noamkay commited on
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Upload folder using huggingface_hub

Browse files
.config/.last_opt_in_prompt.yaml ADDED
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+ last_prompt_time: 1702995615.4104285
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+ {"last_update_check_time": 1702995624.560697, "last_update_check_revision": 20231208151900, "notifications": [], "last_nag_times": {}}
.config/active_config ADDED
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.config/config_sentinel ADDED
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.config/configurations/config_default ADDED
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+ [component_manager]
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+ disable_update_check = true
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+ gce_metadata_read_timeout_sec = 0
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+ 2023-12-19 14:19:39,755 DEBUG root Loaded Command Group: ['gcloud', 'components']
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+ 2023-12-19 14:19:39,759 DEBUG root Loaded Command Group: ['gcloud', 'components', 'update']
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+ 2023-12-19 14:19:39,762 DEBUG root Running [gcloud.components.update] with arguments: [--allow-no-backup: "True", --compile-python: "True", --quiet: "True", COMPONENT-IDS:7: "['core', 'gcloud-deps', 'bq', 'gcloud', 'gcloud-crc32c', 'gsutil', 'anthoscli']"]
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+ 2023-12-19 14:19:39,763 INFO ___FILE_ONLY___ Beginning update. This process may take several minutes.
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+
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+ 2023-12-19 14:19:51,917 DEBUG root Chosen display Format:table[box,title="These components will be updated."](details.display_name:label=Name:align=left,version.version_string:label=Version:align=right,data.size.size(zero="",min=1048576):label=Size:align=right)
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+ 2023-12-19 14:19:51,918 DEBUG root Chosen display Format:table[box,title="These components will be installed."](details.display_name:label=Name:align=left,version.version_string:label=Version:align=right,data.size.size(zero="",min=1048576):label=Size:align=right)
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+ 2023-12-19 14:19:51,939 DEBUG urllib3.connectionpool Starting new HTTPS connection (1): dl.google.com:443
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+ 2023-12-19 14:19:52,096 INFO ___FILE_ONLY___ For the latest full release notes, please visit:
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+ https://cloud.google.com/sdk/release_notes
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+ 2023-12-19 14:19:52,099 INFO ___FILE_ONLY___ ╠═ Creating update staging area ═╣
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+ 2023-12-19 14:19:55,524 INFO ___FILE_ONLY___ ╠═ Installing: BigQuery Command Line Tool (Platform Spec... ═╣
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+ 2023-12-19 14:19:55,529 DEBUG urllib3.connectionpool Starting new HTTPS connection (1): dl.google.com:443
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+ 2023-12-19 14:19:55,611 DEBUG urllib3.connectionpool https://dl.google.com:443 "GET /dl/cloudsdk/channels/rapid/components/google-cloud-sdk-bq-nix-20231025210228.tar.gz HTTP/1.1" 200 2019
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+ 2023-12-19 14:19:55,611 INFO ___FILE_ONLY___ ══════════════════════════════
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+ 2023-12-19 14:19:55,657 INFO ___FILE_ONLY___ ╠═ Installing: Bundled Python 3.11 ═╣
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+ 2023-12-19 14:19:55,661 DEBUG urllib3.connectionpool Starting new HTTPS connection (1): dl.google.com:443
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+ 2023-12-19 14:19:55,747 DEBUG urllib3.connectionpool https://dl.google.com:443 "GET /dl/cloudsdk/channels/rapid/components/google-cloud-sdk-bundled-python3-unix-linux-x86_64-20231110155547.tar.gz HTTP/1.1" 200 77282043
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+ 2023-12-19 14:20:00,372 INFO ___FILE_ONLY___ ╠═ Installing: Bundled Python 3.11 ═╣
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+ 2023-12-19 14:20:00,380 INFO ___FILE_ONLY___ ╠═ Installing: Cloud Storage Command Line Tool ═╣
366
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368
+ 2023-12-19 14:20:00,384 DEBUG urllib3.connectionpool Starting new HTTPS connection (1): dl.google.com:443
369
+ 2023-12-19 14:20:00,471 DEBUG urllib3.connectionpool https://dl.google.com:443 "GET /dl/cloudsdk/channels/rapid/components/google-cloud-sdk-gsutil-20231025210228.tar.gz HTTP/1.1" 200 11833901
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427
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428
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429
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431
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432
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433
+
434
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435
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436
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437
+ 2023-12-19 14:20:02,222 DEBUG urllib3.connectionpool Starting new HTTPS connection (1): dl.google.com:443
438
+ 2023-12-19 14:20:02,308 DEBUG urllib3.connectionpool https://dl.google.com:443 "GET /dl/cloudsdk/channels/rapid/components/google-cloud-sdk-gsutil-nix-20231025210228.tar.gz HTTP/1.1" 200 2035
439
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440
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441
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443
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444
+
445
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446
+
447
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448
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449
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450
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451
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452
+
453
+ 2023-12-19 14:20:02,329 INFO ___FILE_ONLY___ ╠═ Installing: Google Cloud CLI Core Libraries (Platform... ═╣
454
+
455
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456
+ 2023-12-19 14:20:02,333 DEBUG urllib3.connectionpool Starting new HTTPS connection (1): dl.google.com:443
457
+ 2023-12-19 14:20:02,427 DEBUG urllib3.connectionpool https://dl.google.com:443 "GET /dl/cloudsdk/channels/rapid/components/google-cloud-sdk-core-nix-20231025210228.tar.gz HTTP/1.1" 200 2402
458
+ 2023-12-19 14:20:02,428 INFO ___FILE_ONLY___ ══════════════════════════════
459
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460
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461
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463
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464
+
465
+ 2023-12-19 14:20:02,440 INFO ___FILE_ONLY___ ╠═ Installing: Google Cloud CRC32C Hash Tool ═╣
