "output_type": "stream",
     "text": [
      "Epoch 50/200 \t Train Err: 0.5741921290755272 0.004996264638975845 0.007533758063800633 0.15988326235674322 1.8061449080705643\n"
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     "output_type": "stream",
     "text": [
      "Epoch 51/200 \t Train Err: 0.532125998288393 0.001918236821438768 0.0039053107029758394 0.1349696076940745 1.6755047999322414\n"
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     "text": [
      "Epoch 52/200 \t Train Err: 0.5333235822618008 0.002287628563863109 0.005320303003827576 0.14349294803105295 1.6620671227574348\n"
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     "text": [
      "Epoch 53/200 \t Train Err: 0.4952954547479749 0.0010008449244196527 0.003929206512111705 0.1226266548037529 1.5398627035319805\n"
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     "text": [
      "Epoch 55/200 \t Train Err: 0.4630659334361553 0.002020925041506416 0.0043996189124300145 0.11745399446226656 1.4124069288372993\n"
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      "Epoch 59/200 \t Train Err: 0.3944752989336848 0.0025091177740250714 0.005466303970024455 0.10591710731387138 1.1414270270615816\n"
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      "Epoch 60/200 \t Train Err: 0.3850894244387746 0.0022618337816311396 0.00499681286237319 0.10177210683468729 1.1139366328716278\n"
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      "Epoch 61/200 \t Train Err: 0.3536613713949919 0.0016294859724439448 0.003189665607351344 0.09225898724980652 1.0001246724277735\n"
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      "Epoch 62/200 \t Train Err: 0.3431710870936513 0.0014996515783423092 0.0034722756390692666 0.09043321071658283 0.9589487668126822\n"
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     "text": [
      "Epoch 63/200 \t Train Err: 0.3311467822641134 0.001135409189373604 0.0031531365275441203 0.0849773638183251 0.9113363474607468\n"
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     "output_type": "stream",
     "text": [
      "Epoch 64/200 \t Train Err: 0.31347283348441124 0.0010190900329689612 0.003212062452803366 0.08056736667640507 0.8582371436059475\n"
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     "text": [
      "Epoch 65/200 \t Train Err: 0.2970640519633889 0.0010584184110484784 0.002994029131514253 0.07881279999855906 0.8098555449396372\n"
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     "text": [
      "Epoch 66/200 \t Train Err: 0.2887888355180621 0.0012073961215719464 0.0033224597755179275 0.0822362134931609 0.7632680460810661\n"
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     "output_type": "stream",
     "text": [
      "Epoch 67/200 \t Train Err: 0.27321118395775557 0.0009283916206186404 0.0024455750972265378 0.07351583207491785 0.7209800574928522\n"
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     "output_type": "stream",
     "text": [
      "Epoch 68/200 \t Train Err: 0.2642599456012249 0.0024096847391774645 0.004758599683555076 0.07438989775255322 0.6782895382493734\n"
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     "output_type": "stream",
     "text": [
      "Epoch 69/200 \t Train Err: 0.24411699920892715 0.0009143148763541831 0.002384097140748054 0.06588289514183998 0.637260627001524\n"
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     "output_type": "stream",
     "text": [
      "Epoch 70/200 \t Train Err: 0.2355448892340064 0.0011828851866084733 0.00317692070530029 0.07014255912508816 0.5985564198344946\n"
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     "output_type": "stream",
     "text": [
      "Epoch 71/200 \t Train Err: 0.22664254251867533 0.0011206458884771564 0.002485872115357779 0.06011384085286409 0.5786449136212468\n"
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     "output_type": "stream",
     "text": [
      "Epoch 72/200 \t Train Err: 0.21451926324516535 0.0008609092228653026 0.002164756482670782 0.0564098390750587 0.5476357256993651\n"
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     "output_type": "stream",
     "text": [
      "Epoch 73/200 \t Train Err: 0.2044255300424993 0.0010747715696197702 0.00229402982222382 0.05511317937634885 0.5015372652560472\n"
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     "output_type": "stream",
     "text": [
      "Epoch 74/200 \t Train Err: 0.200456322170794 0.001536047003355634 0.003252933354815468 0.05711311026243493 0.48181435000151396\n"
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     "output_type": "stream",
     "text": [
      "Epoch 75/200 \t Train Err: 0.19111737608909607 0.003518748955684714 0.004175829379164497 0.049318774370476604 0.45180629566311836\n"
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     "output_type": "stream",
     "text": [
      "Epoch 76/200 \t Train Err: 0.1808590106666088 0.0013624421417262056 0.002329483479115879 0.04611049557570368 0.4279420841485262\n"
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     "output_type": "stream",
     "text": [
      "Epoch 77/200 \t Train Err: 0.16973466146737337 0.0008994456393338623 0.00207517536728119 0.04326868336647749 0.3952533109113574\n"
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     "output_type": "stream",
     "text": [
      "Epoch 78/200 \t Train Err: 0.1702535841614008 0.0007078972075760248 0.0016872428823262453 0.04189401387702674 0.3984428942203522\n"
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     "output_type": "stream",
     "text": [
      "Epoch 79/200 \t Train Err: 0.16377683635801077 0.0013382542465478764 0.0027396336699894164 0.04209184244973585 0.3769973455928266\n"
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     "output_type": "stream",
     "text": [
      "Epoch 80/200 \t Train Err: 0.1497581924777478 0.0016949967903201468 0.0021530323319893796 0.03568895586067811 0.33177076652646065\n"
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     "output_type": "stream",
     "text": [
      "Epoch 81/200 \t Train Err: 0.14450395992025733 0.001401656336383894 0.002270640025017201 0.03478072857251391 0.317978051956743\n"
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     "output_type": "stream",
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      "Epoch 82/200 \t Train Err: 0.1389398977626115 0.0033198261371580884 0.00435145793017 0.0375910610309802 0.28218549815937877\n"
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     "text": [
      "Epoch 83/200 \t Train Err: 0.12944017979316413 0.0013174645964681986 0.0020839970657107187 0.033180530241224915 0.2718075602315366\n"
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     "output_type": "stream",
     "text": [
      "Epoch 84/200 \t Train Err: 0.1269651602488011 0.001952557386175613 0.002797932973408024 0.030586290056817234 0.2562156552448869\n"
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     "text": [
      "Epoch 85/200 \t Train Err: 0.1262973020784557 0.0034579528983158525 0.004187718592220335 0.03295240050647408 0.26100850058719516\n"
