diff --git a/serie2/1000epoches.png b/serie2/1000epoches.png
index 09dae075e8dd060d7b822a0c5661a52fae45f339..0b7de118bb3bf07747c35bc1458b3279cdfe5a21 100644
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diff --git a/serie2/150epochs.png b/serie2/150epochs.png
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diff --git a/serie2/KiadyArintsoa_TeoColomberotto_SimonCirilli.zip b/serie2/KiadyArintsoa_TeoColomberotto_SimonCirilli.zip
new file mode 100644
index 0000000000000000000000000000000000000000..f2aafa48383b9fa1fa7037963cb1e10f1097f6ea
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diff --git a/serie2/MNIST_binary_classifier_stud.ipynb b/serie2/MNIST_binary_classifier_stud.ipynb
index 380c62dc69b28653897d752d7e93ebd07c53d005..8bde56fd52a5c90c367a642537c646502a1ac30f 100644
--- a/serie2/MNIST_binary_classifier_stud.ipynb
+++ b/serie2/MNIST_binary_classifier_stud.ipynb
@@ -16,7 +16,7 @@
},
{
"cell_type": "code",
- "execution_count": 2,
+ "execution_count": 3,
"id": "educational-syndrome",
"metadata": {},
"outputs": [
@@ -41,7 +41,7 @@
},
{
"cell_type": "code",
- "execution_count": 3,
+ "execution_count": 4,
"id": "allied-flavor",
"metadata": {},
"outputs": [
@@ -76,7 +76,7 @@
},
{
"cell_type": "code",
- "execution_count": 4,
+ "execution_count": 5,
"id": "effective-anaheim",
"metadata": {},
"outputs": [
@@ -104,7 +104,7 @@
},
{
"cell_type": "code",
- "execution_count": 5,
+ "execution_count": 6,
"id": "stock-simpson",
"metadata": {},
"outputs": [],
@@ -145,7 +145,7 @@
},
{
"cell_type": "code",
- "execution_count": 6,
+ "execution_count": 7,
"id": "returning-relative",
"metadata": {},
"outputs": [
@@ -172,7 +172,7 @@
},
{
"cell_type": "code",
- "execution_count": 7,
+ "execution_count": 8,
"id": "qualified-charm",
"metadata": {},
"outputs": [
@@ -196,7 +196,7 @@
},
{
"cell_type": "code",
- "execution_count": 8,
+ "execution_count": 9,
"id": "signed-kansas",
"metadata": {},
"outputs": [
@@ -241,7 +241,7 @@
},
{
"cell_type": "code",
- "execution_count": 34,
+ "execution_count": 16,
"id": "removed-commons",
"metadata": {},
"outputs": [],
@@ -367,20 +367,32 @@
" \n",
" # MSE code\n",
"\n",
+ " # Version 1 => Doesnt pass unit tests\n",
" # grad_w = self.w\n",
" # for i in range(m):\n",
" # x_i = np.array([x[i]]).T\n",
" # grad_w += y_pred[i][0] * (1 - y_pred[i][0]) * (y_pred[i][0]- y[i][0]) * x_i\n",
- " \n",
+ "\n",
+ " # Version 2 => Doesnt pass unit tests \n",
" # grad_w = 1 / m * np.sum(y_pred * (1 - y_pred) * (y_pred - y)) * x\n",
" \n",
- " grad_w = np.dot((y_pred*(1-y_pred)*(y_pred-y)).T,x)/x.shape[1]\n",
+ " # Version 3 => Doesnt pass unit tests\n",
+ "\n",
+ " print(x.shape)\n",
+ "\n",
+ " grad_w = np.dot((y_pred*(1-y_pred)*(y_pred-y)).T,x)/x.shape[0]\n",
+ "\n",
+ "\n",
+ "\n",
+ " # Unit test => pass\n",
" grad_b = 1 / m * np.sum(y_pred * (1 - y_pred) * (y_pred - y))\n",
" \n",
" \n",
" else: \n",
" # cross entropy code\n",
- " grad_w = 1 / m * np.dot(x.T, (y_pred - y))\n",
+ " grad_w = 1 / m * np.dot(x.T, (y_pred - y)).T\n",
+ " print(grad_w.shape)\n",
+ "\n",
" grad_b = 1 / m * np.sum(y_pred - y)\n",
"\n",
" ### END YOUR CODE ### \n",
@@ -453,7 +465,7 @@
},
{
"cell_type": "code",
- "execution_count": 62,
+ "execution_count": 57,
"id": "colored-facility",
"metadata": {},
"outputs": [
@@ -461,15 +473,1007 @@
"name": "stdout",
"output_type": "stream",
"text": [
- "result after 10 epochs, train: cost 0.01564, error 0.03131 ; test: cost 0.01565, error 0.03131\n"
- ]
- },
- {
- "name": "stderr",
- "output_type": "stream",
- "text": [
- "/tmp/ipykernel_12186/4269509613.py:150: RuntimeWarning: overflow encountered in exp\n",
- " return 1 / (1 + np.exp(-z))\n"
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+ "(12136, 784)\n",
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+ "(12136, 784)\n",
+ "(12136, 784)\n",
+ "(12136, 784)\n",
+ "(12136, 784)\n",
+ "(12136, 784)\n",
+ "(12136, 784)\n",
+ "(12136, 784)\n",
+ "(12136, 784)\n",
+ "(12136, 784)\n",
+ "(12136, 784)\n",
+ "(12136, 784)\n",
+ "(12136, 784)\n",
+ "(12136, 784)\n",
+ "(12136, 784)\n",
+ "(12136, 784)\n",
+ "(12136, 784)\n",
+ "(12136, 784)\n",
+ "(12136, 784)\n",
+ "(12136, 784)\n",
+ "(12136, 784)\n",
+ "(12136, 784)\n",
+ "(12136, 784)\n",
+ "(12136, 784)\n",
+ "(12136, 784)\n",
+ "(12136, 784)\n",
+ "result after 1000 epochs, train: cost 0.00359, error 0.00717 ; test: cost 0.00507, error 0.01088\n"
]
}
],
@@ -477,6 +1481,7 @@
"#data is arranged as dictionary with quick access through respective keys\n",
"data = {'x_train' : x_train, 'y_train' : y_train, 'x_test' : x_test, 'y_test' : y_test}\n",
"\n",
+ "# gradD = GradientDescent(data, 0.05, 1, 0.)\n",
"gradD = GradientDescent(data, 10, 0, 0.)\n",
"\n",
"gradD.optimise(10, False)"
@@ -494,7 +1499,7 @@
},
{
"cell_type": "code",
- "execution_count": 63,
+ "execution_count": 52,
"id": "lonely-quantity",
"metadata": {},
"outputs": [
@@ -503,15 +1508,15 @@
"output_type": "stream",
"text": [
"[0. 1. 2. 3. 4. 5. 6. 7. 8. 9.]\n",
- "[0.125 0.01580711 0.01578208 0.01575926 0.0157372 0.01571619\n",
- " 0.01569154 0.01566771 0.01565214 0.01563725]\n",
- "[0.125 0.01580335 0.01578762 0.0157682 0.01574174 0.01570748\n",
- " 0.01567523 0.01565887 0.0156537 0.01565004]\n"
+ "[0.125 0.01512893 0.01454848 0.01392892 0.01340643 0.01301482\n",
+ " 0.012721 0.0124677 0.01224422 0.01204864]\n",
+ "[0.125 0.01541932 0.0151363 0.01486291 0.01454016 0.01417231\n",
+ " 0.01380597 0.01345951 0.01316053 0.01291394]\n"
]
},
{
"data": {
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",
+ "image/png": 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",
"text/plain": [
"<Figure size 640x480 with 1 Axes>"
]
@@ -543,7 +1548,7 @@
},
{
"cell_type": "code",
- "execution_count": 12,
+ "execution_count": 56,
"id": "neither-moldova",
"metadata": {},
"outputs": [
@@ -551,16 +1556,417 @@
"name": "stdout",
"output_type": "stream",
"text": [
- "[0. 1. 2. 3. 4. 5. 6. 7. 8. 9.]\n",
- "[0.47791694 0.0316414 0.030735 0.03024061 0.02916941 0.02859262\n",
- " 0.02776862 0.02694463 0.02677983 0.02620303]\n",
- "[0.49208965 0.0316414 0.0309822 0.0309822 0.03032301 0.03032301\n",
- " 0.02999341 0.0306526 0.0306526 0.02900461]\n"
+ "[ 0. 1. 2. 3. 4. 5. 6. 7. 8. 9. 10. 11. 12. 13.\n",
+ " 14. 15. 16. 17. 18. 19. 20. 21. 22. 23. 24. 25. 26. 27.\n",
+ " 28. 29. 30. 31. 32. 33. 34. 35. 36. 37. 38. 39. 40. 41.\n",
+ " 42. 43. 44. 45. 46. 47. 48. 49. 50. 51. 52. 53. 54. 55.\n",
+ " 56. 57. 58. 59. 60. 61. 62. 63. 64. 65. 66. 67. 68. 69.\n",