466
+
467
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468
+ 2023-12-19 14:20:02,444 DEBUG urllib3.connectionpool Starting new HTTPS connection (1): dl.google.com:443
469
+ 2023-12-19 14:20:02,531 DEBUG urllib3.connectionpool https://dl.google.com:443 "GET /dl/cloudsdk/channels/rapid/components/google-cloud-sdk-gcloud-crc32c-linux-x86_64-20231023224440.tar.gz HTTP/1.1" 200 1289207
470
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475
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479
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480
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481
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482
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483
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484
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485
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487
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489
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490
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491
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492
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493
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494
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495
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496
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498
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500
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501
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502
+ 2023-12-19 14:20:02,582 INFO ___FILE_ONLY___ ╝
503
+
504
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505
+
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+ 2023-12-19 14:20:02,593 INFO ___FILE_ONLY___ ╠═ Installing: Google Cloud CRC32C Hash Tool ═╣
507
+
508
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509
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510
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511
+
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513
+
514
+ 2023-12-19 14:20:02,601 INFO ___FILE_ONLY___ ╠═ Installing: anthoscli ═╣
515
+
516
+ 2023-12-19 14:20:02,601 INFO ___FILE_ONLY___ ╚
517
+ 2023-12-19 14:20:02,606 DEBUG urllib3.connectionpool Starting new HTTPS connection (1): dl.google.com:443
518
+ 2023-12-19 14:20:02,694 DEBUG urllib3.connectionpool https://dl.google.com:443 "GET /dl/cloudsdk/channels/rapid/components/google-cloud-sdk-anthoscli-linux-x86_64-20231201141418.tar.gz HTTP/1.1" 200 72274029
519
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520
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522
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523
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524
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525
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526
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527
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528
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529
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530
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532
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535
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536
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537
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538
+ 2023-12-19 14:20:03,036 INFO ___FILE_ONLY___ ═
539
+ 2023-12-19 14:20:03,039 INFO ___FILE_ONLY___ ═
540
+ 2023-12-19 14:20:03,043 INFO ___FILE_ONLY___ ═
541
+ 2023-12-19 14:20:03,046 INFO ___FILE_ONLY___ ═
542
+ 2023-12-19 14:20:03,049 INFO ___FILE_ONLY___ ═
543
+ 2023-12-19 14:20:03,053 INFO ___FILE_ONLY___ ═
544
+ 2023-12-19 14:20:03,056 INFO ___FILE_ONLY___ ═
545
+ 2023-12-19 14:20:03,059 INFO ___FILE_ONLY___ ═
546
+ 2023-12-19 14:20:03,063 INFO ___FILE_ONLY___ ═
547
+ 2023-12-19 14:20:03,066 INFO ___FILE_ONLY___ ═
548
+ 2023-12-19 14:20:03,069 INFO ___FILE_ONLY___ ═
549
+ 2023-12-19 14:20:05,451 INFO ___FILE_ONLY___ ══════════
550
+ 2023-12-19 14:20:05,457 INFO ___FILE_ONLY___ ═════════
551
+ 2023-12-19 14:20:05,487 INFO ___FILE_ONLY___ ═══════════
552
+ 2023-12-19 14:20:05,487 INFO ___FILE_ONLY___ ╝
553
+
554
+ 2023-12-19 14:20:05,514 INFO ___FILE_ONLY___ ╔═════════════════════════════════��══════════════════════════╗
555
+
556
+ 2023-12-19 14:20:05,514 INFO ___FILE_ONLY___ ╠═ Installing: anthoscli ═╣
557
+
558
+ 2023-12-19 14:20:05,514 INFO ___FILE_ONLY___ ╚
559
+ 2023-12-19 14:20:05,520 INFO ___FILE_ONLY___ ════════════════════════════════════════════════════════════
560
+ 2023-12-19 14:20:05,520 INFO ___FILE_ONLY___ ╝
561
+
562
+ 2023-12-19 14:20:05,522 INFO ___FILE_ONLY___ ╔════════════════════════════════════════════════════════════╗
563
+
564
+ 2023-12-19 14:20:05,522 INFO ___FILE_ONLY___ ╠═ Installing: gcloud cli dependencies ═╣
565
+
566
+ 2023-12-19 14:20:05,522 INFO ___FILE_ONLY___ ╚
567
+ 2023-12-19 14:20:05,527 DEBUG urllib3.connectionpool Starting new HTTPS connection (1): dl.google.com:443
568
+ 2023-12-19 14:20:05,619 DEBUG urllib3.connectionpool https://dl.google.com:443 "GET /dl/cloudsdk/channels/rapid/components/google-cloud-sdk-gcloud-deps-linux-x86_64-20210416153011.tar.gz HTTP/1.1" 200 104
569
+ 2023-12-19 14:20:05,620 INFO ___FILE_ONLY___ ══════════════════════════════
570
+ 2023-12-19 14:20:05,620 INFO ___FILE_ONLY___ ══════════════════════════════
571
+ 2023-12-19 14:20:05,620 INFO ___FILE_ONLY___ ╝
572
+
573
+ 2023-12-19 14:20:05,631 INFO ___FILE_ONLY___ ╔════════════════════════════════════════════════════════════╗
574
+
575
+ 2023-12-19 14:20:05,631 INFO ___FILE_ONLY___ ╠═ Creating backup and activating new installation ═╣
576
+
577
+ 2023-12-19 14:20:05,631 INFO ___FILE_ONLY___ ╚
578
+ 2023-12-19 14:20:05,631 DEBUG root Attempting to move directory [/tools/google-cloud-sdk] to [/tools/google-cloud-sdk.staging/.install/.backup]
579
+ 2023-12-19 14:20:05,631 INFO ___FILE_ONLY___ ══════════════════════════════
580
+ 2023-12-19 14:20:05,631 DEBUG root Attempting to move directory [/tools/google-cloud-sdk.staging] to [/tools/google-cloud-sdk]
581
+ 2023-12-19 14:20:05,631 INFO ___FILE_ONLY___ ══════════════════════════════
582
+ 2023-12-19 14:20:05,631 INFO ___FILE_ONLY___ ╝
583
+
584
+ 2023-12-19 14:20:05,636 DEBUG root Updating notification cache...
585
+ 2023-12-19 14:20:05,637 INFO ___FILE_ONLY___
586
+
587
+ 2023-12-19 14:20:05,639 INFO ___FILE_ONLY___ Performing post processing steps...
588
+ 2023-12-19 14:20:05,639 DEBUG root Executing command: ['python3', '-S', '/tools/google-cloud-sdk/lib/gcloud.py', 'components', 'post-process']
589
+ 2023-12-19 14:20:15,322 DEBUG ___FILE_ONLY___
590
+ 2023-12-19 14:20:15,322 DEBUG ___FILE_ONLY___
591
+ 2023-12-19 14:20:15,404 INFO ___FILE_ONLY___
592
+ Update done!
593
+
594
+
595
+ 2023-12-19 14:20:15,409 DEBUG root Chosen display Format:none
596
+ 2023-12-19 14:20:15,409 INFO root Display format: "none"
.config/logs/2023.12.19/14.20.06.327238.log ADDED
@@ -0,0 +1,5 @@
 