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      "Epoch 88/200 \t Train Err: 0.1069438960403204 0.0007534859851148212 0.0015284864657587605 0.02535622369032353 0.20643022749572992\n"
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      "Epoch 89/200 \t Train Err: 0.1019233949482441 0.0009516884665572434 0.001511421851319028 0.024724961316678673 0.18514555739238858\n"
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      "Epoch 90/200 \t Train Err: 0.09508711867965758 0.0008840842388053716 0.0013085861846775515 0.022952270082896575 0.17402319004759192\n"
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      "Epoch 91/200 \t Train Err: 0.09273395943455398 0.0008308457795465074 0.0014873276431899285 0.020763451553648338 0.1700441548600793\n"
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      "Epoch 92/200 \t Train Err: 0.08730011875741184 0.000720000532510312 0.0011607171036303043 0.020305267593357712 0.15585350175388157\n"
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     "text": [
      "Epoch 93/200 \t Train Err: 0.08319679484702647 0.0007787930949234578 0.0012370077938612667 0.018640907626831904 0.14242116385139525\n"
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      "Epoch 94/200 \t Train Err: 0.08445112314075232 0.002024608746069134 0.0023303976749957656 0.0193185547250323 0.14395680534653366\n"
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     "text": [
      "Epoch 95/200 \t Train Err: 0.0859984909184277 0.001967401332422014 0.002160638517125335 0.02076483884593472 0.14143188181333244\n"
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     "text": [
      "Epoch 96/200 \t Train Err: 0.07500915951095521 0.0011493155893731455 0.0014125802363196271 0.019469743536319584 0.12187899858690798\n"
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     "output_type": "stream",
     "text": [
      "Epoch 97/200 \t Train Err: 0.07068800239358097 0.0006672579925179889 0.0009655766079958994 0.01619866766850464 0.11232927581295371\n"
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     "output_type": "stream",
     "text": [
      "Epoch 98/200 \t Train Err: 0.06899663107469678 0.0029092533500261197 0.002759141156275291 0.01706777891376987 0.10129301541019231\n"
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     "output_type": "stream",
     "text": [
      "Epoch 99/200 \t Train Err: 0.06587723165284842 0.0010087788145938248 0.0015239717367876437 0.015540250373305753 0.10049017099663615\n"
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     "output_type": "stream",
     "text": [
      "Epoch 100/200 \t Train Err: 0.06758000492118299 0.0019963337617809884 0.0021085088956169784 0.018880482559325173 0.10054084449075162\n"
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     "output_type": "stream",
     "text": [
      "Epoch 101/200 \t Train Err: 0.06307036883663386 0.0012753937157867767 0.0016400622589571867 0.015593403164530173 0.09352678328286856\n"
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     "output_type": "stream",
     "text": [
      "Epoch 102/200 \t Train Err: 0.060316542629152536 0.0006818027850385988 0.0010231798241875367 0.013433107393211685 0.09090642654336989\n"
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     "output_type": "stream",
     "text": [
      "Epoch 103/200 \t Train Err: 0.060421090107411146 0.0027040049144488876 0.0027294684268781566 0.01441186985175591 0.0847808476537466\n"
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     "output_type": "stream",
     "text": [
      "Epoch 104/200 \t Train Err: 0.05678413005080074 0.001134833364176302 0.001431436577149725 0.012649833428440616 0.0780830224393867\n"
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     "output_type": "stream",
     "text": [
      "Epoch 105/200 \t Train Err: 0.05805800168309361 0.003231210850572097 0.003441706239755149 0.015121193689992651 0.0752038118080236\n"
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     "output_type": "stream",
     "text": [
      "Epoch 106/200 \t Train Err: 0.0547659246949479 0.002454680496839501 0.002737534454809065 0.013384125923039392 0.07274198567029089\n"
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     "output_type": "stream",
     "text": [
      "Epoch 107/200 \t Train Err: 0.04927568801213056 0.0005825197017657047 0.0007837470261620183 0.010716092379880138 0.05965513514820486\n"
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     "output_type": "stream",
     "text": [
      "Epoch 108/200 \t Train Err: 0.05318018130492419 0.0013841798254361493 0.0015562751477773418 0.01211979822255671 0.07018060437985696\n"
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     "output_type": "stream",
     "text": [
      "Epoch 109/200 \t Train Err: 0.051697830320335925 0.0010280100987074547 0.001098815353543614 0.010801961965626106 0.06942529141088016\n"
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     "output_type": "stream",
     "text": [
      "Epoch 110/200 \t Train Err: 0.04692836094181985 0.0008446434776487877 0.0008702427021489711 0.01032250898424536 0.05933116714004427\n"
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     "output_type": "stream",
     "text": [
      "Epoch 111/200 \t Train Err: 0.044668186688795686 0.0011062668152135302 0.0010394338919468282 0.009862772989436053 0.054550442087929696\n"
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      "Epoch 118/200 \t Train Err: 0.03628927277168259 0.00044126920215603604 0.0005619980265691993 0.007773650635499507 0.0384644401492551\n"
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      "Epoch 119/200 \t Train Err: 0.03890394070185721 0.001729899499878229 0.001493205483257043 0.00798933092301013 0.041969388112192973\n"
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      "Epoch 120/200 \t Train Err: 0.03712784848175943 0.0007071991701650404 0.0008030551111914974 0.007690385325986426 0.04134120314847678\n"
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      "Epoch 121/200 \t Train Err: 0.03516055888030678 0.0004485433178160747 0.0006193339004312293 0.006722165890096221 0.03440287291596178\n"
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      "Epoch 122/200 \t Train Err: 0.03431048977654427 0.0007996012898274785 0.000579838467274385 0.007459181011654437 0.03256209765095264\n"
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      "Epoch 123/200 \t Train Err: 0.0351686873473227 0.0006591397341253469 0.0006555682361977233 0.006613661207666155 0.037333723666961305\n"
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      "Epoch 124/200 \t Train Err: 0.03355215600458905 0.0002945618743979139 0.0004387611575111805 0.006839323941676412 0.034134260716200515\n"
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     "output_type": "stream",
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      "Epoch 126/200 \t Train Err: 0.03119118546601385 0.0005014674925405416 0.0007099974022821698 0.0060376596375135705 0.02815229452244239\n"
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     "output_type": "stream",
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     "output_type": "stream",