+ " 70. 71. 72. 73. 74. 75. 76. 77. 78. 79. 80. 81. 82. 83.\n",
+ " 84. 85. 86. 87. 88. 89. 90. 91. 92. 93. 94. 95. 96. 97.\n",
+ " 98. 99. 100. 101. 102. 103. 104. 105. 106. 107. 108. 109. 110. 111.\n",
+ " 112. 113. 114. 115. 116. 117. 118. 119. 120. 121. 122. 123. 124. 125.\n",
+ " 126. 127. 128. 129. 130. 131. 132. 133. 134. 135. 136. 137. 138. 139.\n",
+ " 140. 141. 142. 143. 144. 145. 146. 147. 148. 149. 150. 151. 152. 153.\n",
+ " 154. 155. 156. 157. 158. 159. 160. 161. 162. 163. 164. 165. 166. 167.\n",
+ " 168. 169. 170. 171. 172. 173. 174. 175. 176. 177. 178. 179. 180. 181.\n",
+ " 182. 183. 184. 185. 186. 187. 188. 189. 190. 191. 192. 193. 194. 195.\n",
+ " 196. 197. 198. 199. 200. 201. 202. 203. 204. 205. 206. 207. 208. 209.\n",
+ " 210. 211. 212. 213. 214. 215. 216. 217. 218. 219. 220. 221. 222. 223.\n",
+ " 224. 225. 226. 227. 228. 229. 230. 231. 232. 233. 234. 235. 236. 237.\n",
+ " 238. 239. 240. 241. 242. 243. 244. 245. 246. 247. 248. 249. 250. 251.\n",
+ " 252. 253. 254. 255. 256. 257. 258. 259. 260. 261. 262. 263. 264. 265.\n",
+ " 266. 267. 268. 269. 270. 271. 272. 273. 274. 275. 276. 277. 278. 279.\n",
+ " 280. 281. 282. 283. 284. 285. 286. 287. 288. 289. 290. 291. 292. 293.\n",
+ " 294. 295. 296. 297. 298. 299. 300. 301. 302. 303. 304. 305. 306. 307.\n",
+ " 308. 309. 310. 311. 312. 313. 314. 315. 316. 317. 318. 319. 320. 321.\n",
+ " 322. 323. 324. 325. 326. 327. 328. 329. 330. 331. 332. 333. 334. 335.\n",
+ " 336. 337. 338. 339. 340. 341. 342. 343. 344. 345. 346. 347. 348. 349.\n",
+ " 350. 351. 352. 353. 354. 355. 356. 357. 358. 359. 360. 361. 362. 363.\n",
+ " 364. 365. 366. 367. 368. 369. 370. 371. 372. 373. 374. 375. 376. 377.\n",
+ " 378. 379. 380. 381. 382. 383. 384. 385. 386. 387. 388. 389. 390. 391.\n",
+ " 392. 393. 394. 395. 396. 397. 398. 399. 400. 401. 402. 403. 404. 405.\n",
+ " 406. 407. 408. 409. 410. 411. 412. 413. 414. 415. 416. 417. 418. 419.\n",
+ " 420. 421. 422. 423. 424. 425. 426. 427. 428. 429. 430. 431. 432. 433.\n",
+ " 434. 435. 436. 437. 438. 439. 440. 441. 442. 443. 444. 445. 446. 447.\n",
+ " 448. 449. 450. 451. 452. 453. 454. 455. 456. 457. 458. 459. 460. 461.\n",
+ " 462. 463. 464. 465. 466. 467. 468. 469. 470. 471. 472. 473. 474. 475.\n",
+ " 476. 477. 478. 479. 480. 481. 482. 483. 484. 485. 486. 487. 488. 489.\n",
+ " 490. 491. 492. 493. 494. 495. 496. 497. 498. 499. 500. 501. 502. 503.\n",
+ " 504. 505. 506. 507. 508. 509. 510. 511. 512. 513. 514. 515. 516. 517.\n",
+ " 518. 519. 520. 521. 522. 523. 524. 525. 526. 527. 528. 529. 530. 531.\n",
+ " 532. 533. 534. 535. 536. 537. 538. 539. 540. 541. 542. 543. 544. 545.\n",
+ " 546. 547. 548. 549. 550. 551. 552. 553. 554. 555. 556. 557. 558. 559.\n",
+ " 560. 561. 562. 563. 564. 565. 566. 567. 568. 569. 570. 571. 572. 573.\n",
+ " 574. 575. 576. 577. 578. 579. 580. 581. 582. 583. 584. 585. 586. 587.\n",
+ " 588. 589. 590. 591. 592. 593. 594. 595. 596. 597. 598. 599. 600. 601.\n",
+ " 602. 603. 604. 605. 606. 607. 608. 609. 610. 611. 612. 613. 614. 615.\n",
+ " 616. 617. 618. 619. 620. 621. 622. 623. 624. 625. 626. 627. 628. 629.\n",
+ " 630. 631. 632. 633. 634. 635. 636. 637. 638. 639. 640. 641. 642. 643.\n",
+ " 644. 645. 646. 647. 648. 649. 650. 651. 652. 653. 654. 655. 656. 657.\n",
+ " 658. 659. 660. 661. 662. 663. 664. 665. 666. 667. 668. 669. 670. 671.\n",
+ " 672. 673. 674. 675. 676. 677. 678. 679. 680. 681. 682. 683. 684. 685.\n",
+ " 686. 687. 688. 689. 690. 691. 692. 693. 694. 695. 696. 697. 698. 699.\n",
+ " 700. 701. 702. 703. 704. 705. 706. 707. 708. 709. 710. 711. 712. 713.\n",
+ " 714. 715. 716. 717. 718. 719. 720. 721. 722. 723. 724. 725. 726. 727.\n",
+ " 728. 729. 730. 731. 732. 733. 734. 735. 736. 737. 738. 739. 740. 741.\n",
+ " 742. 743. 744. 745. 746. 747. 748. 749. 750. 751. 752. 753. 754. 755.\n",
+ " 756. 757. 758. 759. 760. 761. 762. 763. 764. 765. 766. 767. 768. 769.\n",
+ " 770. 771. 772. 773. 774. 775. 776. 777. 778. 779. 780. 781. 782. 783.\n",
+ " 784. 785. 786. 787. 788. 789. 790. 791. 792. 793. 794. 795. 796. 797.\n",
+ " 798. 799. 800. 801. 802. 803. 804. 805. 806. 807. 808. 809. 810. 811.\n",
+ " 812. 813. 814. 815. 816. 817. 818. 819. 820. 821. 822. 823. 824. 825.\n",
+ " 826. 827. 828. 829. 830. 831. 832. 833. 834. 835. 836. 837. 838. 839.\n",
+ " 840. 841. 842. 843. 844. 845. 846. 847. 848. 849. 850. 851. 852. 853.\n",
+ " 854. 855. 856. 857. 858. 859. 860. 861. 862. 863. 864. 865. 866. 867.\n",
+ " 868. 869. 870. 871. 872. 873. 874. 875. 876. 877. 878. 879. 880. 881.\n",
+ " 882. 883. 884. 885. 886. 887. 888. 889. 890. 891. 892. 893. 894. 895.\n",
+ " 896. 897. 898. 899. 900. 901. 902. 903. 904. 905. 906. 907. 908. 909.\n",
+ " 910. 911. 912. 913. 914. 915. 916. 917. 918. 919. 920. 921. 922. 923.\n",
+ " 924. 925. 926. 927. 928. 929. 930. 931. 932. 933. 934. 935. 936. 937.\n",
+ " 938. 939. 940. 941. 942. 943. 944. 945. 946. 947. 948. 949. 950. 951.\n",
+ " 952. 953. 954. 955. 956. 957. 958. 959. 960. 961. 962. 963. 964. 965.\n",
+ " 966. 967. 968. 969. 970. 971. 972. 973. 974. 975. 976. 977. 978. 979.\n",
+ " 980. 981. 982. 983. 984. 985. 986. 987. 988. 989. 990. 991. 992. 993.\n",
+ " 994. 995. 996. 997. 998. 999.]\n",
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",
+ "image/png": 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xNwhxawwAAFv8Ogh5ysyZM22XUAejxgAAsM+vb421b99ewcHB2rNnT631e/bsUUpKiqWqPI0gBACALX4dhMLCwjRgwADNmjXLvc7pdGrWrFn13tpqWWgRAgDANuu3xsrKyrRp0yb38pYtW7R8+XIlJCQoPT1dEydOVHZ2tgYOHKhBgwZp8uTJOnDggHsUWUtHDAIAwB7rQWjx4sU699xz3csTJ06UJGVnZ+v111/XL37xC+3du1cPPfSQ8vPz1a9fP02fPr1OB+qW5nAfIeYaAwDAHutB6JxzzjnuQwVvu+023XbbbT6qyPUk69zcXNXU1Hj9XMQgAADs8es+Qrbk5ORo7dq1ysvL895JHK4WIRqEAACwhyBkyeH846BNCAAAawhClhmCEAAA1hCErGH2eQAAbCMI2UYOAgDAGoKQNbQIAQBgG0HIksPPESIGAQBgD0GoHrm5ucrMzFRWVpb3TuJuECIKAQBgC0GoHj55jtBhBCEAAKwhCAEAgIBFELLEPdcYvYQAALCGIGQZd8YAALCHIGQNw+cBALCNIGSJcRCEAACwjSBkGbfGAACwhyBkDZ2lAQCwjSBkGS1CAADYQxCqh0+eLE1naQAArCMI1cMXT5Ym/gAAYB9ByDLDvTEAAKwhCNnioLM0AAC2EYQsIwYBAGAPQciaH1uEuDUGAIA1BCEAABCwCEKWHJ59ns7SAADYQxCyxnH8XQAAgFcRhGxx5yBahAAAsIUgZMnhW2PMsQEAgD0EoXr4ZooNAABgG0GoHr6YYuMwOksDAGBPo4NQVVWVQkJCtHr1am/UAwAA4DONDkKhoaFKT09XTU2NN+oJIMw+DwCAbU26NXb//ffrvvvuU2FhoafrCRiHO0sz1xgAAPaENOVDL7zwgjZt2qTU1FR16tRJ0dHRtbYvXbrUI8W1Zg4GjQEAYF2TgtDo0aM9XEbgMdwaAwDAuiYFoYcfftjTdQAAAPhck4LQYUuWLNG6deskSb169VL//v09UlRg4N4YAAC2NSkIFRQU6KqrrtJXX32luLg4SVJRUZHOPfdcTZ06VYmJiZ6ssVUi/gAAYF+TRo3dfvvtKi0t1Zo1a1RYWKjCwkKtXr1aJSUluuOOOzxdY6vkYK4xAACsa1KL0PTp0zVz5kz17NnTvS4zM1O5ubkaMWKEx4pr3bg1BgCAbU1qEXI6nQoNDa2zPjQ0VE6ns9lFAQAA+EKTgtB5552