 
 
 
 
 
1
+ 2023-12-19 14:20:06,328 DEBUG root Loaded Command Group: ['gcloud', 'components']
2
+ 2023-12-19 14:20:06,330 DEBUG root Loaded Command Group: ['gcloud', 'components', 'post_process']
3
+ 2023-12-19 14:20:06,333 DEBUG root Running [gcloud.components.post-process] with arguments: []
4
+ 2023-12-19 14:20:15,176 DEBUG root Chosen display Format:none
5
+ 2023-12-19 14:20:15,177 INFO root Display format: "none"
.config/logs/2023.12.19/14.20.16.265569.log ADDED
@@ -0,0 +1,169 @@
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
1
+ 2023-12-19 14:20:16,266 DEBUG root Loaded Command Group: ['gcloud', 'components']
2
+ 2023-12-19 14:20:16,269 DEBUG root Loaded Command Group: ['gcloud', 'components', 'update']
3
+ 2023-12-19 14:20:16,272 DEBUG root Running [gcloud.components.update] with arguments: [--quiet: "True", COMPONENT-IDS:8: "['gcloud', 'core', 'bq', 'gsutil', 'compute', 'preview', 'alpha', 'beta']"]
4
+ 2023-12-19 14:20:16,273 INFO ___FILE_ONLY___ Beginning update. This process may take several minutes.
5
+
6
+ 2023-12-19 14:20:16,279 DEBUG urllib3.connectionpool Starting new HTTPS connection (1): dl.google.com:443
7
+ 2023-12-19 14:20:16,368 DEBUG urllib3.connectionpool https://dl.google.com:443 "GET /dl/cloudsdk/channels/rapid/components-2.json HTTP/1.1" 200 214441
8
+ 2023-12-19 14:20:16,391 WARNING root Component [compute] no longer exists.
9
+ 2023-12-19 14:20:16,391 WARNING root Component [preview] no longer exists.
10
+ 2023-12-19 14:20:16,392 INFO ___FILE_ONLY___
11
+
12
+ 2023-12-19 14:20:16,393 INFO ___FILE_ONLY___
13
+ Your current Google Cloud CLI version is: 457.0.0
14
+
15
+ 2023-12-19 14:20:16,393 INFO ___FILE_ONLY___ Installing components from version: 457.0.0
16
+
17
+ 2023-12-19 14:20:16,393 INFO ___FILE_ONLY___
18
+
19
+ 2023-12-19 14:20:16,393 DEBUG root Chosen display Format:table[box,title="These components will be removed."](details.display_name:label=Name:align=left,version.version_string:label=Version:align=right,data.size.size(zero="",min=1048576):label=Size:align=right)
20
+ 2023-12-19 14:20:16,394 DEBUG root Chosen display Format:table[box,title="These components will be updated."](details.display_name:label=Name:align=left,version.version_string:label=Version:align=right,data.size.size(zero="",min=1048576):label=Size:align=right)
21
+ 2023-12-19 14:20:16,395 DEBUG root Chosen display Format:table[box,title="These components will be installed."](details.display_name:label=Name:align=left,version.version_string:label=Version:align=right,data.size.size(zero="",min=1048576):label=Size:align=right)
22
+ 2023-12-19 14:20:16,397 INFO ___FILE_ONLY___ ┌──────────────────────────────────────────────┐
23
+ 2023-12-19 14:20:16,397 INFO ___FILE_ONLY___
24
+
25
+ 2023-12-19 14:20:16,397 INFO ___FILE_ONLY___ │ These components will be installed. │
26
+ 2023-12-19 14:20:16,397 INFO ___FILE_ONLY___
27
+
28
+ 2023-12-19 14:20:16,397 INFO ___FILE_ONLY___ ├───────────────────────┬────────────┬─────────┤
29
+ 2023-12-19 14:20:16,397 INFO ___FILE_ONLY___
30
+
31
+ 2023-12-19 14:20:16,397 INFO ___FILE_ONLY___ │ Name │ Version │ Size │
32
+ 2023-12-19 14:20:16,397 INFO ___FILE_ONLY___
33
+
34
+ 2023-12-19 14:20:16,398 INFO ___FILE_ONLY___ ├───────────────────────┼────────────┼─────────┤
35
+ 2023-12-19 14:20:16,398 INFO ___FILE_ONLY___
36
+
37
+ 2023-12-19 14:20:16,398 INFO ___FILE_ONLY___ │
38
+ 2023-12-19 14:20:16,398 INFO ___FILE_ONLY___ gcloud Alpha Commands
39
+ 2023-12-19 14:20:16,398 INFO ___FILE_ONLY___
40
+ 2023-12-19 14:20:16,398 INFO ___FILE_ONLY___ │
41
+ 2023-12-19 14:20:16,398 INFO ___FILE_ONLY___ 2023.12.08
42
+ 2023-12-19 14:20:16,398 INFO ___FILE_ONLY___
43