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      "Epoch 128/200 \t Train Err: 0.030603406310547143 0.0006404905841463915 0.0006846741659956024 0.006221181378350593 0.025205713599461887\n"
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      "Epoch 129/200 \t Train Err: 0.029333731392398477 0.0005602014119858723 0.000642365913336107 0.006284213814069517 0.02611703903221496\n"
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     "output_type": "stream",
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      "Epoch 130/200 \t Train Err: 0.028047623636666685 0.0008307177447477443 0.0007543345300291548 0.006256156790186651 0.02223676520952722\n"
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      "Epoch 131/200 \t Train Err: 0.02852347370935604 0.0005703339811589103 0.0009201887021390576 0.005433428774267668 0.025521833274979144\n"
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      "Epoch 132/200 \t Train Err: 0.028580970887560397 0.0005810092270621681 0.0005398729952048598 0.005319414980476722 0.027794180728960782\n"
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      "Epoch 147/200 \t Train Err: 0.020571442728396505 0.00044352215627441183 0.0005010924719499599 0.003667172095447313 0.014751498597888713\n"
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      "Epoch 157/200 \t Train Err: 0.01679313532076776 0.00016895983264930692 0.00023369373479908973 0.0025660492974566296 0.013730080009736412\n"
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      "Epoch 159/200 \t Train Err: 0.020110780431423336 0.000910040460212258 0.000834133242506141 0.004305297403334407 0.013905958928830842\n"
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      "Epoch 160/200 \t Train Err: 0.019773374748183414 0.0009348806023581346 0.0008922487559175352 0.003975474533945089 0.01617197653695257\n"
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      "Epoch 161/200 \t Train Err: 0.017475509172072634 0.00046213520988658274 0.0005404102516877174 0.003040906784008257 0.014273262689584953\n"
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      "Epoch 162/200 \t Train Err: 0.015029630245408043 0.0001904235211895866 0.00021963075801068044 0.002168249424357782 0.010509091553103644\n"
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     "output_type": "stream",
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     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 177/200 \t Train Err: 0.014368639851454645 0.00044816699323746434 0.00045128344186196045 0.002814159254739934 0.008726180123630911\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
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      "\n",
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     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 178/200 \t Train Err: 0.014014390297234058 0.0006206710049809772 0.00046763980253672344 0.002513490486308001 0.008434324339646082\n"
     ]
    },
    {
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      "\n",
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     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 179/200 \t Train Err: 0.011972312582656741 0.00018721630084428398 0.00029149899171443394 0.0016975146063487045 0.007282969920481719\n"
     ]
    },
    {
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     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 180/200 \t Train Err: 0.01289494353113696 0.00020095304734013553 0.000251299350793488 0.0017014556851790985 0.010998782015121833\n"
     ]
    },
    {
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     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 181/200 \t Train Err: 0.013175164320273325 0.000340911025944024 0.00035113496642225073 0.002221787386588403 0.008535892283987323\n"
     ]
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     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 182/200 \t Train Err: 0.012816024391213432 0.0003103266114408143 0.00028728846336889546 0.0020579011925292434 0.00718217800522325\n"
     ]
    },
    {
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     ]
    },
    {
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     "output_type": "stream",
     "text": [
      "Epoch 183/200 \t Train Err: 0.01449834939558059 0.0005017787769361348 0.0005829150882163958 0.002624640494104824 0.012894641169850729\n"
     ]
    },
    {
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     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 184/200 \t Train Err: 0.01308623020304367 0.0003065749452275668 0.0002674301056231343 0.0018643831190274796 0.009087016163903172\n"
     ]
    },
    {
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      "\n",
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     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 185/200 \t Train Err: 0.011409497121348977 0.00029326116043648653 0.0003596148741280558 0.001658103732552263 0.007742758548488382\n"
     ]
    },
    {
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      "\n",
      "00%|███████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████| 32/32 [00:30<00:00,  1.05it/s]"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 186/200 \t Train Err: 0.010975291283102706 0.00022339119993830536 0.00027696753636519134 0.0014451522856688825 0.0054481543454585335\n"
     ]
    },
    {
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     "output_type": "stream",
     "text": [
      "\n",
      "00%|███████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████| 32/32 [00:30<00:00,  1.04it/s]"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 187/200 \t Train Err: 0.010257220594212413 0.00023113110682970728 0.0002813607433154175 0.001320708272032789 0.005819132793249082\n"
     ]
    },
    {
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      "\n",
      "00%|███████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████| 32/32 [00:30<00:00,  1.06it/s]"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 188/200 \t Train Err: 0.012054045568220317 0.000388129293298789 0.0004733345151635149 0.001878096192740486 0.00747043985700202\n"
     ]
    },
    {
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      "\n",
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     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 189/200 \t Train Err: 0.012783893820596859 0.00031030693571665324 0.00032841233428371197 0.0017014134382407065 0.006328187721919676\n"
     ]
    },
    {
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     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 190/200 \t Train Err: 0.01047908267355524 0.000252351564199671 0.0003104653961827353 0.0015047969627630664 0.00647446524692441\n"
     ]
    },
    {
     "name": "stderr",
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      "\n",
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     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 191/200 \t Train Err: 0.01059192547108978 0.00032810493928536744 0.00036611615337278636 0.0015751951596030267 0.005902428454646724\n"
     ]
    },
    {