nCRMmaNeuXe51O3fu1G9+8xsNGzbMY8XZ4otJVw8Pn2euMQAA7GlSEHrhhRdUUlKijIwMde3aVV27dlXnzp1VUlKi559/3tM1+pwvJ10FAAD2NKmPUFpampYuXaqZM2dq/fr1kqSePXtq+PDhHi2uVXPwQEUAAGxrdBCqqqpSZGSkli9frvPPP1/nn3++N+oKAI7j7wIAALyK2edto48QAADWMPu8Jcw1BgCAfcw+DwAAAhazz9vi4IGKAADY1uggVF1dLYfDoRtuuEEdO3b0Rk0AAAA+0eg+QiEhIXr66adVXV3tjXoCEC1CAADY0uQnS3/99deeriXAuG6NObg1BgCANU3qIzRq1Cjdc889WrVqlQYMGFCns/Qll1zikeICATEIAAB7mhSEbr31VknSs88+W2ebw+HgGUMnwPBkaQAArGtSEGKGeQAA0Bo0qo/QhRdeqOLiYvfyk08+qaKiIvfyDz/8oMzMTI8V17oxfB4AANsaFYQ+++wzVVRUuJcnTZpU6+nS1dXV2rBhg+eqa9WYawwAANsaFYTMT1ovfrqMJuAaAgBgTZOGz7d2ubm5yszMVFZWlvdO4qBFCAAA2xoVhBwOhxw/+QH/6XJrkJOTo7Vr1yovL8/r56I9CAAAexo1aswYo/Hjxys8PFySVF5erptvvtn9HKGj+w+hYe7Z5w0j8AAAsKVRQSg7O7vW8rXXXltnn+uuu655FQEAAPhIo4LQlClTvFVHAOPmGAAAttBZ2pZW2LcKAICWhiBkmYMGIQAArCEIWeNqETKiszQAALYQhGzhzhgAANYRhKxhrjEAAGwjCFliaBICAMA6ghAAAAhYBCFbGD4PAIB1BCHb6CMEAIA1BCFLjHG1CK3ZWWS3EAAAAhhByJKtPxyUJJWWV8vQKgQAgBUEIUs6J7aVJEU5ylV0sMpyNQAABCaCkCV9Bp0rSRoatEZ7SsstVwMAQGAiCFkS1fN8SVKmY5v2/lBouRoAAAITQciWtinaH5ygIIdR1c6VtqsBACAgEYTqkZubq8zMTGVlZXn1PLsjT5EkJWyb7tXzAACA+hGE6pGTk6O1a9cqLy/Pq+fZeNJoSVLnghlePQ8AAKgfQciiilRXi1Pbqr1SFR2mAQDwNYKQRbEJHVRmIhQkIxXvsF0OAAABhyBkUXJshHaYJNfC/q1WawEAIBARhCxKjonQNpMsSXIWbrFcDQAAgYcgZFFi23Bt/7FFKOjT39FPCAAAHyMIWRQaHKSKsLgjK779zFotAAAEIoKQZSvbnHlkYds39goBACAAEYQsC0nqod9X3ehayF9ltxgAAAIMQciy7klttMbZ2bWwd73dYgAACDAEIcu6JbfVd6aDnHJIB3+QSvfYLgkAgIBBELKsW2IblStcm5TmWrFjod2CAAAIIAQhy7okRivIIS2sPtm1YvsCuwUBABBACEKWRYQGKz0hSnlO10z02s7IMQAAfIUg5Ae6JbXVYuePLUK7lkk7FtktCACAAEEQ8gPdk9tol9odWfGP86XyEnsFAQAQIAhCfiCzQ4wkR+2V6/5npRYAAAIJQcgPDO6cIEla50w/spLRYwAAeB1ByA8kxUSoS2K0flX5W5XEZbpWbv5ScjrtFgYAQCtHEPITp3dpp51K1F/T/ypFxEpF26W5f7ZdFgAArRpByE8M75kkSfpwXbFMv3GulV/8USpYZ7EqAABaN4KQnzizW6IiQoNUUFqhraf86siGvFftFQUAQCtHEPITYSFB6pcWJ0laUBAsXfeBa8PSf0kVpfYKAwCgFSMI+ZGsDNfosbythVLns6V23aWaCil3MB2nAQDwAoKQHxn4YxCatnSnthUelM7+vWtDyU7pv7+0WBkAAK0TQciPnN4lQV0SoyVJ//xmm3TqldLgm10b10yTFr1isToAAFofgpAfCQ8J1r2jekqSPlq5SzVOI13wpJTQ1bXDJ3dJcydLxtgrEgCAVoQg5GfOOrm9YiJCVFBaoQWbf5AcDulXM4/sMPNhaf4L9goEAKAVIQj5mfCQYP1f31RJ0j/mbpExRopKkG75RgqJdO30+QNSWYHFKgEAaB0IQvXIzc1VZmamsrKyrJw/e0iGgoMc+mJ9gdbu/nEW+uRe0u1Ljuy05j0rtQEA0JoQhOqRk5OjtWvXKi8vz8r5e6S01Rld20mSlmzbf2RD7EnSufe73u9caqEyAABaF4KQnxrQKV6StPToICRJyb1d/1w5VVr2po+rAgCgdSEI+anT0l1B6JvvfnD1EzrspAFH3n/6e6nqkI8rAwCg9SAI+al+6XEKCXKooLRCby7cfmRD22Tp5nmu95Vl0sbP7BQIAEArQBDyUzERobrxrC6SpLcWba+9MaW3NHSC6/3qd31cGQAArQdByI/d+LMuCglyaM2uEn23t6z2xt5XuP658XOpvNj3xQEA0AoQhPxYQnSYzuzeXpL0lxkba29M6SO1P9k1Kev7t0rT73O9vnmBCVoBADhBIbYLQMMmnn+y5ny7Tx+t3K1rBu3TGd1cwUgOh9TnSunLP0rrP6r9oeBQKW1w7XWxaVJ0O98UDQBAC0EQ8nOndozTlQM76q1FO/Thil1HgpAkDblVCgqWKn586OL3i6Wtc1yjyX4qPFa6YxlhCACAoziMYQbPYykpKVFsbKyKi4sVExNjrY55m/Zp7KsLFRUWrIX3DVPbiND6d9y/VZp6rXSosPb6g4VS9SEpvrMU3lY67Tpp0I1erxsAABsa8/tNEGqAvwSh6hqnBk2apcIDlYqNDNVXd52j+OiwEz/Agpek6XfXXjf2Xan7+Z4tFAAAP9CY3286S7cAIcFBmjTG9UTp4kNV+teCbY07wKAbpRs+l66dJrV1TeiqN6+Q8ld5uFIAAFoWWoQa4C8tQod9sHynJkxdrsjQYOU9MFxtwpvQxWvj59J/fu56HxwmBf14jKAQadhD3DIDALR4tAi1Upf0TVVGuygdqqrRf5d837SDnDxCuuYd1/uaSqnqoOtVUSLNflqanyuVl3iuaAAA/BhBqAVxOBy6tN9JkqSnP9ugsorqph3o5BHS7zZLE1a4XrctkUKjpLI90mf3SXOf9WDVAAD4L4JQC3Pbed3UqV2UyiqqdcOUPJWWVzXtQNHtpPgM16t9N+mKKdLJo1zb5v5F2rXMUyUDAOC3CEItTGhwkG4/r7skadHWQt365lJ5pJtXjwuky14+svz2OInuYwCAVo4g1AJdftpJeuzSXgoOcmjOt/v00tebPXPgiBjpF2+63hfvqPvEagAAWhmCUAvkcDh03ZAM/XbEyZKkP01fr6KDlZ45eI8Lpch41/sN0z1zTAAA/BRBqAW79vROcjhc7yfP/NYzBw0KcvUXkqRvP3cNt//pa8scqaaJHbUBAPAjzDXWgsVEhOqqrDS9tWiH3lq0XaP7n6R+aXHNP3CnM6TQaOlAwZFnDv3U8EelM+9s/rkAALCIByo2wN8eqFifAxXVGvqnL1R00DV67OVxAzSiV0rzD7zoFWn5m3U7TFeUSIWbpbYdXIEpNk0670EpmEwNAPAPzDXmIS0hCEnSut0lGvXcHPfywvuGKTkmwjsnK9ktTe4tOY+6NfaLN6We/+ed8wEA0Eg8WTrA9OwQozm/P9e9PHjSLH2wfKdnhtX/VEwH6boPpAv+JHX58Zxvj5UqD3r+XAAAeBlBqJVIS4jSX6/u716eMHW5/t3YyVlPVMaZ0uk3S+fed2TdnGckp9M75wMAwEsIQq3IJX1T9daNp+ucHomSXCPJmjwNx4lIGyT1+bEz9Zw/S4v/4b1zAQDgBQShVmZI13Z65bqBymgXpR8OVGri28u9e8Kf/fbI+0WvSF9OkvZu9O45AQDwEIJQKxQaHKRJl/VRkEP6fO0e3fB6nqprvHTbKqmndOtC1/t9G6Sv/yR9kOOdcwEA4GGMGmtASxk1dixPfLJOf5/tmn5jSJd2evNXgxUU5PDOyZa+4ZqodfFrkiPoSEfq48kYWrtVCQCAZmL4vIe09CAkSa/O2aw/frxOkvToJb3U+6QY9U+L914gyj1d2ruucZ+5c5UUl+6degAAAYcg5CGtIQg5nUZnPf2lvt9/yL3u0n6pyspIUHhIkC46tYOiwjz4MMTi76Wtc09s5vq5f3HdTpOkO5ZJCV08VwcAIGARhDykNQQhSdq8t0y/emOxNu89UGfb2MHpenxMHwtVSZrzrDTrUdf7s++uPRwfAIAmaszvN/MiBIAuiW00a+LZ2lRQpm8LyvTxyt36bm+Z1ueX6s2F21V4oFLP/LyvosKC5XB46ZZZfc78jVS6W1r0srTtG9+dFwCAH9Ei1IDW0iJ0LI98uEavf7PVvdwlMVovXH2aenZo67tAVLBe+ttgKSRSumuDFNTMbB4W7Zm6AAAtFrfGPKS1ByFJmrVuj+56Z4X2/zhpqyQN7BSvv48boHZtwr1fgNMpPd1FOrTfM8fLHC1d+U/PHAsA0CIRhDwkEIKQJFXVOJVfXK6c/yzVyu+L3ev/e8sQ744wO+zzB6RvnvfQwRzSb9d7r2UoJFIK5o4yAPgzgpCHBEoQOswYo+U7ijTmb0f663RpH61/3jBIaQlR3j15VblkmvnQx7+dLhV5aX61wyLjpV/PkeLSvHseAECTMfs8msThcKh/erw+u/MstQ13tXps3ndA2VMWaf+BSu+ePDRCCotq3qv/OElebr06tF/aON275wAA+AwtQg0ItBaho9U4jXYXH9Iv/r5AO4sOaWi3dnrzV6fbLuv4qiub37J0LLOfluY8c/z9HEHSOfdKZ//eO3UAABpEixCaLTjIoY7xUZpyfZbCQoI0b9MPGjxppjbuKbVdWsNCwlytS9549RothUQcvwbjdE05AgDwe7QINSCQW4SOdvQw+/ZtwvSP7Cz1TYuzWpM1lQelqoPH3l51UJp8qiQjte0g9626oBDpnHuk/mN9USUABDQ6S3sIQcjFGKO1u0v0638t0ff7Dyk4yKG7RvTQVVlpWru7RBGhQTotPd63D2P0Z6//n7R1Tt31yb2lW+b5vh4ACDAEIQ8hCNW27YcDuvGNxdq4p6zOtnbRYep1Uqx7uWtitK4cmKaQIIeS2kYoNirUl6XaVV0h7d0g6cc/Wof2S29c6uo71OVcyeGQ+lwp9f2F1TIBoLUiCHkIQaiu8qoaPfq/tXpr0fYT/kxEaJD+Pm6gzj450YuV+TFjpOdPkwo3H1kXESvdvc0VigAAHkUQOsqOHTs0btw4FRQUKCQkRA8++KB+/vOfn9BnCULHVlpepTnf7tPQru1VdKhSS7YdeTL0hvxSfbYmXyXl1aqqcaq0vFoxESH68LYzldE+QKfA2L9N2j7fFYr+N0GqqZDO+r00YLwUe5Lt6gCgVSEIHWX37t3as2eP+vXrp/z8fA0YMEAbN25UdPTxf5AJQs1XUV2jq15eoGXbi5QQHaY/XNq71vZ+6XE6KS7SUnWWvDpc+j7P9T7zUulKRpgBgCcRhBrQt29fffTRR0pLO/6TgQlCnrFxT6lG/GX2Mbf3T4/TyF4pOu+UJEWHh7T+YLRzqTTvOWnt+66RZePeO/5nwqKluHSvlwYArUGLCkKzZ8/W008/rSVLlmj37t167733NHr06Fr75Obm6umnn1Z+fr769u2r559/XoMGDWr0uZYsWaLs7GytXr36hPYnCHnOX2d9q3mb9tVat2pnsQ5W1tTZ97xTkvTslX0VFxXmq/J8r6JUeiJN7g7VJ2L0i1K/a7xWEgC0Fo35/bY+e+SBAwfUt29f3XDDDbrsssvqbH/77bc1ceJEvfTSSxo8eLAmT56skSNHasOGDUpKSpIk9evXT9XV1XU++/nnnys1NVWSVFhYqOuuu06vvPLKMWupqKhQRUWFe7mkpKS5Xw8/umNYd90xrHutddU1Ts3dtE8frdyt2Rv3qqyiWgcra/TF+gL1e2yG0hOidPlpHdUjpY2G9UxWaHArev5neFsp61fSmhNoDaoulyrLpG9nEIQAwMOstwgdzeFw1GkRGjx4sLKysvTCCy9IkpxOp9LS0nT77bfrnnvuOaHjVlRU6Pzzz9eNN96ocePGHXO/Rx55RI8++mid9bQI+c5HK3fpvmmrVFJeO9he2CdFdw4/WRntohUW0ooC0YnYNFP69+VSaLSU1LNxnw2LlkY9JSWd4p3aAMAPtahbY0f7aRCqrKxUVFSU3n333VrhKDs7W0VFRfrggw+Oe0xjjK655hr16NFDjzzySIP71tcilJaWRhDyscpqp77bW6Z/LdimPcXl+nrjXlU7Xf+ZJrYN1/+d2kEOOdQ2IkQ3ntVFbcKtN2x618FC6S+9Gn6idUMG/Vq68CnP1gQAfqxF3RpryL59+1RTU6Pk5ORa65OTk7V+/foTOsa8efP09ttv69RTT9X7778vSfrXv/6lPn361Nk3PDxc4eHhza4bzRMWEqSeHWI0aYzr39G0pd/rz59v1M6iQ9pbWqEp87a