+ 2023-12-19 14:20:16,398 INFO ___FILE_ONLY___ │
44
+ 2023-12-19 14:20:16,398 INFO ___FILE_ONLY___ < 1 MiB
45
+ 2023-12-19 14:20:16,398 INFO ___FILE_ONLY___
46
+ 2023-12-19 14:20:16,398 INFO ___FILE_ONLY___ │
47
+ 2023-12-19 14:20:16,398 INFO ___FILE_ONLY___
48
+
49
+ 2023-12-19 14:20:16,398 INFO ___FILE_ONLY___ │
50
+ 2023-12-19 14:20:16,399 INFO ___FILE_ONLY___ gcloud Beta Commands
51
+ 2023-12-19 14:20:16,399 INFO ___FILE_ONLY___
52
+ 2023-12-19 14:20:16,399 INFO ___FILE_ONLY___ │
53
+ 2023-12-19 14:20:16,399 INFO ___FILE_ONLY___ 2023.12.08
54
+ 2023-12-19 14:20:16,399 INFO ___FILE_ONLY___
55
+ 2023-12-19 14:20:16,399 INFO ___FILE_ONLY___ │
56
+ 2023-12-19 14:20:16,399 INFO ___FILE_ONLY___ < 1 MiB
57
+ 2023-12-19 14:20:16,399 INFO ___FILE_ONLY___
58
+ 2023-12-19 14:20:16,399 INFO ___FILE_ONLY___ │
59
+ 2023-12-19 14:20:16,399 INFO ___FILE_ONLY___
60
+
61
+ 2023-12-19 14:20:16,399 INFO ___FILE_ONLY___ └───────────────────────┴────────────┴─────────┘
62
+ 2023-12-19 14:20:16,399 INFO ___FILE_ONLY___
63
+
64
+ 2023-12-19 14:20:16,399 INFO ___FILE_ONLY___
65
+
66
+ 2023-12-19 14:20:16,404 DEBUG urllib3.connectionpool Starting new HTTPS connection (1): dl.google.com:443
67
+ 2023-12-19 14:20:16,492 DEBUG urllib3.connectionpool https://dl.google.com:443 "GET /dl/cloudsdk/channels/rapid/RELEASE_NOTES HTTP/1.1" 200 1126280
68
+ 2023-12-19 14:20:16,566 INFO ___FILE_ONLY___ For the latest full release notes, please visit:
69
+ https://cloud.google.com/sdk/release_notes
70
+
71
+
72
+ 2023-12-19 14:20:16,568 INFO ___FILE_ONLY___ ╔════════════════════════════════════════════════════════════╗
73
+
74
+ 2023-12-19 14:20:16,568 INFO ___FILE_ONLY___ ╠═ Creating update staging area ═╣
75
+
76
+ 2023-12-19 14:20:16,569 INFO ___FILE_ONLY___ ╚
77
+ 2023-12-19 14:20:16,569 INFO ___FILE_ONLY___ ══════
78
+ 2023-12-19 14:20:17,291 INFO ___FILE_ONLY___ ══════
79
+ 2023-12-19 14:20:17,292 INFO ___FILE_ONLY___ ══════
80
+ 2023-12-19 14:20:17,731 INFO ___FILE_ONLY___ ═
81
+ 2023-12-19 14:20:17,809 INFO ___FILE_ONLY___ ═
82
+ 2023-12-19 14:20:17,871 INFO ___FILE_ONLY___ ═
83
+ 2023-12-19 14:20:17,930 INFO ___FILE_ONLY___ ═
84
+ 2023-12-19 14:20:18,000 INFO ___FILE_ONLY___ ═
85
+ 2023-12-19 14:20:18,077 INFO ___FILE_ONLY___ ═
86
+ 2023-12-19 14:20:18,142 INFO ___FILE_ONLY___ ═
87
+ 2023-12-19 14:20:18,215 INFO ___FILE_ONLY___ ═
88
+ 2023-12-19 14:20:18,273 INFO ___FILE_ONLY___ ═
89
+ 2023-12-19 14:20:18,348 INFO ___FILE_ONLY___ ═
90
+ 2023-12-19 14:20:18,421 INFO ___FILE_ONLY___ ═
91
+ 2023-12-19 14:20:18,502 INFO ___FILE_ONLY___ ═
92
+ 2023-12-19 14:20:18,596 INFO ___FILE_ONLY___ ═
93
+ 2023-12-19 14:20:18,667 INFO ___FILE_ONLY___ ═
94
+ 2023-12-19 14:20:18,733 INFO ___FILE_ONLY___ ═
95
+ 2023-12-19 14:20:18,790 INFO ___FILE_ONLY___ ═
96
+ 2023-12-19 14:20:18,849 INFO ___FILE_ONLY___ ═
97
+ 2023-12-19 14:20:18,897 INFO ___FILE_ONLY___ ═
98
+ 2023-12-19 14:20:18,962 INFO ___FILE_ONLY___ ═
99
+ 2023-12-19 14:20:19,022 INFO ___FILE_ONLY___ ═
100
+ 2023-12-19 14:20:19,092 INFO ___FILE_ONLY___ ═
101
+ 2023-12-19 14:20:19,173 INFO ___FILE_ONLY___ ═
102
+ 2023-12-19 14:20:19,236 INFO ___FILE_ONLY___ ═
103
+ 2023-12-19 14:20:19,307 INFO ___FILE_ONLY___ ═
104
+ 2023-12-19 14:20:19,370 INFO ___FILE_ONLY___ ═
105
+ 2023-12-19 14:20:19,745 INFO ___FILE_ONLY___ ═
106
+ 2023-12-19 14:20:19,839 INFO ___FILE_ONLY___ ═
107
+ 2023-12-19 14:20:19,918 INFO ___FILE_ONLY___ ═
108
+ 2023-12-19 14:20:20,054 INFO ___FILE_ONLY___ ═
109
+ 2023-12-19 14:20:20,088 INFO ___FILE_ONLY___ ═
110
+ 2023-12-19 14:20:20,134 INFO ___FILE_ONLY___ ═
111
+ 2023-12-19 14:20:20,181 INFO ___FILE_ONLY___ ═
112
+ 2023-12-19 14:20:20,218 INFO ___FILE_ONLY___ ═
113