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     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 192/200 \t Train Err: 0.011238765117013827 0.0005425223790780365 0.0005242744022098123 0.0020095554464205634 0.006199448111232186\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      " 75%|█████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████▎                                                 | 24/32 [00:22<00:07,  1.03it/s]"
     ]
    }
   ],
   "source": [
    "while epoch < NEPOCHS:\n",
    "    model.train()\n",
    "    with open(f\"data-new/{epoch}.pickle\", \"rb\") as f:\n",
    "        pickled_stuff = pickle.load(f)\n",
    "    data = pickled_stuff[\"data\"].to(device)\n",
    "    label = pickled_stuff[\"labels\"].to(device).to(torch.float32)\n",
    "    padding = pickled_stuff[\"padding\"].to(device)\n",
    "    dataset = TensorDataset(data, label, padding)\n",
    "    loader = DataLoader(dataset, batch_size=BSZ)\n",
    "    for batch_src, batch_labels, batch_padding_mask in tqdm(loader):\n",
    "        optimizer.zero_grad()\n",
    "        output = model(batch_src, batch_padding_mask)\n",
    "        loss = criterion(output.squeeze(1), batch_labels)\n",
    "        loss.backward()\n",
    "        optimizer.step()\n",
    "        \n",
    "        train_err.append(loss.item())\n",
    "        len1.append(criterion(output[batch_labels == 1].squeeze(1), batch_labels[batch_labels == 1]).item())\n",
    "        len2.append(criterion(output[batch_labels == 2].squeeze(1), batch_labels[batch_labels == 2]).item())\n",
    "        len3.append(criterion(output[batch_labels == 3].squeeze(1), batch_labels[batch_labels == 3]).item())\n",
    "        leninf.append(criterion(output[batch_labels == MAX_VTXS].squeeze(1), batch_labels[batch_labels == MAX_VTXS]).item())\n",
    "        \n",
    "        with open('loss', 'a') as f:\n",
    "            f.write(f\"{train_err[-1]} {len1[-1]} {len2[-1]} {len3[-1]} {leninf[-1]}\\n\")\n",
    "    epoch_err = lambda l: sum(l[-EPOCH_SIZE // BSZ:]) / EPOCH_SIZE * BSZ\n",
    "    print(f\"Epoch {epoch}/{NEPOCHS} \\t Train Err: {epoch_err(train_err)} {epoch_err(len1)} {epoch_err(len2)} {epoch_err(len3)} {epoch_err(leninf)}\")\n",
    "\n",
    "    epoch += 1\n",
    "    if epoch % 10 == 0:\n",
    "        torch.save(model.state_dict(), f\"checkpoints/model_weights_{epoch}.pth\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "execution_state": "idle",
   "metadata": {},
   "outputs": [],
   "source": [
    "def evaluate(data):\n",
    "    model.eval()\n",
    "    test_loss = 0\n",
    "    with torch.no_grad():\n",
    "        batch_src, batch_labels, batch_padding_mask = map(lambda x: x.to(device), data)\n",
    "        output = model(batch_src, batch_padding_mask)\n",
    "        loss = criterion(output.squeeze(1), batch_labels)\n",
    "    return output.detach(), batch_labels.detach().to(torch.uint8), loss.item()\n",
    "\n",
    "def mkhist(data):\n",
    "    y, x, loss = evaluate(data)\n",
    "    print(loss)\n",
    "    cnts = torch.bincount(x)\n",
    "    weights = [1/cnts[i.item()].item() for i in x] # normalize by label count\n",
    "    fig, ax = plt.subplots()\n",
    "    h = ax.hist2d(x.cpu().numpy().flatten(), y.cpu().numpy().flatten(), weights=weights, bins=[15,50], norm=mpl.colors.LogNorm())\n",
    "    ax.set_xlabel(\"Label\")\n",
    "    ax.set_ylabel(\"Model Output\")\n",
    "    fig.colorbar(h[3], ax=ax)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/home/sipb/.venv/lib64/python3.12/site-packages/torch/nn/modules/transformer.py:871: UserWarning: Memory Efficient attention on Navi31 GPU is still experimental. Enable it with TORCH_ROCM_AOTRITON_ENABLE_EXPERIMENTAL=1. (Triggered internally at ../aten/src/ATen/native/transformers/hip/sdp_utils.cpp:269.)\n",
      "  return torch._transformer_encoder_layer_fwd(\n",
      "/home/sipb/.venv/lib64/python3.12/site-packages/torch/nn/modules/transformer.py:871: UserWarning: Attempting to use hipBLASLt on an unsupported architecture! Overriding blas backend to hipblas (Triggered internally at ../aten/src/ATen/Context.cpp:296.)\n",
      "  return torch._transformer_encoder_layer_fwd(\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "0.031184175983071327"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "evaluate(mkbatch(BSZ))[2]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "with open('loss') as f:\n",
    "    losses = torch.tensor([list(map(float, l.split())) for l in f.readlines()])\n",
    "plt.plot(torch.log(losses[:, 0]), label='Train', color='blue')\n",
    "plt.xlabel('Epochs')\n",
    "plt.ylabel('Log MSE')\n",
    "with open(\"plots/train-loss.html\", \"w\") as f:\n",
    "    mpld3.save_html(plt.gcf(), f)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {
    "id": "LoGEmM5lH7_A"
   },
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "mkhist(mkbatch(BSZ))\n",
    "plt.show()\n",
    "with open(\"plots/train-hist.html\", \"w\") as f:\n",
    "    mpld3.save_html(plt.gcf(), f)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "plt.suptitle('Log MSE vs Epochs')\n",
    "plt.plot(torch.log(torch.tensor(losses[:, 0])), label='Train', color='blue')\n",
    "plt.plot(torch.log(torch.tensor(losses[:, 1])), label='Len 1', color='red')\n",
    "plt.plot(torch.log(torch.tensor(losses[:, 2])), label='Len 2', color='green')\n",
    "plt.plot(torch.log(torch.tensor(losses[:, 3])), label='Len 3', color='yellow')\n",
    "plt.plot(torch.log(torch.tensor(losses[:, 4])), label='Len 15', color='magenta')\n",
    "plt.xlabel('Epochs')\n",
    "plt.ylabel('Log MSE')\n",
    "plt.legend()\n",
    "with open(\"plots/train-lens.html\", \"w\") as f:\n",
    "    mpld3.save_html(plt.gcf(), f)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "LC6Xv3YfC0Rm"
   },
   "source": [
    "# Step 5: Fine Tune"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [],
   "source": [
    "N_TUNE_EPOCHS = 100\n",
    "TUNE_LR = 1e-5\n",
    "\n",
    "tune_criterion = nn.MSELoss()\n",
    "tune_optimizer = torch.optim.Adam(model.parameters(), lr=TUNE_LR)\n",
    "\n",
    "tune_train_err = []\n",
    "\n",
    "# clear loss file\n",
    "open('tune_loss', 'w').close()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 0/100 \t Train Err: 0.4553\n",
      "Epoch 1/100 \t Train Err: 0.2545\n",
      "Epoch 2/100 \t Train Err: 0.2368\n",
      "Epoch 3/100 \t Train Err: 0.2138\n",
      "Epoch 4/100 \t Train Err: 0.1898\n",
      "Epoch 5/100 \t Train Err: 0.1931\n",
      "Epoch 6/100 \t Train Err: 0.1948\n",
      "Epoch 7/100 \t Train Err: 0.1847\n",
      "Epoch 8/100 \t Train Err: 0.1638\n",
      "Epoch 9/100 \t Train Err: 0.1490\n",
      "Epoch 10/100 \t Train Err: 0.1457\n",
      "Epoch 11/100 \t Train Err: 0.1509\n",
      "Epoch 12/100 \t Train Err: 0.1537\n",
      "Epoch 13/100 \t Train Err: 0.1472\n",
      "Epoch 14/100 \t Train Err: 0.1369\n",
      "Epoch 15/100 \t Train Err: 0.1304\n",
      "Epoch 16/100 \t Train Err: 0.1257\n",
      "Epoch 17/100 \t Train Err: 0.1201\n",
      "Epoch 18/100 \t Train Err: 0.1160\n",
      "Epoch 19/100 \t Train Err: 0.1064\n",
      "Epoch 20/100 \t Train Err: 0.1026\n",