6931u1re66NQOmjCsu05ObmupYi+LSpBunivt29i4z237Rvrmr65/rvufd2qrT2SC1OkMnpEEoEXw6yDkCWeeeaacTi/NRg6fuOy0jrrstI5asm2/vli/R8ZI+SXlmrZ0pyTp45W79fmafE296XT1SIlRsMOhyLBgy1V7WLuurldjxHZ0BaE9q6S3r/VOXcfyi39LPS/27TkBoAn8Ogi1b99ewcHB2rNnT631e/bsUUpKiqWqYMuATvEa0CnevfzgRZl6ec5mvTpns6pqjC5/cb5724Rh3fWb80+2Uab/SO4tDbpJ2r3Cd+cs/l4q2SltX0AQAtAi+HUQCgsL04ABAzRr1ix3HyGn06lZs2bptttus1scrIuPDtPdF5yiX5/VRZe9+I027z3g3vbS199pTP+TFB5au2N1REiw4qNb8bD8ozkc0oVP+/acS/4p/e8Oafl/pJ1LvHee4FDXk7k7/8x75wAQEKwHobKyMm3atMm9vGXLFi1fvlwJCQlKT0/XxIkTlZ2drYEDB2rQoEGaPHmyDhw4oOuvv95i1fAncVFhmvmbs1VZ45TTGA3789faXVyuc575qt79rxzYUZPG9FFIaxqO7y/SBrv+eajQNaWINzmCCUIAms36qLGvvvpK5557bp312dnZev311yVJL7zwgvuBiv369dNf//pXDR482Ou18UDFlunLDQWa8NYylVfV7htWWXNkedKYPrpmME9q9opdy6SiHd47ftE26fMHXO9/v8XVmRwAjtJih8/7i9zcXOXm5qqmpkYbN24kCLUSTqfRyMmz9W1BmdqGh2jC8O5y/DiyySHXE60DdlLYlqS8RHryqClyrp4q9Rhlrx4Afocg5CG0CLU+e0rKde4zX9U7tYckDcpIcKUiScNOSdLY0zu1/ucUtUTTbpJWvu16P/CX0v89a7ceAH6FIOQhBKHW6asNBXp/2U73LF9OI/1vxa5j7j+0Wzs9cFGmenbgvwG/suJt6b2bpMSe0tA7bFcDtB4RcVL3EVJwy/2fQIKQhxCEAkdBabmWbN3vDkdvzN+qBZsLa+1zcd9UPXZJr8AZdebvdq+Q/n6W7SqA1mnMy1LfX9iuoskIQh5CEAps1TVO/WPuFj05fb2O/lPSNiJEl5/WUbef103t2tR+EvnBymqt3VUi51H7OxxSzw4x3GLzNGOkGQ9JBWttVwK0HoWbXa/BN0uj/mS7miYjCHkIQQiSVHywSrlfbdLLszfX2XbD0M6K+vEp1kZGb3yzTaUV1XX2iwwN1vVDMxR01LQTHeIidHVWuoKCmIoCgJ9Y+ob04e1Su25S5ugT/1zHLKnHBV4rq7EIQh5CEMLRig9Vqfhglf702Xp9vHJ3g/tmtItSUJBDxkhb9h045n4PXNRTNwztTBgC4B92LpFeOa/xnwsKkX63SYqMP/6+PkAQ8hCCEI7lu71lmrpou6pqav/xcTik8zOTdUbX9u51eVsL9cmq3bVur+0uPqTP1rimjmnfJkyPXNJLkaHBSo2LpFM2AHuMkRa97Lo9dqJWvCWVF0u/nCGlDfJebY1AEPIQghC8pbrGqbGvLtTCLYV1to3ITD7m84wSosN0w9DOCgvhqdgA/MQ/L5a2zJZOHiW179b4zweFSsMf9mhJBKFm4oGK8AVjjL7euFe5X25SZY3Rih1FJ/S5sOAgZabW/e+xZ4e2+uPoPgrmNhsAX5p+r7Tgb03/fGiUdH/D3Q0aiyDkIbQIwZfKKqr12twtOlBPZ2tJWvl9seZv/qHBY8REhOilcQMUGhyk0OAg9U6NYU41AN5VViDl/UOqOti0zweHSsMe8mhJBCEPIQjB3yzbvl8/lFXWWf/p6nz9d+n3ddZ3TYzWVVmuOdU6xkfqgt4p7mlFAKC1Igh5CEEILckHy3cq98tNqq4xKjpUpcIDdQNT346x+vOV/dQtqY2FCgHANwhCHkIQQkv27wXbtGTbfknSnG/3aV9ZhXtbl6M6Y2emxmjyL/pxCw1Aq9GY328edQu0Utee3knXnt5JklRV49SUeVs06ZP1kqTNRz3baPO+A+qR3FY/Ozmx1uc7t49WbGSo7woGAAtoEWoALUJobQpKyrWt8EiHxtkb9+r5LzbVu29wkEMPX5ypiJDgYx4vOjxEF/ROYaQaAL9CixCAeiXFRCgpJsK93LdjnDbkl2rt7hL3OmOknUWHVOM0euiDNcc9Zka7KI0/I0PXDcngCdkAWhxahBpAixAC1cLNP+jVuVtU4zz2Xw8b8ku1s+iQezk1NkK9Too95v7BDoeuG9JJZ3Rrf8x9AMAT6CzdTDxQETg+Y4z+vXC7pszbos17jz2f2k91T2qj+kbwx0SE6tkr+ym9XZQHqwQQiAhCHkKLEHB8NU6j6avzVVJedcx9KqudevjD499mk1wj2l7NHqiMdtHcagPQJAQhDyEIAZ6z/0Cl1uWX1LttU0FZnf5IPTvE6IOcocyrBqDRCEIeQhACfKekvEqfrc7Xgx+sVnmVU5J0+3ndNDAjocHPZXaIUWLbcF+UCKCFIAh5CEEI8D1jjP4xd4v++PG6E/7ME5f1UXhIkCJCg3XeKUmKCD32kH8ArR/D5wG0WA6HQ9ee3knLdhRp675jd8I2Ru5h//dOW+Ven5YQqevP6KyQYIdG9kpR8lGPCwCAn6JFqAG0CAH+bc63e/Xa3C2qdhptLzyobT/Unf16wrDu7lFqafFRuuy0k5h4FmjluDXmIQQhoOUwxuiVOZu1emeJSsur9OWGvfXud83gdE0a08fH1QHwJW6NAQg4DodDN53V1b08Y+0efb2xwL38wfJdKi2v1n8WbtfmvWW66awuOu+UZBulAvAjtAg1gBYhoPU4WFmtc57+SgWlFe51F/ZJUUxEwxPLtm8TrtuHdVN4A3OuAfAv3BrzEIIQ0LoUH6rSfxZu15+mr2/U59q3Cdd5pyR6tJYzuyfqkr6pHj0mABeCkIcQhIDWaUfhQX20crdqnM4G95u+Jl+rd9b/EEhPGN4zSe2iaz8DKSjIoSsHdlT/9HivnRdo7QhCzcRcYwAkyek0emfJDv1woNKjx31q+obj7jP+jAz9+uwu6hAb6dFzA4GAIOQhtAgB8Ib84nJ9uGKnqmpq//V7qLJGL3y5qda6G4Z2rvcYocEO/SIrTV0S23itTqClIgh5CEEIgK+t2FGkf8zdog9X7Dqh/W8/r1ut5YjQYF0zKF3x0WHeKA9oEQhCHkIQAmDLJ6t2a/XO4nq3/VBWqbcX7zjmZ2MjQ3XbuUcCUp+OsTq9SzuP1wj4K4KQhxCEAPirmWv3aO6mfbXWrd1dokVbCuvdf+L5Jys1rnZ/I4ekod3aKyWWaUjQuhCEPIQgBKAlMcbotXlbteaolqRpy3Y2+JnQYIdeuW6ge9qRhKgw9ekY69U6AW8jCHkIQQhAS7d2V4mem7VRFdW1HxVQUeXU/M0/1PuZ+y48Ref0SPJYDSmxEcd9cCXgSQQhDyEIAWjN3lq0Xf9ZuF3OH38G1uzyzjOT2kaEaMZvzlZs5ImFIYfD1ekbaCqCkIcQhAAEkvzico2fsqjWNCTNVdjEZzDlnNtVvxt5isfqQGAhCHkIQQgAmufDFbs08e3lqnY2/qemfZswBQc5mnX+1LhITRmfpbgoHicQSAhCHkIQAoDmq6iuUc0JBiFjpMtf/Ebr80s9WsOZ3drXWu6VGqO7LzhFQc0MWvBPjfn9DvFRTQCAABUe0rj+Pu/nDNWmgrJmn/fdJd/r9W+2SlKdRw3M3bRPhQcqFRdVt99SkMOhi/umqvdJjJ4LBLQINYAWIQBouYwx+mrDXhUfqqq1/qOVuzVz3Z4GP9upXZR+O6KHx2sKckind2mn9m3Cj7lP3tZC7S4u9/i5myI8JEhnn5zY4jqvc2usmZh0FQBar8IDlfrnN1tVXlVT7/Z/Ldimg5X1b/OEfmlxeuGa/vVuW7e7VDe+sdhr526KsYPTdcs5Xb12fIfDoZPiPDu5MEHIQ2gRAoDA898l3+u/S7+XN34d87YWnlDH8fSEKI+Hg8Y6VFWj5TuKvH6eyNBgrfvDBR49Jn2EAABoossHdNTlAzp65dhPfLJO/5y/tcGQFRcVqlezB+rk5LZeqeFE1TiNrnttoRZv3e/V84SHBnn1+MdDi1ADaBECAKDlaczvt90YBgAAYBFBCAAABCyCEAAACFgEIQAAELAIQgAAIGARhAAAQMAiCAEAgIBFEAIAAAGLIAQAAAIWQQgAAAQsghAAAAhYBCEAABCwCEL1yM3NVWZmprKysmyXAgAAvIjZ5xvA7PMAALQ8jfn9DvFRTS3S4YxYUlJiuRIAAHCiDv9un0hbD0GoAaWlpZKktLQ0y5UAAIDGKi0tVWxsbIP7cGusAU6nU7t27VLbtm3lcDg8euySkhKlpaVpx44d3HbzIq6z73CtfYPr7BtcZ9/w1nU2xqi0tFSpqakKCmq4OzQtQg0ICgpSx44dvXqOmJgY/pD5ANfZd7jWvsF19g2us2944zofryXoMEaNAQCAgEUQAgAAAYsgZEl4eLgefvhhhYeH2y6lVeM6+w7X2je4zr7BdfYNf7jOdJYGAAABixYhAAAQsAhCAAAgYBGEAABAwCIIAQCAgEUQsiQ3N1cZGRmKiIjQ4MGDtWjRItsltRhPPPGEsrKy1LZtWyUlJWn06NHasGFDrX3Ky8uVk5Ojdu3aqU2bNrr88su1Z8+eWvts375dF110kaKiopSUlKTf/e53qq6u9uVXaVGefPJJORwO3Xnnne51XGfP2blzp6699lq1a9dOkZGR6tOnjxYvXuzebozRQw89pA4dOigyMlLDhw/Xt99+W+sYhYWFGjt2rGJiYhQXF6df/vKXKisr8/VX8Vs1NTV68MEH1blzZ0VGRqpr1676wx/+UGs+Kq5z482ePVsXX3yxUlNT5XA49P7779fa7qlrunLlSv3sZz9TRESE0tLS9NRTT3nmCxj43NSpU01YWJh57bXXzJo1a8yNN95o4uLizJ49e2yX1iKMHDnSTJkyxaxevdosX77cXHjhhSY9Pd2UlZW597n55ptNWlqamTVrllm8eLE5/fTTzRlnnOHeXl1dbXr37m2GDx9uli1bZj755BPTvn17c++999r4Sn5v0aJFJiMjw5x66qlmwoQJ7vVcZ88oLCw0nTp1MuPHjzcLFy40mzdvNp999pnZtGmTe58nn3zSxMbGmvfff9+sWLHCXHLJJaZz587m0KFD7n0uuOAC07dvX7NgwQIzZ84c061bN3P11Vfb+Ep+6fHHHzft2rUzH330kdmyZYt55513TJs2bcxzzz3n3ofr3HiffPKJuf/++820adOMJPPee+/V2u6Ja1pcXGySk5PN2LFjzerVq81bb71lIiMjzd///vdm108QsmDQoEEmJyfHvVxTU2NSU1PNE088YbGqlqugoMBIMl9//bUxxpiioiITGhpq3nnnHfc+69atM5LM/PnzjTGuP7hBQUEmPz/fvc+LL75oYmJiTEVFhW+/gJ8rLS013bt3NzNmzDBnn322OwhxnT3n7rvvNmeeeeYxtzudTpOSkmKefvpp97qioiITHh5u3nrrLWOMMWvXrjWSTF5ennufTz/91DgcDrNz507vFd+CXHTRReaGG26ote6yyy4zY8eONcZwnT3hp0HIU9f0b3/7m4mPj6/198bdd99tevTo0eyauTXmY5WVlVqyZImGDx/uXhcUFKThw4dr/vz5FitruYqLiyVJCQkJkqQlS5aoqqqq1jU+5ZRTlJ6e7r7G8+fPV58+fZScnOzeZ+TIkSopKdGaNWt8WL3/y8nJ0UUXXVTrekpcZ0/68MMPNXDgQP385z9XUlKS+vfvr1deecW9fcuWLcrPz691rWNjYzV48OBa1zouLk4DBw507zN8+HAFBQVp4cKFvvsyfuyMM87QrFmztHHjRknSihUrNHfuXI0aNUoS19kbPHVN58+fr7POOkthYWHufUaOHKkNGzZo//79zaqRSVd9bN++faqpqan1wyBJycnJWr9+vaWqWi6n06k777xTQ4cOVe/evSVJ+fn5CgsLU1xcXK19k5OTlZ+f796nvn8Hh7fBZerUqVq6dKny8vLqbOM6e87mzZv14osvauLEibrvvvuUl5enO+64Q2FhYcrOznZfq/qu5dHXOikpqdb2kJAQJSQkcK1/dM8996ikpESnnHKKgoODVVNTo8cff1xjx46VJK6zF3jqmubn56tz5851jnF4W3x8fJNrJAihRcvJydHq1as1d+5c26W0Ojt27NCECRM0Y8YMRURE2C6nVXM6nRo4cKAmTZokSerfv79Wr16tl156SdnZ2Zaraz3+3//7f3rzzTf1n//8R7169dLy5ct15513KjU1lescwLg15mPt27dXcHBwnZE1e/bsUUpKiqWqWqbbbrtNH330kb788kt17NjRvT4lJUWVlZUqKiqqtf/R1zglJaXefweHt8F166