+ 2023-12-19 14:20:20,282 INFO ___FILE_ONLY___ ═
114
+ 2023-12-19 14:20:20,393 INFO ___FILE_ONLY___ ═
115
+ 2023-12-19 14:20:20,484 INFO ___FILE_ONLY___ ═
116
+ 2023-12-19 14:20:20,760 INFO ___FILE_ONLY___ ═
117
+ 2023-12-19 14:20:20,827 INFO ___FILE_ONLY___ ═
118
+ 2023-12-19 14:20:20,891 INFO ___FILE_ONLY___ ═
119
+ 2023-12-19 14:20:20,951 INFO ___FILE_ONLY___ ═
120
+ 2023-12-19 14:20:21,013 INFO ___FILE_ONLY___ ═
121
+ 2023-12-19 14:20:21,075 INFO ___FILE_ONLY___ ═
122
+ 2023-12-19 14:20:21,075 INFO ___FILE_ONLY___ ╝
123
+
124
+ 2023-12-19 14:20:24,353 INFO ___FILE_ONLY___ ╔════════════════════════════════════════════════════════════╗
125
+
126
+ 2023-12-19 14:20:24,353 INFO ___FILE_ONLY___ ╠═ Installing: gcloud Alpha Commands ═╣
127
+
128
+ 2023-12-19 14:20:24,353 INFO ___FILE_ONLY___ ╚
129
+ 2023-12-19 14:20:24,358 DEBUG urllib3.connectionpool Starting new HTTPS connection (1): dl.google.com:443
130
+ 2023-12-19 14:20:24,441 DEBUG urllib3.connectionpool https://dl.google.com:443 "GET /dl/cloudsdk/channels/rapid/components/google-cloud-sdk-alpha-20231208151900.tar.gz HTTP/1.1" 200 800
131
+ 2023-12-19 14:20:24,442 INFO ___FILE_ONLY___ ══════════════════════════════
132
+ 2023-12-19 14:20:24,444 INFO ___FILE_ONLY___ ══════════════════════════════
133
+ 2023-12-19 14:20:24,444 INFO ___FILE_ONLY___ ╝
134
+
135
+ 2023-12-19 14:20:24,453 INFO ___FILE_ONLY___ ╔════════════════════════════════════════════════════════════╗
136
+
137
+ 2023-12-19 14:20:24,453 INFO ___FILE_ONLY___ ╠═ Installing: gcloud Beta Commands ═╣
138
+
139
+ 2023-12-19 14:20:24,453 INFO ___FILE_ONLY___ ╚
140
+ 2023-12-19 14:20:24,457 DEBUG urllib3.connectionpool Starting new HTTPS connection (1): dl.google.com:443
141
+ 2023-12-19 14:20:24,544 DEBUG urllib3.connectionpool https://dl.google.com:443 "GET /dl/cloudsdk/channels/rapid/components/google-cloud-sdk-beta-20231208151900.tar.gz HTTP/1.1" 200 797
142
+ 2023-12-19 14:20:24,545 INFO ___FILE_ONLY___ ══════════════════════════════
143
+ 2023-12-19 14:20:24,546 INFO ___FILE_ONLY___ ══════════════════════════════
144
+ 2023-12-19 14:20:24,546 INFO ___FILE_ONLY___ ╝
145
+
146
+ 2023-12-19 14:20:24,555 INFO ___FILE_ONLY___ ��════════════════════════════════════════════════════════════╗
147
+
148
+ 2023-12-19 14:20:24,555 INFO ___FILE_ONLY___ ╠═ Creating backup and activating new installation ═╣
149
+
150
+ 2023-12-19 14:20:24,555 INFO ___FILE_ONLY___ ╚
151
+ 2023-12-19 14:20:24,555 DEBUG root Attempting to move directory [/tools/google-cloud-sdk] to [/tools/google-cloud-sdk.staging/.install/.backup]
152
+ 2023-12-19 14:20:24,555 INFO ___FILE_ONLY___ ══════════════════════════════
153
+ 2023-12-19 14:20:24,555 DEBUG root Attempting to move directory [/tools/google-cloud-sdk.staging] to [/tools/google-cloud-sdk]
154
+ 2023-12-19 14:20:24,555 INFO ___FILE_ONLY___ ══════════════════════════════
155
+ 2023-12-19 14:20:24,555 INFO ___FILE_ONLY___ ╝
156
+
157
+ 2023-12-19 14:20:24,560 DEBUG root Updating notification cache...
158
+ 2023-12-19 14:20:24,561 INFO ___FILE_ONLY___
159
+
160
+ 2023-12-19 14:20:24,563 INFO ___FILE_ONLY___ Performing post processing steps...
161
+ 2023-12-19 14:20:24,563 DEBUG root Executing command: ['python3', '-S', '/tools/google-cloud-sdk/lib/gcloud.py', 'components', 'post-process']
162
+ 2023-12-19 14:20:34,281 DEBUG ___FILE_ONLY___
163
+ 2023-12-19 14:20:34,282 DEBUG ___FILE_ONLY___
164
+ 2023-12-19 14:20:34,327 INFO ___FILE_ONLY___
165
+ Update done!
166
+
167
+
168
+ 2023-12-19 14:20:34,331 DEBUG root Chosen display Format:none
169
+ 2023-12-19 14:20:34,331 INFO root Display format: "none"
.config/logs/2023.12.19/14.20.25.266295.log ADDED
@@ -0,0 +1,5 @@
 