      "Epoch 21/100 \t Train Err: 0.0964\n",
      "Epoch 22/100 \t Train Err: 0.1165\n",
      "Epoch 23/100 \t Train Err: 0.0990\n",
      "Epoch 24/100 \t Train Err: 0.0691\n",
      "Epoch 25/100 \t Train Err: 0.0633\n",
      "Epoch 26/100 \t Train Err: 0.0552\n",
      "Epoch 27/100 \t Train Err: 0.0505\n",
      "Epoch 28/100 \t Train Err: 0.0429\n",
      "Epoch 29/100 \t Train Err: 0.0376\n",
      "Epoch 30/100 \t Train Err: 0.0309\n",
      "Epoch 31/100 \t Train Err: 0.0252\n",
      "Epoch 32/100 \t Train Err: 0.0233\n",
      "Epoch 33/100 \t Train Err: 0.0219\n",
      "Epoch 34/100 \t Train Err: 0.0196\n",
      "Epoch 35/100 \t Train Err: 0.0186\n",
      "Epoch 36/100 \t Train Err: 0.0181\n",
      "Epoch 37/100 \t Train Err: 0.0189\n",
      "Epoch 38/100 \t Train Err: 0.0174\n",
      "Epoch 39/100 \t Train Err: 0.0141\n",
      "Epoch 40/100 \t Train Err: 0.0138\n",
      "Epoch 41/100 \t Train Err: 0.0136\n",
      "Epoch 42/100 \t Train Err: 0.0124\n",
      "Epoch 43/100 \t Train Err: 0.0121\n",
      "Epoch 44/100 \t Train Err: 0.0109\n",
      "Epoch 45/100 \t Train Err: 0.0107\n",
      "Epoch 46/100 \t Train Err: 0.0105\n",
      "Epoch 47/100 \t Train Err: 0.0103\n",
      "Epoch 48/100 \t Train Err: 0.0088\n",
      "Epoch 49/100 \t Train Err: 0.0089\n",
      "Epoch 50/100 \t Train Err: 0.0092\n",
      "Epoch 51/100 \t Train Err: 0.0078\n",
      "Epoch 52/100 \t Train Err: 0.0075\n",
      "Epoch 53/100 \t Train Err: 0.0068\n",
      "Epoch 54/100 \t Train Err: 0.0066\n",
      "Epoch 55/100 \t Train Err: 0.0065\n",
      "Epoch 56/100 \t Train Err: 0.0055\n",
      "Epoch 57/100 \t Train Err: 0.0056\n",
      "Epoch 58/100 \t Train Err: 0.0052\n",
      "Epoch 59/100 \t Train Err: 0.0055\n",
      "Epoch 60/100 \t Train Err: 0.0048\n",
      "Epoch 61/100 \t Train Err: 0.0050\n",
      "Epoch 62/100 \t Train Err: 0.0049\n",
      "Epoch 63/100 \t Train Err: 0.0051\n",
      "Epoch 64/100 \t Train Err: 0.0047\n",
      "Epoch 65/100 \t Train Err: 0.0045\n",
      "Epoch 66/100 \t Train Err: 0.0041\n",
      "Epoch 67/100 \t Train Err: 0.0038\n",
      "Epoch 68/100 \t Train Err: 0.0036\n",
      "Epoch 69/100 \t Train Err: 0.0035\n",
      "Epoch 70/100 \t Train Err: 0.0035\n",
      "Epoch 71/100 \t Train Err: 0.0032\n",
      "Epoch 72/100 \t Train Err: 0.0032\n",
      "Epoch 73/100 \t Train Err: 0.0030\n",
      "Epoch 74/100 \t Train Err: 0.0031\n",
      "Epoch 75/100 \t Train Err: 0.0026\n",
      "Epoch 76/100 \t Train Err: 0.0030\n",
      "Epoch 77/100 \t Train Err: 0.0024\n",
      "Epoch 78/100 \t Train Err: 0.0026\n",
      "Epoch 79/100 \t Train Err: 0.0028\n",
      "Epoch 80/100 \t Train Err: 0.0026\n",
      "Epoch 81/100 \t Train Err: 0.0023\n",
      "Epoch 82/100 \t Train Err: 0.0022\n",
      "Epoch 83/100 \t Train Err: 0.0022\n",
      "Epoch 84/100 \t Train Err: 0.0021\n",
      "Epoch 85/100 \t Train Err: 0.0023\n",
      "Epoch 86/100 \t Train Err: 0.0023\n",
      "Epoch 87/100 \t Train Err: 0.0020\n",
      "Epoch 88/100 \t Train Err: 0.0020\n",
      "Epoch 89/100 \t Train Err: 0.0021\n",
      "Epoch 90/100 \t Train Err: 0.0020\n",
      "Epoch 91/100 \t Train Err: 0.0019\n",
      "Epoch 92/100 \t Train Err: 0.0017\n",
      "Epoch 93/100 \t Train Err: 0.0019\n",
      "Epoch 94/100 \t Train Err: 0.0019\n",
      "Epoch 95/100 \t Train Err: 0.0015\n",
      "Epoch 96/100 \t Train Err: 0.0016\n",
      "Epoch 97/100 \t Train Err: 0.0017\n",
      "Epoch 98/100 \t Train Err: 0.0015\n",
      "Epoch 99/100 \t Train Err: 0.0015\n"
     ]
    }
   ],
   "source": [
    "for epoch in range(N_TUNE_EPOCHS):\n",
    "    model.train()\n",
    "    train_loss = 0\n",
    "    batch_src, batch_labels, batch_padding_mask = map(lambda x: x.to(device), mkbatch(BSZ, large=False, target=\"onpath\"))\n",
    "    optimizer.zero_grad()\n",
    "    output = model(batch_src, batch_padding_mask)\n",
    "    loss = criterion(output.squeeze(1), batch_labels)\n",
    "    train_loss = loss.item()\n",
    "    loss.backward()\n",
    "    optimizer.step()\n",
    "    \n",
    "    tune_train_err.append(train_loss)\n",
    "    with open('tune_loss', 'a') as f:\n",
    "        f.write(f\"{train_loss}\\n\")\n",
    "    print(f\"Epoch {epoch}/{N_TUNE_EPOCHS} \\t Train Err: {train_loss:.4f}\")\n",
    "\n",
    "    if epoch % 10 == 9:\n",
    "        torch.save(model.state_dict(), f\"checkpoints/tune_model_weights_{epoch + 1}.pth\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.001733972690999508"
      ]
     },
     "execution_count": 19,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "evaluate(mkbatch(BSZ, large=False, target=\"onpath\"))[2]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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1no53QLGYDg4AgG1sDTdFRUXKyspSenp6SUFhYUpPT9fKlStPed4jjzyixMRE3XTTTb/5HYWFhcrPz/fbgiU8XIqIsJ7TcgMAgD1sDTd79uxRcXGxkpKS/PYnJSUpJyfnpOcsX75cL7zwgmbNmlWm75g0aZLi4+N9W0pKSkBqPxUGFQMAYC/bu6XKo6CgQNdff71mzZqlhISEMp0zbtw45eXl+bYdO3YEtUbCDQAA9oqw88sTEhIUHh6u3Nxcv/25ubmqV69eqeO/++47bd26VQMGDPDt83g8kqSIiAht2bJFzZs39zsnMjJSkScOhgkywg0AAPayteXG7XYrNTVVmZmZvn0ej0eZmZlKS0srdXzLli21adMmZWdn+7Yrr7xSPXv2VHZ2dtC7nMqCcAMAgL1sbbmRpLFjxyojI0MdO3ZUp06dNHXqVB08eFDDhw+XJA0bNkwNGjTQpEmTFBUVpdatW/udX7NmTUkqtd8u3DwTAAB72R5uBg8erN27d2v8+PHKyclR+/bttWTJEt8g4+3btyssrPIMDaLlBgAAe7mMMcbuIkIpPz9f8fHxysvLU1xcXMA//6KLpBUrpH/9S/rDHwL+8QAAVEnl+ftdeZpEKglabgAAsBfhJsAINwAA2ItwE2CEGwAA7EW4CTBmSwEAYC/CTYDRcgMAgL0INwHmXQyZcAMAgD0INwFGyw0AAPYi3AQY4QYAAHsRbgKMcAMAgL0INwFGuAEAwF6EmwBjKjgAAPYi3AQYs6UAALAX4SbA6JYCAMBehJsAI9wAAGAvwk2AEW4AALAX4SbACDcAANiLcBNgzJYCAMBehJsAY7YUAAD2ItwEGN1SAADYi3ATYIQbAADsRbgJMMINAAD2ItwE2Inhxhi7qwEAoOoh3ASYN9xI0tGjdlYCAEDVRLgJsBPDDV1TAACEHuEmwNzukueEGwAAQo9wE2AuF2vdAABgJ8JNEDBjCgAA+xBugoBwAwCAfQg3QUC4AQDAPoSbIODmmQAA2IdwEwQMKAYAwD6EmyCgWwoAAPsQboKAcAMAgH0IN0FAuAEAwD6EmyAg3AAAYB/CTRAwWwoAAPsQboKA2VIAANiHcBMEdEsBAGAfwk0QEG4AALAP4SYICDcAANiHcBMEhBsAAOxDuAkCZksBAGAfwk0Q0HIDAIB9CDdBwFRwAADsQ7gJAlpuAACwD+EmCAg3AADYh3ATBIQbAADsQ7gJAsINAAD2IdwEAVPBAQCwD+EmCJgtBQCAfQg3QUC3FAAA9iHcBAHhBgAA+xBugoBwAwCAfQg3QUC4AQDAPoSbIPCGm6NHJY/H7moAAKhaCDdB4J0tJaaDAwAQcoSbIPC23IiuKQAAQo5wEwQREVLY8StLuAEAILQIN0HgcjGoGAAAuxBugoRwAwCAPQg3QUK4AQDAHoSbIPHOmGK2FAAAoUW4CRJabgAAsEeFCDfPPPOMmjRpoqioKHXu3Flr1qw55bFvvfWWOnbsqJo1a6pGjRpq3769Xn311ZDWWxaEGwAA7GF7uJk/f77Gjh2rCRMmaP369WrXrp169+6tXbt2nfT42rVr64EHHtDKlSu1ceNGDR8+XMOHD9f7778f8tpPh3ADAIA9bA83Tz75pG655RYNHz5cF1xwgWbOnKnq1atr9uzZJz2+R48euvrqq3X++eerefPmGjNmjNq2bavly5eHvPbTIdwAAGAPW8NNUVGRsrKylJ6eXlJQWJjS09O1cuXK3zzfGKPMzExt2bJFl1xyyUmPKSwsVH5+vt8WCoQbAADsYWu42bNnj4qLi5WUlOS3PykpSTk5Oac8Ly8vTzExMXK73erfv7+mTZumXr16nfTYSZMmKT4+3relpKQE/HecjDfcMFsKAIDQsr1b6kzExsYqOztba9eu1cSJEzV27Fh99NFHJz123LhxysvL8207duwISY3eqeC03AAAEFoRdn55QkKCwsPDlZub67c/NzdX9erVO+V5YWFhOueccyRJ7du31+bNmzVp0iT16NGj1LGRkZGKPPE23SFCtxQAAPawteXG7XYrNTVVmZmZvn0ej0eZmZlKS0sr8+d4PB4VVrD+H8INAAD2sLXlRpLGjh2rjIwMdezYUZ06ddLUqVN18OBBDR8+XJI0bNgwNWjQQJMmTZKOj6Hp2LGjmjdvrsLCQr333nt69dVXNWPGDJt/iT/CDQAA9rA93AwePFi7d+/W+PHjlZOTo/bt22vJkiW+Qcbbt29XWFhJA9PBgwc1atQo/fjjj4qOjlbLli312muvafDgwTb+itIINwAA2MNljDF2FxFK+fn5io+PV15enuLi4oL2PQ8/LD30kDRypPTss0H7GgAAqoTy/P2ulLOlKgNmSwEAYA/CTZDQLQUAgD0IN0FCuAEAwB6EmyAh3AAAYA/CTZAQbgAAsAfhJkgINwAA2INwEyTe2VIVbOFkAAAcj3ATJLTcAABgD8JNkBBuAACwB+EmSAg3AADYg3ATJIQbAADsQbgJEsINAAD2INwECbOlAACwB+EmSLwtN8XF0rFjdlcDAEDVQbgJEm+4EV1TAACEFOEmSLzdUiLcAAAQUoSbIAkPl6pVs54TbgAACB3CTRAxYwoAgNAj3ASRN9wwYwoAgNAh3ASRd9wNLTcAAIQO4SaI6JYCACD0CDdBRLgBACD0CDdBRLgBACD0CDdBRLgBACD0CDdBRLgBACD0CDdBxM0zAQAIPcJNENWoYT3u2mV3JQAAVB2EmyC65BLr8a237K4EAICqg3ATRNdea91jau1a6dtv7a4GAICqgXATRImJ0mWXWc/nzrW7GgAAqgbCTZD98Y/W49y5kjF2VwMAgPMRboLs6qutWVObN0sbN9pdDQAAzke4CbK4OKl/f+v5nDl2VwMAgPMRbkJgyBDrcd48yeOxuxoAAJyNcBMC/ftLsbHS9u3SypV2VwMAgLMRbkIgOtoaeyO6pgAACLpyhZspU6bo8OHDvtcrVqxQ4Qn3FigoKNCoUaMCW6FDeLumFiyQjh2zuxoAAJzLZUzZJyiHh4fr559/VmJioiQpLi5O2dnZatasmSQpNzdXycnJKi4uDl7FZyk/P1/x8fHKy8tTXFxcyL736FGpQQNp925pyRKpd++QfTUAAJVeef5+l6vl5tc5qBy5qMqrVs1asVgs6AcAQFAx5iaEvF1Tb79tteQAAIDAI9yEUNeuUp06Un6+tGaN3dUAAOBMEeU94Z///KdiYmIkSceOHdNLL72khIQE6fiAYpxaWJh1r6k33pCWLpW6dbO7IgAAnKdcA4qbNGkil8v1m8f98MMPZ1tX0Ng1oNjrn/+UbrnFCjbLl4f86wEAqJTK8/e7XC03W7duPdvaqrz0dOtx1Sqre8qGfAUAgKMx5ibEmjSRmjeXiouljz+2uxoAAJynXOFm5cqVWrRokd++V155RU2bNlViYqJuvfVWv0X9cHK9elmPH3xgdyUAADhPucLNI488oi+++ML3etOmTbrpppuUnp6u++67T++++64mTZoUjDodxds1tXSp3ZUAAOA85Qo32dnZuuyyy3yv582bp86dO2vWrFkaO3asnn76ab3xxhvBqNNRLr1UcrmkzZuln36yuxoAAJylXOFm3759SkpK8r3++OOP1bdvX9/rCy+8UDt27AhshQ5Uq5bUsaP1nK4pAAACq1zhJikpyTfNu6ioSOvXr1eXLl187xcUFKhatWqBr9KBGHcDAEBwlCvc9OvXT/fdd58+/fRTjRs3TtWrV9fFF1/se3/jxo1q3rx5MOp0HO+4mw8+kLhFFwAAgVOucPO3v/1NERER6t69u2bNmqXnn39ebrfb9/7s2bN1+eWXB6NOx+naVYqOlnJypBPGaAMAgLNUrkX8EhIS9MknnygvL08xMTEKDw/3e3/BggWKjY0NdI2OFBkpXXKJ9P77VutN69Z2VwQAgDOUK9zceOONZTpu9uzZZ1pPlZKeboWbpUulu+6yuxoAAJyhXOHmpZdeUuPGjdWhQweV45ZUOAXvoOKPP5aKiqQTevgAAMAZKle4GTlypObOnasffvhBw4cP15/+9CfVrl07eNU5XJs2Ut260u7d0urV0gljswEAwBkq14DiZ555Rj///LP+8pe/6N1331VKSooGDRqk999/n5acMxAWJnnXRGS1YgAAAqPcN86MjIzUkCFDtHTpUn355Zdq1aqVRo0apSZNmujAgQPBqdLBvOFm2TK7KwEAwBnO6q7gYWFhcrlcMsaouLg4cFVVIZdeaj2uWiUdPGh3NQAAVH7lDjeFhYWaO3euevXqpXPPPVebNm3S9OnTtX37dsXExASnSgdr2lRq3Fg6dkxavtzuagAAqPzKFW5GjRql+vXra/Lkybriiiu0Y8cOLViwQP369VNY2Fk1AlVZLpfUs6f1nK4pAADOnsuUYyRwWFiYGjVqpA4dOsjlcp3yuLfeeitQ9QVcfn6+4uPjlZeXp7i4OLvLkSS9+qo0bJh04YXSmjV2VwMAQMVTnr/f5ZoKPmzYsNOGGpwZb8tNVpaUlyfFx9tdEQAAlVe5F/ELhmeeeUaPPfaYcnJy1K5dO02bNk2dOnU66bGzZs3SK6+8os8//1ySlJqaqr///e+nPL4yaNhQatFC+uYb6ZNPpAED7K4IAIDKy/aBMvPnz9fYsWM1YcIErV+/Xu3atVPv3r21a9eukx7/0UcfaciQIVq2bJlWrlyplJQUXX755frpp59CXnsgeWdNffih3ZUAAFC5lWvMTTB07txZF154oaZPny5J8ng8SklJ0ejRo3Xffff95vnFxcWqVauWpk+frmHDhv3m8RVxzI0kzZ8vXXed1K6dlJ1tdzUAAFQs5fn7bWvLTVFRkbKyspSenl5SUFiY0tPTtXLlyjJ9xqFDh3T06NFT3gaisLBQ+fn5fltF1KOH9bhhg7Rnj93VAABQedkabvbs2aPi4mIlJSX57U9KSlJOTk6ZPuPee+9VcnKyX0A60aRJkxQfH+/bUlJSAlJ7oCUlSa1bW88/+sjuagAAqLxsH3NzNiZPnqx58+bp7bffVlRU1EmPGTdunPLy8nzbjh07Ql5nWbHeDQAAZ8/WcJOQkKDw8HDl5ub67c/NzVW9evVOe+7jjz+uyZMn67///a/atm17yuMiIyMVFxfnt1VU5RlU/NNP0rZtQS8JAIBKx9Zw43a7lZqaqszMTN8+j8ejzMxMpaWlnfK8KVOm6G9/+5uWLFmijh07hqja4Ove3Vqx+KuvpJ07/d8rLJQyM6V77pHatLGmj597rrRunV3VAgBQMdneLTV27FjNmjVLL7/8sjZv3qyRI0fq4MGDGj58uHR84cBx48b5jn/00Uf14IMPavbs2WrSpIlycnKUk5PjiDuS16oldehgPfeOu9m3T7r/fikhQUpPlx5/XDq+xI+KiqSRIyXuWQoAQIlyLeIXDIMHD9bu3bs1fvx45eTkqH379lqyZIlvkPH27dv97ls1Y8YMFRUV6ZprrvH7nAkTJuihhx4Kef2Bduml0vr10qJF0tat0pQp1qrFklSvntSnj7W1aSOlpVktN7NmSSNG2F05AAAVg+3r3IRaRV3nxmvxYqlfP/99rVtLEydaKxefePeLadOkO++UataUtmyREhNDXi4AACFRada5QWkXXSRFR1vPmzWTXnvNWtTvyiv9g41kdUl16CDt3y/de68t5QIAUOEQbiqY2Fjp/felefOkzZuloUOl8PCTHxsRIc2YYYWel16Sli8PdbUAAFQ8hJsK6OKLpcGDJbf7t4/t3Fm65Rbr+ciR0tGjQS8PAIAKjXDjAH//u1SnjjWL6vgtugAAqLIINw5Qp44VcCSrmwoAgKqMcOMQQ4ZI1apJ33wjff213dUAAGAfwo1DxMaW3Fn8P/+xuxoAAOxDuHGQ/v2tx0WL7K4EAAD7EG4c5IorrMdPPpHy8+2uBgAAexBuHKR5c+m886Rjx6T//tfuagAAsAfhxmG8rTeMuwEAVFWEG4fxjrt57z3J47G7GgAAQo9w4zAXXSTFxUm7dll3DAcAoKoh3DhMtWpS797Wc2ZNAQCqIsKNAzHuBgBQlRFuHKhvX+tO4evXSzt32l0NAAChRbhxoLp1rbuF6/jAYgAAqhLCjUOxWjEAoKoi3DiUd9zNBx9IR47YXQ0AAKFDuHGodu2kBg2kgwdZrRgAULUQbhzK5ZKuu856/txzdlcDAEDoEG4cbMQI63HxYun77+2uBgCA0CDcONg551gL+hkjzZxpdzUAAIQG4cbhbr/denzhBenwYburAQAg+Ag3Dtevn9SokbR3rzR/vt3VAAAQfIQbhwsPLxl78+yzdlcDAEDwEW6qgJtuktxuae1aayuP/fulKVOkyy+XPv00WBUCABA4hJsqIDFRuvZa63lZW2+2bZP+/GcpJUW6915p6VLrnlWrVgW1VAAAzhrhporwDiyeN0/65ZdTH3fggDR8uNS8uTR1qvW6VSspLc1aELBvXyk7O2RlAwBQboSbKqJLF6l9e+tWDC+8cPJjtm+XunWTXnpJKi6WLrvMWiNn0yar5aZbN6ubqlcvafPmUP8CAADKhnBTRbhcJa0348ZJt94q7dxZ8v7q1VKnTtLGjVJSkvTJJ9Z9qfr0sc6tUUP6z3+k1FRpzx4r+Hz3nW0/BwCAU3IZY4zdRYRSfn6+4uPjlZeXp7i4OLvLCanCQun666UFC6zX0dHWuJpzzpFGjrTeb9tWevdda/r4yfzyi9Sjh/T551LTptKGDVJsbEh/BgCgCirP329abqqQyEjpjTesWU9du1qL+v3979KNN1rBZsAAafnyUwcbSapTx+qiatxY+uEHaeLEUP4CAAB+G+GmCrroIivELFwotWxp7ft//096++2ytcLUqydNn249f/JJacuW4NYLAEB50C1VxRUXS7t3W4GlvK64whqH07u3NfDY5QpGhQAA0C2FcggPP7NgI1lTxd1u6f33pXfeCXRlAACcGcINztg550h33209v+subswJAKgYCDc4K/ffLzVsKG3dat2mAQAAuxFucFZq1JCeeMJ6PnmyFXIAALAT4QZn7dprpZ49rdWPBw2S8vPtrggAUJURbnDWXC5p5kxrDZy1a6X+/a37UAEAYAfCDQLi3HOl//5Xio+31tC56iqrJQcAgFAj3CBgfvc7a72bGjWs+1Jde61UVGR3VQCAqoZwg4BKS5MWLZKioqzHoUNpwQEAhBbhBgHXo4d1K4dq1aQ337Ru9/DDD3ZXBQCoKgg3CIo+faxbM9SpI2VlWV1W775rd1UAgKqAcIOg6dVL+uwzqUsXaf9+6corpfvuk44ds7syAICTEW4QVCkp0scfS2PGWK8ffdSaKn7ggN2VAQCcinCDoHO7rZtsvvGGVL26NWX80kulPXvsrgwA4ESEG4TMtddKH35YstjfRRdJ27bZXRUAwGkINwipzp2tRf5SUqQtW6Ru3aQvvrC7KgCAkxBuEHItW0r/+590wQXSTz9JF18sLVhgd1UAAKcg3MAWDRtKn34qde0q7dtn3XDz2mulXbvsrgwAUNkRbmCb2rWlZcuk8eOliAhrwb9WrQLXirNrl5STE5jPAgBUHoQb2Mrtlh5+WFqzRmrTxppBNWiQNHr02X3ukSPWwoFt2lhr7AAAqg7CDSqEDh2kdeukBx+UwsKk6dOtm3CeqQ8/tMbz7NkjvfVWICsFAFR0hBtUGG639Mgj0l13Wa9vu00qKDizz3rnnZLnc+YEpj4AQOVAuEGF88gjUpMm0o4d0l//Wv7zPR7/cLNsmfTzzwEtEQBQgRFuUOHUqCE995z1fNo0adWq8p2flWWFmZgYKTXVCjtvvBGUUgEAFRDhBhXS5ZdLGRmSMdLNN0tFRWU/99//th779JFuuMF6TtcUAFQdhBtUWE88IdWta61gPHly2c/zdkn9/vfW2jnh4dZsrG+/DVqpAIAKhHCDCqtOHenpp63n//d//uNoTuWHH6RNm6xA06+flJQkpadb782dG9x6AQAVA+EGFdrgwdKVV0pHj1otMQMHSj/+eOrjvQHo4outRQIl6Y9/tB7nzLG6uQAAzmZ7uHnmmWfUpEkTRUVFqXPnzlqzZs0pj/3iiy80cOBANWnSRC6XS1OnTg1prQg9l8tqcbn3Xqs15q23pPPPt1p0iotLH+8db/P735fsu+oqKSpK+uoracOG0NUOALCHreFm/vz5Gjt2rCZMmKD169erXbt26t27t3ad4gZDhw4dUrNmzTR58mTVq1cv5PXCHtWrW2Nu1q+XunSRDhyQxoyRevSQ8vNLjtu3T/rkE+v5lVeW7I+Lk664wnrOwGIAcD5bw82TTz6pW265RcOHD9cFF1ygmTNnqnr16po9e/ZJj7/wwgv12GOP6brrrlNkZGTI64W92raVVqyQZs60Asvy5daMKG/Aee89qzWndWupWTP/c71dU3PnWlPDAQDOZVu4KSoqUlZWltK9oz0lhYWFKT09XStXrgzY9xQWFio/P99vQ+UVFmatXLxsmVSrlrRypdS3r7WSsXe8zYmtNl59+0rx8dZ4neXLQ142ACCEbAs3e/bsUXFxsZKSkvz2JyUlKSeAt3KeNGmS4uPjfVtKSkrAPhv2+d3vpA8+kGrWlP73P6sFx3svqhPH23hFRVmDkSUW9AMAp7N9QHGwjRs3Tnl5eb5tx44ddpeEAPl1wCkokOrXlzp2PPnxffpYj+Vd8RgAULnYFm4SEhIUHh6u3Nxcv/25ubkBHSwcGRmpuLg4vw3OkZoqLV1qBRxJGjDA6ro6mQsvtB43bpQKC0NXIwAgtGwLN263W6mpqcrMzPTt83g8yszMVFpaml1loRLq2FH66CNp5EjpgQdOfVzjxtbCgEePMiUcAJzM1m6psWPHatasWXr55Ze1efNmjRw5UgcPHtTw4cMlScOGDdO4ceN8xxcVFSk7O1vZ2dkqKirSTz/9pOzsbH3LuvpVXrt20rPPSo0anfoYl6uk9WbdupCVBgAIsQg7v3zw4MHavXu3xo8fr5ycHLVv315LlizxDTLevn27wk7oY9i5c6c6dOjge/3444/r8ccfV/fu3fXRRx/Z8htQuXTsKC1ZIq1da3clAIBgcRlTtRakz8/PV3x8vPLy8hh/UwW98441m6pVK+nzz+2uBgBQVuX5++342VLAibwzqTZvtlY6BgA4D+EGVUpysrV5PNJnn9ldDQAgGAg3qHK8g4oZdwMAzkS4QZXDjCkAcDbCDaoc77gbWm4AwJkIN6hyvOHm22+lffvsrgYAEGiEG1Q5depIzZpZz7Oy7K4GABBohBtUSXRNAYBzEW5QJTFjCgCci3CDKokZUwDgXIQbVEm/+511I80dO6TcXLurAQAEEuEGVVJsrNSypfWcrikAcBbCDaosuqYAwJkIN6iymDEFAM5EuEGVdeKMKWPsrgYAECiEG1RZ7dpJERHS7t3Se+/ZXQ0AIFAIN6iyoqOlIUOs51dfLf3rX3ZXBAAIBMINqrR//lMaNEg6etR6fPlluysCAJwtwg2qNLdbmjNHuvFGyeORbrhBmj7d7qoAAGeDcIMqLzxcmjVLGjPGej16