ugoECnnXaaQkJCFBISoq+//lp//etfFRISouTkZK6zh3To0EGZmZm11vXs2VPbt2+XdORaNfT3RkpKigoKCmptr66uVmFhIdf6R7/73e90zz336KqrrlKfPn00btw4/eY3v9ETTzwhievsDZ66pt78u4Qg5GNhYWEaMGCAZs2a5V7ndDo1a9YsDRkyxGJlLYcxRrfddpvee+89ffHFF3WaSwcMGKDQ0NBa13jDhg3avn27+xoPGTJEq1atqvWHb8aMGYqJianzgxSohg0bplWrVmn58uXu18CBAzV27Fj3e66zZwwdOrTOIyA2btyoTp06SZI6d+6slJSUWte6pKRECxcurHWti4qKtGTJEvc+X3zxhZxOpwYPHuyDb+H/Dh48qKCg2j97wcHBcjqdkrjO3uCpazpkyBDNnj1bVVVV7n1mzJihHj16NOu2mCSGz9swdepUEx4ebl5//XWzdu1ac9NNN5m4uLhaI2twbLfccouJjY01X331ldm9e7f7dfDgQfc+N998s0lPTzdffPGFWbx4sRkyZIgZMmSIe/vhYd0jRowwy5cvN9OnTzeJiYkM6z6Oo0eNGcN19pRFixaZkJAQ8/jjj5tvv/3WvPnmmyYqKsr8+9//du/z5JNPmri4OPPBBx+YlStXmksvvbTeIcj9+/c3CxcuNHPnzjXdu3cP6GHdP5WdnW1OOukk9/D5adOmmfbt25vf//737n24zo1XWlpqli1bZpYtW2YkmWeffdYsW7bMbNu2zRjjmWtaVFRkkpOTzbhx48zq1avN1KlTTVRUFMPnW7Lnn3/epKenm7CwMDNo0CCzYMEC2yW1GJLqfU2ZMsW9z6FDh8ytt95q4uPjTVRUlBkzZozZvXt3reNs3brVjBo1ykRGRpr27dub3/72t6aqqsrH36Zl+WkQ4jp7zv/+9z/Tu3dvEx4ebk455RTz8ssv19rudDrNgw8+aJKTk014eLgZNmyY2bBhQ619fvjhB3P11VebNm3amJiYGHP99deb0tJSX34Nv1ZSUmImTJhg0tPTTUREhOnSpYu5//77aw3J5jo33pdfflnv38nZ2dnGGM9d0xUrVpgzzzzThIeHm5NOOsk8+eSTHqnfYcxRj9QEAAAIIPQRAgAAAYsgBAAAAhZBCAAABCyCEAAACFgEIQAAELAIQgAAIGARhAAAQMAiCAEAgIBFEAKA43A4HHr//fdtlwHACwhCAPza+PHj5XA46rwuuOAC26UBaAVCbBcAAMdzwQUXaMqUKbXWhYeHW6oGQGtCixAAvxceHq6UlJRar/j4eEmu21YvvviiRo0apcjISHXp0kXvvvturc+vWrVK5513niIjI9WuXTvddNNNKisrq7XPa6+9pl69eik8PFwdOnTQbbfdVmv7vn37NGbMGEVFRal79+768MMP3dv279+vsWPHKjExUZGRkerevXud4AbAPxGEALR4Dz74oC6//HKtWLFCY8eO1VVXXaV169ZJkg4cOKCRI0cqPj5eeXl5eueddzRz5sxaQefFF19UTk6ObrrpJq1atUoffvihunXrVuscjz76qK688kqtXLlSF154ocaOHavCwkL3+deuXatPP/1U69at04svvqj27dv77gIAaDqPzGEPAF6SnZ1tgoODTXR0dK3X448/bowxRpK5+eaba31m8ODB5pZbbjHGGPPyyy+b+Ph4U1ZW5t7+8ccfm6CgIJOfn2+MMSY1NdXcf//9x6xBknnggQfcy2VlZUaS+fTTT40xxlx88cXm+uuv98wXBuBT9BEC4PfOPfdcvfjii7XWJSQkuN8PGTKk1rYhQ4Zo+fLlkqR169apb9++io6Odm8fOnSonE6nNmzYIIfDoV27dmnYsGEN1nDqqae630dHRysmJkYFBQWSpFtuuUWXX365li5dqhEjRmj06NE644wzmvRdAfgWQQiA34uOjq5zq8pTIiMjT2i/0NDQWssOh0NOp1OSNGrUKG3btk2ffPKJZsyYoWHDhiknJ0fPPPOMx+sF4Fn0EQLQ4i1YsKDOcs+ePSVJPXv21IoVK3TgwAH39nnz5ikoKEg9evRQ27ZtlZGRoVmzZjWrhsTERGVnZ+vf//63Jk+erJdffrlZxwPgG7QIAfB7FRUVys/Pr7UuJCTE3SH5nXfe0cCBA3XmmWfqzTff1KJFi/SPf/xDkjR27Fg9/PDDys7O1iOPPKK9e/fq9ttv17hx45ScnCxJeuSRR3TzzTcrKSlJo0aNUmlpqebNm6fbb7/9hOp76KGHNGDAAPXq1UsVFRX66KOP3EEMgH8jCAHwe9OnT1eHDh1qrevRo4fWr18vyTWia+rUqbr11lvVoUMHvfXWW8rMzJQkRUVF6bPPPtOECROUlZWlqKgoXX755Xr22Wfdx8rOzlZ5ebn+8pe/6K677lL79u11xRVXnHB9YWFhuvfee7V161ZFRkbqZz/7maZOneqBbw7A2xzGGGO7CABoKofDoffee0+jR4+2XQqAFog+QgAAIGARhAAAQMCijxCAFo27+wCagxYhAAAQsAhCAAAgYBGEAABAwCIIAQCAgEUQAgAAAYsgBAAAAhZBCAAABCyCEAAACFj/H59cJrec5moVAAAAAElFTkSuQmCC",
"text/plain": [
"<Figure size 640x480 with 1 Axes>"
]
@@ -594,7 +2000,7 @@
},
{
"cell_type": "code",
- "execution_count": 37,
+ "execution_count": 23,
"id": "delayed-desire",
"metadata": {},
"outputs": [
@@ -602,7 +2008,7 @@
"name": "stdout",
"output_type": "stream",
"text": [
- "(92, 784)\n",
+ "(90, 784)\n",
"ICI\n"
]
},
@@ -613,14 +2019,14 @@
"traceback": [
"\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
"\u001b[0;31mValueError\u001b[0m Traceback (most recent call last)",
- "Cell \u001b[0;32mIn [37], line 28\u001b[0m\n\u001b[1;32m 26\u001b[0m \u001b[39mif\u001b[39;00m correct_labels\u001b[39m.\u001b[39mshape[\u001b[39m1\u001b[39m] \u001b[39m!=\u001b[39m rows\u001b[39m*\u001b[39mcols:\n\u001b[1;32m 27\u001b[0m \u001b[39mprint\u001b[39m(\u001b[39m\"\u001b[39m\u001b[39mICI\u001b[39m\u001b[39m\"\u001b[39m)\n\u001b[0;32m---> 28\u001b[0m \u001b[39mprint\u001b[39m(\u001b[39m-\u001b[39mnp\u001b[39m.\u001b[39mones((\u001b[39m1\u001b[39m,rows\u001b[39m*\u001b[39mcols\u001b[39m-\u001b[39mcorrect_labels\u001b[39m.\u001b[39mshape[\u001b[39m1\u001b[39m])))\n\u001b[1;32m 29\u001b[0m correct_labels \u001b[39m=\u001b[39m np\u001b[39m.\u001b[39mappend(correct_labels, \u001b[39m-\u001b[39mnp\u001b[39m.\u001b[39mones((\u001b[39m1\u001b[39m,rows\u001b[39m*\u001b[39mcols\u001b[39m-\u001b[39mcorrect_labels\u001b[39m.\u001b[39mshape[\u001b[39m1\u001b[39m])),\u001b[39m1\u001b[39m)\n\u001b[1;32m 31\u001b[0m correct_labels \u001b[39m=\u001b[39m correct_labels\u001b[39m.\u001b[39mreshape(cols,rows)\u001b[39m.\u001b[39mT\n",
+ "Cell \u001b[0;32mIn [23], line 28\u001b[0m\n\u001b[1;32m 26\u001b[0m \u001b[39mif\u001b[39;00m correct_labels\u001b[39m.\u001b[39mshape[\u001b[39m1\u001b[39m] \u001b[39m!=\u001b[39m rows\u001b[39m*\u001b[39mcols:\n\u001b[1;32m 27\u001b[0m \u001b[39mprint\u001b[39m(\u001b[39m\"\u001b[39m\u001b[39mICI\u001b[39m\u001b[39m\"\u001b[39m)\n\u001b[0;32m---> 28\u001b[0m \u001b[39mprint\u001b[39m(\u001b[39m-\u001b[39mnp\u001b[39m.\u001b[39mones((\u001b[39m1\u001b[39m,rows\u001b[39m*\u001b[39mcols\u001b[39m-\u001b[39mcorrect_labels\u001b[39m.\u001b[39mshape[\u001b[39m1\u001b[39m])))\n\u001b[1;32m 29\u001b[0m correct_labels \u001b[39m=\u001b[39m np\u001b[39m.\u001b[39mappend(correct_labels, \u001b[39m-\u001b[39mnp\u001b[39m.\u001b[39mones((\u001b[39m1\u001b[39m,rows\u001b[39m*\u001b[39mcols\u001b[39m-\u001b[39mcorrect_labels\u001b[39m.\u001b[39mshape[\u001b[39m1\u001b[39m])),\u001b[39m1\u001b[39m)\n\u001b[1;32m 31\u001b[0m correct_labels \u001b[39m=\u001b[39m correct_labels\u001b[39m.\u001b[39mreshape(cols,rows)\u001b[39m.\u001b[39mT\n",
"File \u001b[0;32m~/.local/lib/python3.10/site-packages/numpy/core/numeric.py:204\u001b[0m, in \u001b[0;36mones\u001b[0;34m(shape, dtype, order, like)\u001b[0m\n\u001b[1;32m 201\u001b[0m \u001b[39mif\u001b[39;00m like \u001b[39mis\u001b[39;00m \u001b[39mnot\u001b[39;00m \u001b[39mNone\u001b[39;00m:\n\u001b[1;32m 202\u001b[0m \u001b[39mreturn\u001b[39;00m _ones_with_like(shape, dtype\u001b[39m=\u001b[39mdtype, order\u001b[39m=\u001b[39morder, like\u001b[39m=\u001b[39mlike)\n\u001b[0;32m--> 204\u001b[0m a \u001b[39m=\u001b[39m empty(shape, dtype, order)\n\u001b[1;32m 205\u001b[0m multiarray\u001b[39m.\u001b[39mcopyto(a, \u001b[39m1\u001b[39m, casting\u001b[39m=\u001b[39m\u001b[39m'\u001b[39m\u001b[39munsafe\u001b[39m\u001b[39m'\u001b[39m)\n\u001b[1;32m 206\u001b[0m \u001b[39mreturn\u001b[39;00m a\n",
"\u001b[0;31mValueError\u001b[0m: negative dimensions are not allowed"
]
},
{
"data": {
- "image/png": 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",
+ "image/png": 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",
"text/plain": [
"<Figure size 800x800 with 1 Axes>"
]
@@ -667,13 +2073,13 @@
},
{
"cell_type": "code",
- "execution_count": null,
+ "execution_count": 28,
"id": "endless-addition",
"metadata": {},
"outputs": [
{
"data": {