 
 
 
 
 
1
+ 2023-12-19 14:20:25,267 DEBUG root Loaded Command Group: ['gcloud', 'components']
2
+ 2023-12-19 14:20:25,269 DEBUG root Loaded Command Group: ['gcloud', 'components', 'post_process']
3
+ 2023-12-19 14:20:25,272 DEBUG root Running [gcloud.components.post-process] with arguments: []
4
+ 2023-12-19 14:20:34,156 DEBUG root Chosen display Format:none
5
+ 2023-12-19 14:20:34,157 INFO root Display format: "none"
.config/logs/2023.12.19/14.20.35.154355.log ADDED
@@ -0,0 +1,8 @@
 
 
 
 
 
 
 
 
 
1
+ 2023-12-19 14:20:35,156 DEBUG root Loaded Command Group: ['gcloud', 'config']
2
+ 2023-12-19 14:20:35,187 DEBUG root Loaded Command Group: ['gcloud', 'config', 'set']
3
+ 2023-12-19 14:20:35,190 DEBUG root Running [gcloud.config.set] with arguments: [SECTION/PROPERTY: "component_manager/disable_update_check", VALUE: "true"]
4
+ 2023-12-19 14:20:35,191 INFO ___FILE_ONLY___ Updated property [component_manager/disable_update_check].
5
+
6
+ 2023-12-19 14:20:35,191 DEBUG root Chosen display Format:default
7
+ 2023-12-19 14:20:35,192 INFO root Display format: "default"
8
+ 2023-12-19 14:20:35,193 DEBUG root SDK update checks are disabled.
.config/logs/2023.12.19/14.20.35.996145.log ADDED
@@ -0,0 +1,8 @@
 
 
 
 
 
 
 
 
 
1
+ 2023-12-19 14:20:35,998 DEBUG root Loaded Command Group: ['gcloud', 'config']
2
+ 2023-12-19 14:20:36,026 DEBUG root Loaded Command Group: ['gcloud', 'config', 'set']
3
+ 2023-12-19 14:20:36,029 DEBUG root Running [gcloud.config.set] with arguments: [SECTION/PROPERTY: "compute/gce_metadata_read_timeout_sec", VALUE: "0"]
4
+ 2023-12-19 14:20:36,030 INFO ___FILE_ONLY___ Updated property [compute/gce_metadata_read_timeout_sec].
5
+
6
+ 2023-12-19 14:20:36,031 DEBUG root Chosen display Format:default
7
+ 2023-12-19 14:20:36,032 INFO root Display format: "default"
8
+ 2023-12-19 14:20:36,033 DEBUG root SDK update checks are disabled.
.gitattributes CHANGED
@@ -33,3 +33,5 @@ saved_model/**/* filter=lfs diff=lfs merge=lfs -text
33
  *.zip filter=lfs diff=lfs merge=lfs -text
34
  *.zst filter=lfs diff=lfs merge=lfs -text
35
  *tfevents* filter=lfs diff=lfs merge=lfs -text
 
 
 
33
  *.zip filter=lfs diff=lfs merge=lfs -text
34
  *.zst filter=lfs diff=lfs merge=lfs -text
35
  *tfevents* filter=lfs diff=lfs merge=lfs -text
36
+ sample_data/mnist_test.csv filter=lfs diff=lfs merge=lfs -text
37
+ sample_data/mnist_train_small.csv filter=lfs diff=lfs merge=lfs -text
README.md CHANGED
@@ -1,12 +1,6 @@
1
  ---
2
- title: Visualizing Neural Networks
3
- emoji: 📈
4
- colorFrom: yellow
5
- colorTo: red
6
  sdk: gradio
7
  sdk_version: 4.12.0
8
- app_file: app.py
9
- pinned: false
10
  ---
11
-
12
- Check out the configuration reference at https://huggingface.co/docs/hub/spaces-config-reference
 
1
  ---
2
+ title: visualizing_Neural_Networks
3
+ app_file: /content/luanch_app.py
 