tDR1qt1VAQDOFOEGkBQWJv3jH9KDD1qv/9//k5Yts7sqAMCZINwAx7lc0sMPSxkZVhfVdddJO3faXRUAoLwIN8AJXC7p2Weltm2lXbusgHP0qN1VAQDKg3AD/Er16tKbb1r3n/r0U+n+++2uCABQHoQb4CRatJBeesl6/vjj0ttv210RAKCsCDfAKfzhD9LYsdbzG26wWnEAABUf4QY4jcmTpYsvlvLzpZ49pSef5D5UAFDREW6A06hWTVq82LpNQ3GxNUV88GCpoMDuygAAp0K4AX5DjRrS669L06ZZN9pcsEDq1En66iu7KwMAnAzhBigDl0u64w7pk0+k5GQr2PTsKf34o92VAQB+jXADlENamvTZZ1Lr1lJOjnU38cOH7a4KAHAiwg1QTomJ0jvvSHXqSOvWSTffzCBjAKhICDfAGWja1FroLyLCuqv4lCl2VwQA8CLcAGeoRw/pqaes5+PGSf/5j90VAQBEuAHOzsiR0m23Wd1SQ4ZY3VQAAHsRboCz4HJJTz8tXXKJtfZN9+7Sv/9td1UAULURboCz5HZL774r9e4tHTpkzaDydlcBAEKPcAMEQFycFXBuvdXqorrrLunOO61VjQEAoUW4AQKkWjVp5sySmVPTpknXXCMdPWp3ZQBQtRBugAByuaR77rFu0RAZKS1cKN10k+Tx2F0ZAFQdhBsgCK65RvrXv6TwcOnVV6W772ahPwAIFcINECT9+0svvmg9/8c/pMmT7a4IAKoGwg0QRNdfLz35pPX8/vulWbPsrggAnI9wAwTZn/9srWAsSSNGSK+/bndFAOBshBsgBCZOtKaJezxWa84zz9hdEQA4F+EGCAGXS5oxQxo92hpYfMcd0t/+xiBjAAgGwg0QImFh1srFEyZYr8ePt7qsmCYOAIFFuAFCyOWSHnqo5PYMTz1l3a5hyRKpqMju6gDAGQg3gA3uvFN65RVrHZx33pH69pXq1pWGDrXWx9m3z+4KAaDychlTtXr98/PzFR8fr7y8PMXFxdldDqq4tWuttXDeflvKyfF/74ILpIsukrp1k3r0kBo1sqtKALBfef5+E26ACsDjkVavlt56S1q0SPrqq9LHdO8u3XCDtfpxTIwdVQKAfQg3p0G4QWWwe7f0v/9Jy5db2+rVJTOrqle3Ak7PnlLLltZWs6bdFQNAcBFuToNwg8poxw7rHlUvvyx9/XXp9+vVk845R0pIkGrXlurUsR737ZO2bi3ZCgqkTp2sbq4ePaQuXaSoKDt+EQCUD+HmNAg3qMyMkVatkt54Q9q0Sdq8Wdq588w/LzLSugfWI49IrVoFslIACCzCzWkQbuA0+fnWGJ2tW6VffpH27i15jI+XmjQp2dxuacUK6aOPpGXLSgYxu1zWyskPP2wdBwAVDeHmNAg3gMUYaeNGa6Xkf/3L2letmnTzzVKbNlKNGiffYmKk2FiJf30AhBLh5jQIN0Bpa9ZYN/f88MOyn5OUJLVrV7Kdc44VeGJjrQAUEyNFRASzagBVCeHmNAg3wKl98IE0Z46UlycdPGhtBw6UPPe+Li4u2+edc47UsWPJ1qqVFXoiI62uMAAoK8LNaRBugLNjjBVyvvhC2rChZNu+3dpfUCAdPXr6z3C5rCnt0dElW1RUyeOvt6ZNpYsvtmZ3Va8eql8KoCIpz99vGo0BlIvLZbW+dO5sbSdTVGRNQ9+4UVq3rmTbvt163xuQDh4s33dHREipqdb3ut1WiPJuhw9bg6sLCqzHI0esVZ67drW29u2tcwA4Hy03AELGG0IOHSrZjhyx9nkfDx+WCgut10eOWMds3Ch9+qn0449n/t1RUSULHsbFWTPJataUkpOtW1t4t/r1rYHVACoWWm4AVEjVqlnbmfz/CmOkbdukTz6xwo7LVfJ51apZ4SUurmRQc3i49Nln1krPK1da0+Ozs8v2XWFh1rgg71atmtVqFB5uPVavLp17rtUydP751mPt2tZYJO8mlQSo8PDS3+ENerGxjD8CAo2WGwCOZ4z0zTfSd99ZXVZ5eda2f7/VGrR9u7Xt2PHb44XORFycFX7Cwkq6zgoLrffcbqu1qEEDa4uPt97zbkVF1nknBjnJv/Xr0CErcJ24OnV8fMmYJW9Ii4uz3vdutWvTVYfKo9K13DzzzDN67LHHlJOTo3bt2mnatGnq1KnTKY9fsGCBHnzwQW3dulUtWrTQo48+qn79+oW0ZgCVh8tltbSce+7pjysutkLPieGisFA6dsx/y8uzFk788ktrlegvv7TGD4WHW1tYmBWoDh+2Pjc/39pOpqjIapHati3wv7ss3O6S9Ytq1LBCkjcQeR+9rVZhYSW/8devo6Otc73biTPivI+Rkf6DyN1u63p6x00dO2bVkJAg1a1rPUZGWtcoL88ax7V/vxXw6tWzjjndcgPGWJ9bVGRtbrf1vSdrSYOz2B5u5s+fr7Fjx2rmzJnq3Lmzpk6dqt69e2vLli1KTEwsdfz//vc/DRkyRJMmTdIVV1yhOXPm6KqrrtL69evVunVrW34DAGcID7daM8piwIDfPqaoyPpjvG+ftWK0MVY3lHcRxKgo6yapP/1Ush044N8l5nZbd433hoBjx6zPOTFIREVZQWrv3pIVqvfv9w9oR45YAeGXX6xt3z7rc7x/+PftO+vLFxSRkSWtXL8WFmYFnLp1reviHbPlHbdVVHTy87zdmN5Qd+Lmdpd0P54Y4lwuawsLK725XP7X2RuIvf0i3sfoaP8FMd3u0l2Sxlj/e3s3HR9I793Cw0u6SSMiSlrzTvxnJjLSOvfXLYBSye9wufy7cuPirN9vjBXyPR7/blbva4/H//vcbuv1r39HZKQVQO1ie7dU586ddeGFF2r69OmSJI/Ho5SUFI0ePVr33XdfqeMHDx6sgwcPatGiRb59Xbp0Ufv27TVz5szf/D66pQCgpJXqxHWMDhwoGdx94qDuX/+BO9nmHfzt3byB5MQ/8IWFJeHj0CHrD673D7X38cABac8ea/v1ekregeBHj0q7dpX88UfFk5ZmjXcLpErTLVVUVKSsrCyNGzfOty8sLEzp6elauXLlSc9ZuXKlxo4d67evd+/eWrhw4UmPLywsVOEJsT//VG3DAFCFeFupytpSFWoej9X6lJ9f0rJwYhdUcbEVgHJyrNavatX810v6detXtWpWmDpxVt6Joa6gwHr0to6dGNy8rSm/fvSGPWP8u/G8g9C9XC7r2CNHrO87dMh6/HXLkjGlW4W8LSneLtGjR63X3jqPHbM+59ctR78eFH/i2CpjrO3IkZIu0/x86/e7XCfvdvS+DguzvvvEFqGTjVOLjAzGPxVlZ2u42bNnj4qLi5WUlOS3PykpSV999dVJz8nJyTnp8TneOwD+yqRJk/Twww8HsGoAQLCFhZ0+fIWHW7cA+dWfg9OqVs3qDoLzhdldQLCNGzdOeXl5vm3Hjh12lwQAAILI1pabhIQEhYeHKzc3129/bm6u6p1iJFK9evXKdXxkZKQi7W4fAwAAIWNry43b7VZqaqoyMzN9+zwejzIzM5WWlnbSc9LS0vyOl6SlS5ee8ngAAFC12D4VfOzYscrIyFDHjh3VqVMnTZ06VQcPHtTw4cMlScOGDVODBg00adIkSdKYMWPUvXt3PfHEE+rfv7/mzZundevW6fnnn7f5lwAAgIrA9nAzePBg7d69W+PHj1dOTo7at2+vJUuW+AYNb9++XWFhJQ1MXbt21Zw5c/TXv/5V999/v1q0aKGFCxeyxg0AAJAqwjo3ocY6NwAAVD7l+fvt+NlSAACgaiHcAAAARyHcAAAARyHcAAAARyHcAAAARyHcAAAARyHcAAAARyHcAAAAR7F9heJQ865ZmJ+fb3cpAACgjLx/t8uy9nCVCzcFBQWSpJSUFLtLAQAA5VRQUKD4+PjTHlPlbr/g8Xi0c+dOxcbGyuVyBfSz8/PzlZKSoh07dnBrhyDjWocO1zp0uNahw7UOnUBda2OMCgoKlJyc7HfPyZOpci03YWFhatiwYVC/Iy4ujn9ZQoRrHTpc69DhWocO1zp0AnGtf6vFxosBxQAAwFEINwAAwFEINwEUGRmpCRMmKDIy0u5SHI9rHTpc69DhWocO1zp07LjWVW5AMQAAcDZabgAAgKMQbgAAgKMQbgAAgKMQbgAAgKMQbgLkmWeeUZMmTRQVFaXOnTtrzZo1dpdU6U2aNEkXXnihYmNjlZiYqKuuukpbtmzxO+bIkSO6/fbbVadOHcXExGjgwIHKzc21rWanmDx5slwul+666y7fPq514Pz000/605/+pDp16ig6Olpt2rTRunXrfO8bYzR+/HjVr19f0dHRSk9P1zfffGNrzZVRcXGxHnzwQTVt2lTR0dFq3ry5/va3v/ndm4hrfeY++eQTDRgwQMnJyXK5XFq4cKHf+2W5tnv37tXQoUMVFxenmjVr6qabbtKBAwfOvjiDszZv3jzjdrvN7NmzzRdffGFuueUWU7NmTZObm2t3aZVa7969zYsvvmg+//xzk52dbfr162caNWpkDhw44DtmxIgRJiUlxWRmZpp169aZLl26mK5du9pad2W3Zs0a06RJE9O2bVszZswY336udWDs3bvXNG7c2Nxwww1m9erV5vvvvzfvv/+++fbbb33HTJ482cTHx5uFCxeaDRs2mCuvvNI0bdrUHD582NbaK5uJEyeaOnXqmEWLFpkffvjBLFiwwMTExJinnnrKdwzX+sy999575oEHHjBvvfWWkWTefvttv/fLcm379Olj2rVrZ1atWmU+/fRTc84555ghQ4acdW2EmwDo1KmTuf32232vi4uLTXJyspk0aZKtdTnNrl27jCTz8ccfG2OM2b9/v6lWrZpZsGCB75jNmzcbSWblypU2Vlp5FRQUmBYtWpilS5ea7t27+8IN1zpw7r33XnPRRRed8n2Px2Pq1atnHnvsMd++/fv3m8jISDN37twQVekM/fv3NzfeeKPfvj/84Q9m6NChxnCtA+rX4aYs1/bLL780kszatWt9xyxevNi4XC7z008/nVU9dEudpaKiImVlZSk9Pd23LywsTOnp6Vq5cqWttTlNXl6eJKl27dqSpKysLB09etTv2rds2VKNGjXi2p+h22+/Xf379/e7puJaB9Q777yjjh076tprr1ViYqI6dOigWbNm+d7/4YcflJOT43et4+Pj1blzZ651OXXt2lWZmZn6+uuvJUkbNmzQ8uXL1bdvX4lrHVRlubYrV65UzZo11bFjR98x6enpCgsL0+rVq8/q+6vcjTMDbc+ePSouLlZSUpLf/qSkJH311Ve21eU0Ho9Hd911l7p166bWrVtLknJycuR2u1WzZk2/Y5OSkpSTk2NTpZXXvHnztH79eq1du7bUe1zrwPn+++81Y8YMjR07Vvfff7/Wrl2rO++8U263WxkZGb7rebL/pnCty+e+++5Tfn6+WrZsqfDwcBUXF2vixIkaOnSodPyfa3Gtg6Is1zYnJ0eJiYl+70dERKh27dpnff0JN6gUbr/9dn3++edavny53aU40o4dOzRmzBgtXbpUUVFRdpfjaB6PRx07dtTf//53SVKHDh30+eefa+bMmcrIyLC7PEd544039Prrr2vOnDlq1aqVsrOzdddddyk5OZlr7XB0S52lhIQEhYeHl5o1kpubq3r16tlWl5PccccdWrRokZYtW6aGDRv69terV09FRUXav3+/3/Fc+/LLysrSrl279Lvf/U4RERGKiIjQxx9/rKeffloRERFKSkriWgdI/fr1dcEFF/jtO//887V9+3bp+D/XOn5tT8S1Lr977rlH9913n6677jq1adNG119/vf785z9r0qRJEtc6qMpybevVq6ddu3b5vX/s2DHt3bv3rK8/4eYsud1upaamKjMz07fP4/EoMzNTaWlpttZW2RljdMcdd+jtt9/Whx9+qKZNm/q9n5qaqmrVqvld+y1btmj79u1c+3K67LLLtGnTJmVnZ/u2jh07aujQob7nXOvA6NatW6klDb7++ms1btxYktS0aVPVq1fP71rn5+dr9erVXOtyOnTokMLC/P/MhYeHy+PxSFzroCrLtU1LS9P+/fuVlZXlO+bDDz+Ux+NR586dz66AsxqODGOOTwWPjIw0L730kvnyyy/NrbfeamrWrGlycnLsLq1SGzlypImPjzcfffSR+fnnn33boUOHfMeMGDHCNGrUyHz44Ydm3bp1Ji0tzaSlpdlat1OcOFvKcK0DZs2aNSYiIsJMnDjRfPPNN+b111831atXN6+99prvmMmTJ5uaNWuaf//732bjxo3m97//PdOTz0BGRoZp0KCBbyr4W2+9ZRISEsxf/vIX3zFc6zNXUFBgPvvsM/PZZ58ZSebJJ580n332mdm2bZsxZby2ffr0MR06dDCrV682y5cvNy1atGAqeEUybdo006hRI+N2u02nTp3MqlWr7C6p0pN00u3FF1/0HXP48GEzatQoU6tWLVO9enVz9dVXm59//tnWup3i1+GGax047777rmndurWJjIw0LVu2NM8//7zf+x6Pxzz44IMmKSnJREZGmssuu8xs2bLFtnorq/z8fDNmzBjTqFEjExUVZZo1a2YeeOABU1hY6DuGa33mli1bdtL/RmdkZBhTxmv7yy+/mCFDhpiYmBgTFxdnhg8fbgoKCs66Npc5calGAACASo4xNwAAwFEINwAAwFEINwAAwFEINwAAwFEINwAAwFEINwAAwFEINwAAwFEINwAAwFEINwCqJJfLpYULF9pdBoAgINwACLkbbrhBLper1NanTx+7SwPgABF2FwCgaurTp49efPFFv32RkZG21QPAOWi5AWCLyMhI1atXz2+rVauWdLzLaMaMGerbt6+io6PVrFkzvfnmm37nb9q0SZdeeqmio6NVp04d3XrrrTpw4IDfMbNnz1arVq0UGRmp+vXr64477vB7f8+ePbr66qtVvXp1tWjRQu+8847vvX379mno0KGqW7euoqOj1aJFi1JhDEDFRLgBUCE9+OCDGjhwoDZs2KChQ4fquuuu0+bNmyVJBw8eVO/evVWrVi2tXbtWCxYs0AcffOAXXmbMmKHbb79dt956qzZt2qR33nlH55xzjt93PPzwwxo0aJA2btyofv36aejQodq7d6/v+7/88kstXrxYmzdv1owZM5SQkBDiqwDgjJz1fcUBoJwyMjJMeHi4qVGjht82ceJEY4wxksyIESP8zuncubMZOXKkMcaY559/3tSqVcscOHDA9/5//vMfExYWZnJycowxxiQnJ5sHHnjglDVIMn/96199rw8cOGAkmcWLFxtjjBkwYIAZPnx4gH85gFBgzA0AW/Ts2VMzZszw21e7dm3f87S0NL/30tLSlJ2dLUnavHmz2rVrpxo1avje79atmzwej7Zs2SKXy6WdO3fqsssuO20Nbdu29T2vUaOG4uLitGvXLknSyJEjNXDgQK1fv16XX365rrrqKnXt2vUsfzWAUCDcALBFjRo1SnUTBUp0dHSZjqtWrZrfa5fLJY/HI0nq27evtm3bpvfee09Lly7VZZddpttvv12PP/54UGoGEDiMuQFQIa1atarU6/PPP1+SdP7552vDhg06ePCg7/0VK1YoLCxM5513nmJjY9WkSRNlZmaeVQ1169ZVRkaGXnvtNU2dOlXPP//8WX0egNCg5QaALQoLC5WTk+O3LyIiwjdod8GCBerYsaMuuugivf7661qzZo1eeOEFSdLQoUM1YcIEZWRk6KGHHtLu3bs1evRoXX/99UpKSpIkPfTQQxoxYoQSExPVt29fFRQUaMWKFRo9enSZ6hs/frxSU1PVqlUrFRYWatGiRb5wBaBiI9wAsMWSJUtUv359v33nnXeevvrqK+n4TKZ58+Zp1KhRql+/vubOnasLLrhAklS9enW9//77GjNmjC688EJVr15dAwcO1JNPPun7rIyMDB05ckT/+Mc/dPfddyshIUHXXHNNmetzu90aN26ctm7dqujoaF188cWaN29ewH4/gOBxGWvWAABUGC6XS2+//bauuuoqu0sBUAkx5gYAADgK4QYAADgKY24AVDj0lgM4G7TcAAAARyHcAAAARyHcAAAARyHcAAAARyHcAAAARyHcAAAARyHcAAAARyHcAAAAR/n/66M0wFecaLoAAAAASUVORK5CYII=",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.suptitle('Log