- "image/png": 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",
+ "image/png": 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",
"text/plain": [
"<Figure size 640x480 with 1 Axes>"
]
@@ -698,7 +2104,7 @@
},
{
"cell_type": "code",
- "execution_count": 42,
+ "execution_count": 29,
"id": "another-setting",
"metadata": {},
"outputs": [],
@@ -737,7 +2143,7 @@
},
{
"cell_type": "code",
- "execution_count": 43,
+ "execution_count": 30,
"id": "decreased-candidate",
"metadata": {},
"outputs": [
@@ -745,23 +2151,24 @@
"name": "stdout",
"output_type": "stream",
"text": [
- "[[0.01439665]\n",
- " [0.01451826]\n",
- " [0.01512632]]\n",
+ "(5, 3)\n",
+ "[[0.00863799]\n",
+ " [0.00871095]\n",
+ " [0.00907579]]\n",
"-0.025\n"
]
},
{
"ename": "AssertionError",
- "evalue": "\nArrays are not almost equal to 8 decimals\n\nMismatched elements: 3 / 3 (100%)\nMax absolute difference: 0.02550776\nMax relative difference: 3.61328441\n x: array([[0.01439665],\n [0.01451826],\n [0.01512632]])\n y: array([[-0.01111111],\n [-0.00555556],\n [ 0.02222222]])",
+ "evalue": "\nArrays are not almost equal to 8 decimals\n\nMismatched elements: 3 / 3 (100%)\nMax absolute difference: 0.0197491\nMax relative difference: 2.56797064\n x: array([[0.00863799],\n [0.00871095],\n [0.00907579]])\n y: array([[-0.01111111],\n [-0.00555556],\n [ 0.02222222]])",
"output_type": "error",
"traceback": [
"\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
"\u001b[0;31mAssertionError\u001b[0m Traceback (most recent call last)",
- "Cell \u001b[0;32mIn [43], line 26\u001b[0m\n\u001b[1;32m 18\u001b[0m grad_w_exp \u001b[39m=\u001b[39m np\u001b[39m.\u001b[39marray([[\u001b[39m-\u001b[39m\u001b[39m0.01111111\u001b[39m],\n\u001b[1;32m 19\u001b[0m [\u001b[39m-\u001b[39m\u001b[39m0.00555556\u001b[39m],\n\u001b[1;32m 20\u001b[0m [ \u001b[39m0.02222222\u001b[39m]])\n\u001b[1;32m 22\u001b[0m grad_b_exp \u001b[39m=\u001b[39m np\u001b[39m.\u001b[39marray([\u001b[39m-\u001b[39m\u001b[39m0.025\u001b[39m])\n\u001b[0;32m---> 26\u001b[0m np\u001b[39m.\u001b[39mtesting\u001b[39m.\u001b[39massert_array_almost_equal(grad_w_pred,grad_w_exp,decimal\u001b[39m=\u001b[39m\u001b[39m8\u001b[39m)\n\u001b[1;32m 27\u001b[0m np\u001b[39m.\u001b[39mtesting\u001b[39m.\u001b[39massert_array_almost_equal(grad_b_pred,grad_b_exp,decimal\u001b[39m=\u001b[39m\u001b[39m8\u001b[39m)\n",
+ "Cell \u001b[0;32mIn [30], line 26\u001b[0m\n\u001b[1;32m 18\u001b[0m grad_w_exp \u001b[39m=\u001b[39m np\u001b[39m.\u001b[39marray([[\u001b[39m-\u001b[39m\u001b[39m0.01111111\u001b[39m],\n\u001b[1;32m 19\u001b[0m [\u001b[39m-\u001b[39m\u001b[39m0.00555556\u001b[39m],\n\u001b[1;32m 20\u001b[0m [ \u001b[39m0.02222222\u001b[39m]])\n\u001b[1;32m 22\u001b[0m grad_b_exp \u001b[39m=\u001b[39m np\u001b[39m.\u001b[39marray([\u001b[39m-\u001b[39m\u001b[39m0.025\u001b[39m])\n\u001b[0;32m---> 26\u001b[0m np\u001b[39m.\u001b[39mtesting\u001b[39m.\u001b[39massert_array_almost_equal(grad_w_pred,grad_w_exp,decimal\u001b[39m=\u001b[39m\u001b[39m8\u001b[39m)\n\u001b[1;32m 27\u001b[0m np\u001b[39m.\u001b[39mtesting\u001b[39m.\u001b[39massert_array_almost_equal(grad_b_pred,grad_b_exp,decimal\u001b[39m=\u001b[39m\u001b[39m8\u001b[39m)\n",
" \u001b[0;31m[... skipping hidden 1 frame]\u001b[0m\n",
"File \u001b[0;32m~/.local/lib/python3.10/site-packages/numpy/testing/_private/utils.py:844\u001b[0m, in \u001b[0;36massert_array_compare\u001b[0;34m(comparison, x, y, err_msg, verbose, header, precision, equal_nan, equal_inf)\u001b[0m\n\u001b[1;32m 840\u001b[0m err_msg \u001b[39m+\u001b[39m\u001b[39m=\u001b[39m \u001b[39m'\u001b[39m\u001b[39m\\n\u001b[39;00m\u001b[39m'\u001b[39m \u001b[39m+\u001b[39m \u001b[39m'\u001b[39m\u001b[39m\\n\u001b[39;00m\u001b[39m'\u001b[39m\u001b[39m.\u001b[39mjoin(remarks)\n\u001b[1;32m 841\u001b[0m msg \u001b[39m=\u001b[39m build_err_msg([ox, oy], err_msg,\n\u001b[1;32m 842\u001b[0m verbose\u001b[39m=\u001b[39mverbose, header\u001b[39m=\u001b[39mheader,\n\u001b[1;32m 843\u001b[0m names\u001b[39m=\u001b[39m(\u001b[39m'\u001b[39m\u001b[39mx\u001b[39m\u001b[39m'\u001b[39m, \u001b[39m'\u001b[39m\u001b[39my\u001b[39m\u001b[39m'\u001b[39m), precision\u001b[39m=\u001b[39mprecision)\n\u001b[0;32m--> 844\u001b[0m \u001b[39mraise\u001b[39;00m \u001b[39mAssertionError\u001b[39;00m(msg)\n\u001b[1;32m 845\u001b[0m \u001b[39mexcept\u001b[39;00m \u001b[39mValueError\u001b[39;00m:\n\u001b[1;32m 846\u001b[0m \u001b[39mimport\u001b[39;00m \u001b[39mtraceback\u001b[39;00m\n",
- "\u001b[0;31mAssertionError\u001b[0m: \nArrays are not almost equal to 8 decimals\n\nMismatched elements: 3 / 3 (100%)\nMax absolute difference: 0.02550776\nMax relative difference: 3.61328441\n x: array([[0.01439665],\n [0.01451826],\n [0.01512632]])\n y: array([[-0.01111111],\n [-0.00555556],\n [ 0.02222222]])"
+ "\u001b[0;31mAssertionError\u001b[0m: \nArrays are not almost equal to 8 decimals\n\nMismatched elements: 3 / 3 (100%)\nMax absolute difference: 0.0197491\nMax relative difference: 2.56797064\n x: array([[0.00863799],\n [0.00871095],\n [0.00907579]])\n y: array([[-0.01111111],\n [-0.00555556],\n [ 0.02222222]])"
]
}
],
@@ -806,7 +2213,7 @@
},
{
"cell_type": "code",
- "execution_count": 47,
+ "execution_count": 31,
"id": "hungry-electron",
"metadata": {},
"outputs": [
@@ -814,21 +2221,24 @@
"name": "stdout",
"output_type": "stream",
"text": [
- "[[0.03455195 0.03484382 0.03630316]]\n",
+ "(1, 3)\n",
+ "[[0.03455195]\n",
+ " [0.03484382]\n",
+ " [0.03630316]]\n",
"-0.1\n"
]
},
{
"ename": "AssertionError",
- "evalue": "\nArrays are not almost equal to 8 decimals\n\n(shapes (1, 3), (3, 1) mismatch)\n x: array([[0.03455195, 0.03484382, 0.03630316]])\n y: array([[-0.04444444],\n [-0.02222222],\n [ 0.08888889]])",
+ "evalue": "\nArrays are not almost equal to 8 decimals\n\nMismatched elements: 3 / 3 (100%)\nMax absolute difference: 0.07899639\nMax relative difference: 2.56797205\n x: array([[0.03455195],\n [0.03484382],\n [0.03630316]])\n y: array([[-0.04444444],\n [-0.02222222],\n [ 0.08888889]])",
"output_type": "error",
"traceback": [
"\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
"\u001b[0;31mAssertionError\u001b[0m Traceback (most recent call last)",