 
4
  sdk: gradio
5
  sdk_version: 4.12.0
 
 
6
  ---
 
 
luanch_app.py ADDED
@@ -0,0 +1,50 @@
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
1
+ from vizualize_nn import *
2
+ """## Launch the app"""
3
+
4
+ device ='cuda' if torch.cuda.is_available() else 'cpu'
5
+ init_net_and_train_part = partial(init_net_and_train,device=device)
6
+
7
+ with gr.Blocks() as iface:
8
+
9
+ tab_train = gr.Tab("Network Training")
10
+ tab_viz = gr.Tab("Network Visualization")
11
+
12
+ with tab_train:
13
+ hidden_units_slider = gr.Slider(minimum=1, maximum=10, step=1, value=4, label="number of neurons in hidden layer")
14
+ noise_slider = gr.Slider(minimum=0.001, maximum=0.7, step=0.01, value=0.2, label="Noise")
15
+ epochs_slider = gr.Slider(minimum=1, maximum=50, step=1, value=30, label="Epochs")
16
+ lr_slider = gr.Slider(minimum=0.001, maximum=0.05, step=0.001, value=0.008, label="Learning Rate")
17
+ data_points_slider = gr.Slider(minimum=100, maximum=2000, step=4, value=1000, label="Data Points")
18
+ train_button = gr.Button("Train Network")
19
+ learning_curve = gr.Plot(label="Learning Curve")
20
+
21
+ with tab_viz:
22
+ with (gr.Row() if NETWORK_ORIENTAION != 'h' else dummy_context()):
23
+ with (gr.Column() if NETWORK_ORIENTAION != 'h' else dummy_context()):
24
+ with (gr.Row() if NETWORK_ORIENTAION != 'v' else dummy_context()):
25
+ epoch_viz_slider = gr.Slider(minimum=1, maximum=50, step=1, value=1, label="Visualize Epoch") # Dynamic update needed here
26
+ ner_bounds = gr.Checkbox(label="Invidual neurons decision boundaries")
27
+ generate_button = gr.Button("Visualize Network")
28
+ plot_output = gr.Plot(label="Decision Boundary")
29
+ overall_net_output = gr.Image(type="filepath",label="Network Visualization")
30
+ with (gr.Column() if NETWORK_ORIENTAION != 'h' else dummy_context()):
31
+ with gr.Row():
32
+ input_x = gr.Number(label="Input X")
33
+ input_y = gr.Number(label="Input Y")
34
+ update_button = gr.Button("Check Input")
35
+ net_activity_sample_output = gr.HTML(label="Network Activity for an Input")
36
+ # net_activity_sample_output = gr.Image(type="filepath", label="Network Activity for an Input")
37
+
38
+ # Set up button click actions
39
+ train_button.click(fn=init_net_and_train, inputs=[hidden_units_slider, noise_slider, epochs_slider, data_points_slider, lr_slider], outputs=learning_curve)
40
+ generate_button.click(fn=generate_images, inputs=[epoch_viz_slider,ner_bounds], outputs=[plot_output, overall_net_output])
41
+ update_button.click(fn=get_network_with_inputs, inputs=[epoch_viz_slider, input_x, input_y], outputs=net_activity_sample_output)
42
+
43
+ # # Add Tabs to the interface
44
+ # iface.add_tabs(tab_train, tab_viz)
45
+
46
+ iface.title = "Neural Network Visualization"
47
+ iface.description = "Adjust parameters and train the network to see its performance and visualization."
48
+
49
+ # Launch the app
50
+ iface.launch()
requirements.txt ADDED
@@ -0,0 +1,9 @@
 
 
 
 
 
 
 
 
 
 
1
+ scikit-learn
2
+ torch
3
+ gradio
4
+ plotly
5
+ matplotlib
6
+ graphviz
7
+ colorsys
8
+ numpy
9
+ functools
sample_data/README.md ADDED
@@ -0,0 +1,19 @@
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
1
+ This directory includes a few sample datasets to get you started.
2
+
3
+ * `california_housing_data*.csv` is California housing data from the 1990 US
4
+ Census; more information is available at:
5
+ https://developers.google.com/machine-learning/crash-course/california-housing-data-description
6
+
7
+ * `mnist_*.csv` is a small sample of the
8
+ [MNIST database](https://en.wikipedia.org/wiki/MNIST_database), which is
9
+ described at: http://yann.lecun.com/exdb/mnist/
10
+
11
+ * `anscombe.json` contains a copy of
12
+ [Anscombe's quartet](https://en.wikipedia.org/wiki/Anscombe%27s_quartet); it
13
+ was originally described in
14
+
15
+ Anscombe, F. J. (1973). 'Graphs in Statistical Analysis'. American
16
+ Statistician. 27 (1): 17-21. JSTOR 2682899.
17
+
18
+ and our copy was prepared by the
19
+ [vega_datasets library](https://github.com/altair-viz/vega_datasets/blob/4f67bdaad10f45e3549984e17e1b3088c731503d/vega_datasets/_data/anscombe.json).
sample_data/anscombe.json ADDED
@@ -0,0 +1,49 @@
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
1
+ [
2
+ {"Series":"I", "X":10.0, "Y":8.04},
3
+ {"Series":"I", "X":8.0, "Y":6.95},
4
+ {"Series":"I", "X":13.0, "Y":7.58},
5
+ {"Series":"I", "X":9.0, "Y":8.81},
6
+ {"Series":"I", "X":11.0, "Y":8.33},
7
+ {"Series":"I", "X":14.0, "Y":9.96},
8
+ {"Series":"I", "X":6.0, "Y":7.24},
9
+ {"Series":"I", "X":4.0, "Y":4.26},
10
+ {"Series":"I", "X":12.0, "Y":10.84},
11
+ {"Series":"I", "X":7.0, "Y":4.81},
12
+ {"Series":"I", "X":5.0, "Y":5.68},
13
+
14
+ {"Series":"II", "X":10.0, "Y":9.14},
15
+ {"Series":"II", "X":8.0, "Y":8.14},
16
+ {"Series":"II", "X":13.0, "Y":8.74},
17
+ {"Series":"II", "X":9.0, "Y":8.77},
18
+ {"Series":"II", "X":11.0, "Y":9.26},
19
+ {"Series":"II", "X":14.0, "Y":8.10},
20
+ {"Series":"II", "X":6.0, "Y":6.13},
21
+ {"Series":"II", "X":4.0, "Y":3.10},
22
+ {"Series":"II", "X":12.0, "Y":9.13},
23
+ {"Series":"II", "X":7.0, "Y":7.26},
24
+ {"Series":"II", "X":5.0, "Y":4.74},
25
+
26
+ {"Series":"III", "X":10.0, "Y":7.46},
27
+ {"Series":"III", "X":8.0, "Y":6.77},
28
+ {"Series":"III", "X":13.0, "Y":12.74},
29
+ {"Series":"III", "X":9.0, "Y":7.11},
30
+ {"Series":"III", "X":11.0, "Y":7.81},
31
+ {"Series":"III", "X":14.0, "Y":8.84},
32
+ {"Series":"III", "X":6.0, "Y":6.08},
33
+ {"Series":"III", "X":4.0, "Y":5.39},
34
+ {"Series":"III", "X":12.0, "Y":8.15},
35
+ {"Series":"III", "X":7.0, "Y":6.42},
36
+ {"Series":"III", "X":5.0, "Y":5.73},
37
+
38
+ {"Series":"IV", "X":8.0, "Y":6.58},
39
+ {"Series":"IV", "X":8.0, "Y":5.76},
40
+ {"Series":"IV", "X":8.0, "Y":7.71},
41
+ {"Series":"IV", "X":8.0, "Y":8.84},
42
+ {"Series":"IV", "X":8.0, "Y":8.47},
43
+ {"Series":"IV", "X":8.0, "Y":7.04},
44
+ {"Series":"IV", "X":8.0, "Y":5.25},
45
+ {"Series":"IV", "X":19.0, "Y":12.50},
46
+ {"Series":"IV", "X":8.0, "Y":5.56},
47
+ {"Series":"IV", "X":8.0, "Y":7.91},
48
+ {"Series":"IV", "X":8.0, "Y":6.89}
49
+ ]
sample_data/california_housing_test.csv ADDED
The diff for this file is too large to render. See raw diff
 