MSE vs Epochs')\n",
    "plt.plot(torch.log(tune_train_err), label='Train', color='blue')\n",
    "plt.xlabel('Epochs')\n",
    "plt.ylabel('Log MSE')\n",
    "plt.show()\n",
    "with open(\"plots/tune-loss.html\", \"w\") as f:\n",
    "    mpld3.save_html(plt.gcf(), f)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(array([[6.0290e+03, 1.8597e+04, 8.4000e+01, ..., 0.0000e+00, 0.0000e+00,\n",
       "         0.0000e+00],\n",
       "        [0.0000e+00, 0.0000e+00, 0.0000e+00, ..., 0.0000e+00, 0.0000e+00,\n",
       "         0.0000e+00],\n",
       "        [0.0000e+00, 0.0000e+00, 0.0000e+00, ..., 0.0000e+00, 0.0000e+00,\n",
       "         0.0000e+00],\n",
       "        ...,\n",
       "        [0.0000e+00, 0.0000e+00, 0.0000e+00, ..., 0.0000e+00, 0.0000e+00,\n",
       "         0.0000e+00],\n",
       "        [0.0000e+00, 0.0000e+00, 0.0000e+00, ..., 0.0000e+00, 0.0000e+00,\n",
       "         0.0000e+00],\n",
       "        [0.0000e+00, 0.0000e+00, 0.0000e+00, ..., 0.0000e+00, 0.0000e+00,\n",
       "         1.0000e+00]]),\n",
       " array([1.   , 1.1  , 1.2  , 1.3  , 1.4  , 1.5  , 1.6  , 1.699, 1.8  ,\n",
       "        1.9  , 2.   , 2.1  , 2.2  , 2.3  , 2.398, 2.5  , 2.6  , 2.7  ,\n",
       "        2.8  , 2.898, 3.   , 3.1  , 3.2  , 3.299, 3.398, 3.5  , 3.6  ,\n",
       "        3.7  , 3.799, 3.898, 4.   , 4.1  , 4.2  , 4.297, 4.4  , 4.5  ,\n",
       "        4.6  , 4.7  , 4.797, 4.9  , 5.   , 5.098, 5.2  , 5.3  , 5.4  ,\n",
       "        5.5  , 5.598, 5.7  , 5.797, 5.9  , 6.   ], dtype=float16),\n",
       " array([0.9014, 0.998 , 1.094 , 1.19  , 1.287 , 1.384 , 1.48  , 1.576 ,\n",
       "        1.673 , 1.77  , 1.865 , 1.962 , 2.059 , 2.156 , 2.25  , 2.348 ,\n",
       "        2.445 , 2.541 , 2.637 , 2.734 , 2.83  , 2.926 , 3.023 , 3.121 ,\n",
       "        3.215 , 3.312 , 3.41  , 3.504 , 3.602 , 3.7   , 3.793 , 3.89  ,\n",
       "        3.988 , 4.082 , 4.18  , 4.277 , 4.375 , 4.47  , 4.566 , 4.664 ,\n",
       "        4.758 , 4.855 , 4.953 , 5.05  , 5.145 , 5.242 , 5.34  , 5.434 ,\n",
       "        5.53  , 5.63  , 5.723 ], dtype=float16),\n",
       " <matplotlib.collections.QuadMesh at 0x7f201dae35c0>)"
      ]
     },
     "execution_count": 21,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "mkhist(mkbatch(BSZ, large=False, target=\"onpath\"))\n",
    "with open(\"plots/tune-hist.html\", \"w\") as f:\n",
    "    mpld3.save_html(plt.gcf(), f)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "JtTLXn4zC1z_"
   },
   "source": [
    "# Step 6: Test generalization"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "mkhist(mkbatch(BSZ, large=True, target=\"onpath\", largetarget=False))\n",
    "with open(\"plots/test-onpath-smalltarget.html\", \"w\") as f:\n",
    "    mpld3.save_html(plt.gcf(), f)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0.20130713284015656\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "(array([[1.7789e+04, 6.5000e+02, 4.8000e+02, ..., 0.0000e+00, 1.0000e+00,\n",
       "         1.0000e+00],\n",
       "        [0.0000e+00, 0.0000e+00, 0.0000e+00, ..., 0.0000e+00, 0.0000e+00,\n",
       "         0.0000e+00],\n",
       "        [0.0000e+00, 0.0000e+00, 0.0000e+00, ..., 0.0000e+00, 0.0000e+00,\n",
       "         0.0000e+00],\n",
       "        ...,\n",
       "        [0.0000e+00, 0.0000e+00, 0.0000e+00, ..., 0.0000e+00, 0.0000e+00,\n",
       "         0.0000e+00],\n",
       "        [0.0000e+00, 0.0000e+00, 0.0000e+00, ..., 0.0000e+00, 0.0000e+00,\n",
       "         0.0000e+00],\n",
       "        [0.0000e+00, 0.0000e+00, 0.0000e+00, ..., 0.0000e+00, 0.0000e+00,\n",
       "         0.0000e+00]]),\n",
       " array([ 1.   ,  1.18 ,  1.36 ,  1.54 ,  1.721,  1.9  ,  2.08 ,  2.262,\n",
       "         2.441,  2.621,  2.8  ,  2.98 ,  3.16 ,  3.34 ,  3.521,  3.701,\n",
       "         3.88 ,  4.062,  4.242,  4.42 ,  4.6  ,  4.78 ,  4.96 ,  5.14 ,\n",
       "         5.32 ,  5.5  ,  5.68 ,  5.863,  6.043,  6.223,  6.402,  6.582,\n",
       "         6.76 ,  6.94 ,  7.12 ,  7.3  ,  7.48 ,  7.66 ,  7.844,  8.02 ,\n",
       "         8.2  ,  8.38 ,  8.56 ,  8.74 ,  8.92 ,  9.1  ,  9.28 ,  9.46 ,\n",
       "         9.64 ,  9.82 , 10.   ], dtype=float16),\n",
       " array([ 0.879,  1.156,  1.434,  1.71 ,  1.987,  2.266,  2.541,  2.818,\n",
       "         3.096,  3.373,  3.65 ,  3.928,  4.203,  4.48 ,  4.758,  5.035,\n",
       "         5.312,  5.59 ,  5.867,  6.145,  6.42 ,  6.7  ,  6.977,  7.254,\n",
       "         7.527,  7.805,  8.08 ,  8.36 ,  8.64 ,  8.92 ,  9.19 ,  9.47 ,\n",
       "         9.75 , 10.016, 10.3  , 10.58 , 10.86 , 11.125, 11.41 , 11.69 ,\n",
       "        11.97 , 12.234, 12.516, 12.8  , 13.08 , 13.34 , 13.625, 13.91 ,\n",
       "        14.17 , 14.45 , 14.734], dtype=float16),\n",
       " <matplotlib.collections.QuadMesh at 0x7f201daaff50>)"
      ]
     },
     "execution_count": 27,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "mkhist(mkbatch(BSZ, large=True, target=\"onpath\", largetarget=True))\n",
    "with open(\"plots/test-onpath-largetarget.html\", \"w\") as f:\n",
    "    mpld3.save_html(plt.gcf(), f)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "mkhist(mkbatch(BSZ, large=False, target=\"any\", largetarget=False))\n",
    "with open(\"plots/test-small-any.html\", \"w\") as f:\n",
    "    mpld3.save_html(plt.gcf(), f)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "mkhist(mkbatch(BSZ, large=True, target=\"any\", largetarget=False))\n",
    "with open(\"plots/test-large-any-smalltarget.html\", \"w\") as f:\n",
    "    mpld3.save_html(plt.gcf(), f)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "mkhist(mkbatch(BSZ, large=True, target=\"any\", largetarget=True))\n",
    "with open(\"plots/test-large-any-largetarget.html\", \"w\") as f:\n",
    "    mpld3.save_html(plt.gcf(), f)"
   ]
  }
 ],
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   "provenance": []
  },
  "kernelspec": {
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   "language": "python",
   "name": "python3"
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