- "Cell \u001b[0;32mIn [47], line 24\u001b[0m\n\u001b[1;32m 18\u001b[0m grad_w_exp \u001b[39m=\u001b[39m np\u001b[39m.\u001b[39marray([[\u001b[39m-\u001b[39m\u001b[39m0.04444444\u001b[39m],\n\u001b[1;32m 19\u001b[0m [\u001b[39m-\u001b[39m\u001b[39m0.02222222\u001b[39m],\n\u001b[1;32m 20\u001b[0m [ \u001b[39m0.08888889\u001b[39m]])\n\u001b[1;32m 22\u001b[0m grad_b_exp \u001b[39m=\u001b[39m np\u001b[39m.\u001b[39marray([\u001b[39m-\u001b[39m\u001b[39m0.1\u001b[39m])\n\u001b[0;32m---> 24\u001b[0m np\u001b[39m.\u001b[39mtesting\u001b[39m.\u001b[39massert_array_almost_equal(grad_w_pred,grad_w_exp,decimal\u001b[39m=\u001b[39m\u001b[39m8\u001b[39m)\n\u001b[1;32m 25\u001b[0m np\u001b[39m.\u001b[39mtesting\u001b[39m.\u001b[39massert_array_almost_equal(grad_b_pred,grad_b_exp,decimal\u001b[39m=\u001b[39m\u001b[39m8\u001b[39m)\n",
+ "Cell \u001b[0;32mIn [31], line 24\u001b[0m\n\u001b[1;32m 18\u001b[0m grad_w_exp \u001b[39m=\u001b[39m np\u001b[39m.\u001b[39marray([[\u001b[39m-\u001b[39m\u001b[39m0.04444444\u001b[39m],\n\u001b[1;32m 19\u001b[0m [\u001b[39m-\u001b[39m\u001b[39m0.02222222\u001b[39m],\n\u001b[1;32m 20\u001b[0m [ \u001b[39m0.08888889\u001b[39m]])\n\u001b[1;32m 22\u001b[0m grad_b_exp \u001b[39m=\u001b[39m np\u001b[39m.\u001b[39marray([\u001b[39m-\u001b[39m\u001b[39m0.1\u001b[39m])\n\u001b[0;32m---> 24\u001b[0m np\u001b[39m.\u001b[39mtesting\u001b[39m.\u001b[39massert_array_almost_equal(grad_w_pred,grad_w_exp,decimal\u001b[39m=\u001b[39m\u001b[39m8\u001b[39m)\n\u001b[1;32m 25\u001b[0m np\u001b[39m.\u001b[39mtesting\u001b[39m.\u001b[39massert_array_almost_equal(grad_b_pred,grad_b_exp,decimal\u001b[39m=\u001b[39m\u001b[39m8\u001b[39m)\n",
" \u001b[0;31m[... skipping hidden 1 frame]\u001b[0m\n",
- "File \u001b[0;32m~/.local/lib/python3.10/site-packages/numpy/testing/_private/utils.py:763\u001b[0m, in \u001b[0;36massert_array_compare\u001b[0;34m(comparison, x, y, err_msg, verbose, header, precision, equal_nan, equal_inf)\u001b[0m\n\u001b[1;32m 757\u001b[0m \u001b[39mif\u001b[39;00m \u001b[39mnot\u001b[39;00m cond:\n\u001b[1;32m 758\u001b[0m msg \u001b[39m=\u001b[39m build_err_msg([x, y],\n\u001b[1;32m 759\u001b[0m err_msg\n\u001b[1;32m 760\u001b[0m \u001b[39m+\u001b[39m \u001b[39mf\u001b[39m\u001b[39m'\u001b[39m\u001b[39m\\n\u001b[39;00m\u001b[39m(shapes \u001b[39m\u001b[39m{\u001b[39;00mx\u001b[39m.\u001b[39mshape\u001b[39m}\u001b[39;00m\u001b[39m, \u001b[39m\u001b[39m{\u001b[39;00my\u001b[39m.\u001b[39mshape\u001b[39m}\u001b[39;00m\u001b[39m mismatch)\u001b[39m\u001b[39m'\u001b[39m,\n\u001b[1;32m 761\u001b[0m verbose\u001b[39m=\u001b[39mverbose, header\u001b[39m=\u001b[39mheader,\n\u001b[1;32m 762\u001b[0m names\u001b[39m=\u001b[39m(\u001b[39m'\u001b[39m\u001b[39mx\u001b[39m\u001b[39m'\u001b[39m, \u001b[39m'\u001b[39m\u001b[39my\u001b[39m\u001b[39m'\u001b[39m), precision\u001b[39m=\u001b[39mprecision)\n\u001b[0;32m--> 763\u001b[0m \u001b[39mraise\u001b[39;00m \u001b[39mAssertionError\u001b[39;00m(msg)\n\u001b[1;32m 765\u001b[0m flagged \u001b[39m=\u001b[39m bool_(\u001b[39mFalse\u001b[39;00m)\n\u001b[1;32m 766\u001b[0m \u001b[39mif\u001b[39;00m isnumber(x) \u001b[39mand\u001b[39;00m isnumber(y):\n",
- "\u001b[0;31mAssertionError\u001b[0m: \nArrays are not almost equal to 8 decimals\n\n(shapes (1, 3), (3, 1) mismatch)\n x: array([[0.03455195, 0.03484382, 0.03630316]])\n y: array([[-0.04444444],\n [-0.02222222],\n [ 0.08888889]])"
+ "File \u001b[0;32m~/.local/lib/python3.10/site-packages/numpy/testing/_private/utils.py:844\u001b[0m, in \u001b[0;36massert_array_compare\u001b[0;34m(comparison, x, y, err_msg, verbose, header, precision, equal_nan, equal_inf)\u001b[0m\n\u001b[1;32m 840\u001b[0m err_msg \u001b[39m+\u001b[39m\u001b[39m=\u001b[39m \u001b[39m'\u001b[39m\u001b[39m\\n\u001b[39;00m\u001b[39m'\u001b[39m \u001b[39m+\u001b[39m \u001b[39m'\u001b[39m\u001b[39m\\n\u001b[39;00m\u001b[39m'\u001b[39m\u001b[39m.\u001b[39mjoin(remarks)\n\u001b[1;32m 841\u001b[0m msg \u001b[39m=\u001b[39m build_err_msg([ox, oy], err_msg,\n\u001b[1;32m 842\u001b[0m verbose\u001b[39m=\u001b[39mverbose, header\u001b[39m=\u001b[39mheader,\n\u001b[1;32m 843\u001b[0m names\u001b[39m=\u001b[39m(\u001b[39m'\u001b[39m\u001b[39mx\u001b[39m\u001b[39m'\u001b[39m, \u001b[39m'\u001b[39m\u001b[39my\u001b[39m\u001b[39m'\u001b[39m), precision\u001b[39m=\u001b[39mprecision)\n\u001b[0;32m--> 844\u001b[0m \u001b[39mraise\u001b[39;00m \u001b[39mAssertionError\u001b[39;00m(msg)\n\u001b[1;32m 845\u001b[0m \u001b[39mexcept\u001b[39;00m \u001b[39mValueError\u001b[39;00m:\n\u001b[1;32m 846\u001b[0m \u001b[39mimport\u001b[39;00m \u001b[39mtraceback\u001b[39;00m\n",
+ "\u001b[0;31mAssertionError\u001b[0m: \nArrays are not almost equal to 8 decimals\n\nMismatched elements: 3 / 3 (100%)\nMax absolute difference: 0.07899639\nMax relative difference: 2.56797205\n x: array([[0.03455195],\n [0.03484382],\n [0.03630316]])\n y: array([[-0.04444444],\n [-0.02222222],\n [ 0.08888889]])"
]
}
],
@@ -870,7 +2280,7 @@
},
{
"cell_type": "code",
- "execution_count": 44,
+ "execution_count": null,
"id": "identical-worthy",
"metadata": {},
"outputs": [],
@@ -905,7 +2315,7 @@
},
{
"cell_type": "code",
- "execution_count": 45,
+ "execution_count": null,
"id": "chemical-nothing",
"metadata": {},
"outputs": [],
@@ -941,7 +2351,7 @@
},
{
"cell_type": "code",
- "execution_count": 14,
+ "execution_count": null,
"id": "90f77759",
"metadata": {},
"outputs": [
@@ -991,7 +2401,7 @@
},
{
"cell_type": "code",
- "execution_count": 15,
+ "execution_count": null,
"id": "5e873ea8",
"metadata": {},
"outputs": [
@@ -1017,7 +2427,7 @@
},
{
"cell_type": "code",
- "execution_count": 25,
+ "execution_count": null,
"id": "85bb33b8",
"metadata": {},
"outputs": [
diff --git a/serie2/equation.png b/serie2/equation.png
new file mode 100644
index 0000000000000000000000000000000000000000..dddd3637eba301525247a23194f4259389d6f16f
Binary files /dev/null and b/serie2/equation.png differ
diff --git a/serie2/output001.png b/serie2/output001.png
index cf1c9d057702135fd2803c9af74fcb236f61db30..b062d75cfb7724d93a2596180f9bdb7e00272a5f 100644
Binary files a/serie2/output001.png and b/serie2/output001.png differ
diff --git a/serie2/output005.png b/serie2/output005.png
index bfdb3d651dd7f905b6de2e925d68b730e5562d0c..1028db301ba16f99e4ab50f6a0e28d1290a5749f 100644
Binary files a/serie2/output005.png and b/serie2/output005.png differ
diff --git a/serie2/output01.png b/serie2/output01.png
index 01c24623cd5579bc756ba06f52a0072b1da9eaa4..9332325a49b360aa09752dac3d92ac4a2dbde963 100644
Binary files a/serie2/output01.png and b/serie2/output01.png differ
diff --git a/serie2/output10.png b/serie2/output10.png
index bc678b687d444b7fc6b3491707c5e272df78cb4e..0f91cee1ef2f40192144a0e37645487035ba4438 100644
Binary files a/serie2/output10.png and b/serie2/output10.png differ
diff --git a/serie2/output5.png b/serie2/output5.png
index 4174ccbc724a95066744554e0fe588d19671ae47..a94525d0b6cbc6c79a4bab61dfc8cc0f6e03adfa 100644
Binary files a/serie2/output5.png and b/serie2/output5.png differ
diff --git a/serie2/outputCost.png b/serie2/outputCost.png
index 8ed49c332a2bb2a52f8fa2c6f1f8b9199b64af6d..dcddddfa4631bd43c83aab20609eeccebde8d1bb 100644
Binary files a/serie2/outputCost.png and b/serie2/outputCost.png differ
diff --git a/serie2/rapport.md b/serie2/rapport.md
index 8c74f07ee69530bdc70bdab2452e3648b226299a..2fd2bc2f6bb6ddc76e3052ecd3765f1aba7d872b 100644
--- a/serie2/rapport.md
+++ b/serie2/rapport.md
@@ -6,13 +6,7 @@ As for part 2 of this work, we have largely solved everything on paper. This wor
### c)
The calculation of the asymptotes are for -inf and +inf :
-$$
-\lim_{z \to -\infty} -ln(\frac{1}{1+e^{+z}})= \lim_{z \to -\infty} -ln(->0) = - (-\infty) = +\infty
-$$
-
-$$
-\lim_{z \to +\infty} -ln(\frac{1}{1+e^{+z}})= \lim_{z \to +\infty} -ln(->1) = - (0) = 0
-$$
+
The graph of this function is :
@@ -78,9 +72,9 @@ We have tested our code with the unit test but we have some few failures. The pr
```python
# MSE grad cost
# calculated weights
-[[0.01439665]
- [0.01451826]
- [0.01512632]]
+[[0.00863799]
+ [0.00871095]
+ [0.00907579]]
-0.025
# expected weights
@@ -92,7 +86,9 @@ We have tested our code with the unit test but we have some few failures. The pr
# CE grad cost
# calculated weights
-[[0.03455195 0.03484382 0.03630316]]
+[[0.03455195]
+ [0.03484382]
+ [0.03630316]]
-0.1
@@ -111,58 +107,64 @@ We see the error is slowly decreasing :
#### Learning rate = 0.5
```python
# train cost
-[0.125 0.01536504 0.014986 0.01457691 0.01413949 0.01378105
- 0.01344739 0.01318818 0.01299845 0.01282704]
+[0.125 0.01167834 0.0094656 0.00821998 0.00745537 0.00693789
+ 0.00655898 0.00626461 0.00602531 0.00582378]
# test cost
-[0.125 0.01556244 0.01537012 0.01520221 0.01504856 0.01491241
- 0.01473657 0.01452109 0.01429011 0.01403994]
+[0.125 0.01221453 0.00991035 0.00868214 0.00793829 0.0074321
+ 0.00705811 0.00676609 0.00652882 0.00633006]
```
#### Learning rate = 0.01
```python
# train cost
-[0.125 0.10246172 0.08545479 0.07284244 0.06341362 0.05622866
- 0.05063015 0.04617138 0.04254854 0.03955205]
+[0.125 0.11008301 0.09752466 0.087086 0.07844205 0.07126836
+ 0.0652801 0.06024259 0.05596834 0.05230978]
# test cost
-[0.125 0.1027335 0.0859324 0.07347156 0.06415158 0.05704332
- 0.05149794 0.04707527 0.04347625 0.04049475]
+[0.125 0.11026367 0.09785747 0.0875453 0.07900537 0.07191644
+ 0.06599674 0.0610143 0.05678406 0.05316044]
```
#### Learning rate = 0.05
```python
# train cost
-[0.125 0.01241361 0.01054175 0.00924545 0.00834697 0.00769963
- 0.00721371 0.00683615 0.00653386 0.00628502]
- # test cost
-[0.125 0.01287107 0.01101644 0.00979839 0.0089649 0.00835911
- 0.00789375 0.00751864 0.00720543 0.00693776]
+[0.125 0.06407043 0.04362913 0.03443157 0.02913729 0.02565044
+ 0.02315538 0.02126727 0.0197798 0.01857194]
+# test cost
+[0.125 0.06478542 0.0445854 0.03541178 0.03010005 0.02658546
+ 0.02406153 0.02214626 0.02063417 0.01940429]
```
#### Learning rate = 0.1
```python
-[0.125 0.01374854 0.01248129 0.01146193 0.01065814 0.00999054
- 0.00942033 0.00892796 0.00850293 0.00813431]
-[0.125 0.01417265 0.01304014 0.01208604 0.01133821 0.01073285
- 0.01021371 0.00976315 0.00938548 0.00907634]
+# train cost
+[0.125 0.03368306 0.02485264 0.02067068 0.01812153 0.01636634
+ 0.01506612 0.01405447 0.01323907 0.01256409]
+ # test cost
+[0.125 0.03466544 0.02581943 0.02154076 0.01893808 0.01714329
+ 0.01581322 0.01477861 0.01394524 0.01325605]
```
#### Learning rate = 5
```python
-[0.125 0.01580711 0.01578208 0.01575926 0.0157372 0.01571619
- 0.01569154 0.01566771 0.01565214 0.01563725]
-[0.125 0.01580335 0.01578762 0.0157682 0.01574174 0.01570748
- 0.01567523 0.01565887 0.0156537 0.01565004]
+# train cost
+[0.125 0.01512893 0.01454848 0.01392892 0.01340643 0.01301482
+ 0.012721 0.0124677 0.01224422 0.01204864]
+# test cost
+[0.125 0.01541932 0.0151363 0.01486291 0.01454016 0.01417231
+ 0.01380597 0.01345951 0.01316053 0.01291394]
```
#### Learning rate = 10
```python
-[0.125 0.01582558 0.01581444 0.01580841 0.01579636 0.01577758
- 0.01577106 0.01576299 0.01575024 0.01574215]
-[0.125 0.01581955 0.01581834 0.01581776 0.01581751 0.01581771
- 0.01581701 0.01581501 0.01581027 0.01580263]
+# train cost
+[0.125 0.01548169 0.01518021 0.0148827 0.01454819 0.01419855
+ 0.01394402 0.01367097 0.01340131 0.01322291]
+# test cost
+[0.125 0.01562651 0.0154658 0.01533677 0.01519471 0.01508492
+ 0.01501356 0.01491323 0.01478684 0.01463886]
```
### Summarize best learning rate
@@ -170,7 +172,7 @@ We see that the learning rate of 0.05 is the best because it is the one that has
### Analyse the dependency of the final error rate on the number of epochs
-We tried many value of epochs and we saw that after ~90 epoches for the learning rate 0.05, the error stop decreasing for the test set. For 150 epoches we have :
+We tried many value of epochs and we saw that after ~450 epoches for the learning rate 0.05, the error stop decreasing for the test set. For 150 epoches we have :
And for 1000 epoches we have :
@@ -181,9 +183,9 @@ We also tried it for the learning rate 10 (to see the effect of a big learning r
We can say that we need a higher number of epoches to have a good results with small learning rate but we need a lower number of epoches with a big learning rate. The goal is to have the maximum learning rate possible to reach the minimum error rate in the minimum number of epoches but if our learning rate is too big we will oscillate around the minimum error rate and we will never reach it.
-### Plot a histogram of the weights finally obtained from learing. A strong peak at zero remains. Why ?
+### Plot a histogram of the weights finally obtained from learning. A strong peak at zero remains. Why ?
We see that if we plot an histogramm of the weights finally obtained we have :
-There is a strong peak at 0 because
\ No newline at end of file
+There is a strong peak at 0 because we have a lot of undefined pixels on the MNIST data (only a few per image to draw the number) during image recognition. So we don't need to change the weight of the undefined pixel, because it is already at 0.
\ No newline at end of file
diff --git a/serie2/rapport.pdf b/serie2/rapport.pdf
new file mode 100644
index 0000000000000000000000000000000000000000..7dd01f9f2347c65ae5c065bb5b047d399be37d8b
Binary files /dev/null and b/serie2/rapport.pdf differ