sample_data/california_housing_train.csv ADDED
The diff for this file is too large to render. See raw diff
 
sample_data/mnist_test.csv ADDED
@@ -0,0 +1,3 @@
 
 
 
 
1
+ version https://git-lfs.github.com/spec/v1
2
+ oid sha256:51c292478d94ec3a01461bdfa82eb0885d262eb09e615679b2d69dedb6ad09e7
3
+ size 18289443
sample_data/mnist_train_small.csv ADDED
@@ -0,0 +1,3 @@
 
 
 
 
1
+ version https://git-lfs.github.com/spec/v1
2
+ oid sha256:1ef64781aa03180f4f5ce504314f058f5d0227277df86060473d973cf43b033e
3
+ size 36523880
temp/network_with_weights_activation_30.png ADDED
vizualize_nn.py ADDED
@@ -0,0 +1,562 @@
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
1
+
2
+ # !pip install gradio
3
+
4
+ from sklearn.model_selection import train_test_split
5
+ from sklearn.preprocessing import StandardScaler
6
+ from torch.utils.data import TensorDataset, DataLoader
7
+ import torch.nn as nn
8
+ import torch.optim as optim
9
+ import torch
10
+ # Visualize the simulated data
11
+ import matplotlib.pyplot as plt
12
+ import plotly.graph_objs as go
13
+ import IPython
14
+ import numpy as np
15
+ from graphviz import Digraph
16
+ import copy
17
+ import plotly.graph_objs as go
18
+ import torch
19
+ import numpy as np
20
+ import colorsys
21
+ from functools import partial
22
+ import gradio as gr # may requeire session restart
23
+ import os
24
+ import uuid
25
+ from contextlib import contextmanager
26
+ NETWORK_ORIENTAION = 'h' # 'h' for horizontal 'v' for vertical
27
+ TEMP_DIR = "/content/temp"
28
+ if not os.path.exists(TEMP_DIR):
29
+ os.makedirs(TEMP_DIR)
30
+
31
+ """## functions"""
32
+
33
+ # @title generate data
34
+
35
+ def simulate_clusters(noise=0.3,data_points=1000):
36
+ assert data_points%4==0, 'Data points should be dived by 4'
37
+ # Set random seed for reproducibility
38
+ np.random.seed(0)
39
+
40
+ # Define means and covariances for the Gaussian distributions
41
+ means = [(-1, -1), (-1, 1), (1, -1), (1, 1)]
42
+ covs = [np.eye(2) * noise for _ in means] # Small covariance for tight clusters
43
+
44
+ # Generate samples for each cluster
45
+ cluster_samples = []
46
+ for mean, cov in zip(means, covs):
47
+ samples = np.random.multivariate_normal(mean, cov, data_points//4)
48
+ cluster_samples.append(samples)
49
+
50
+ # Concatenate all samples and create labels
51
+ X = np.vstack(cluster_samples)
52
+ y = np.array([i//(data_points//4) for i in range(data_points)]) # Assign labels based on cluster index
53
+ # Clusters [(-1, -1), (1, 1)] have label 0, and [(-1, 1), (1, -1)] have label 1.
54
+ y_adjusted = np.array([0 if i in [0, 3] else 1 for i in y])
55
+
56
+ # Split the adjusted dataset
57
+ X_train_adj, X_test_adj, y_train_adj, y_test_adj = train_test_split(X, y_adjusted, test_size=0.2, random_state=42)
58
+
59
+ # Normalize the features
60
+ scaler_adj = StandardScaler()
61
+ X_train_scaled_adj = scaler_adj.fit_transform(X_train_adj)
62
+ X_test_scaled_adj = scaler_adj.transform(X_test_adj)
63
+
64
+ # Convert to PyTorch tensors
65
+ X_train_tensor_adj = torch.tensor(X_train_scaled_adj, dtype=torch.float32)
66
+ y_train_tensor_adj = torch.tensor(y_train_adj, dtype=torch.long)
67
+ X_test_tensor_adj = torch.tensor(X_test_scaled_adj, dtype=torch.float32)
68
+ y_test_tensor_adj = torch.tensor(y_test_adj, dtype=torch.long)
69
+
70
+ return X_train_tensor_adj,y_train_tensor_adj,X_test_tensor_adj,y_test_tensor_adj
71
+
72
+ # @title plotting network with activation
73
+ def get_color(activation, base_color=False):
74
+ if base_color:
75
+ # Convert base color from hex to RGB
76
+ r_base, g_base, b_base = int(base_color[1:3], 16), int(base_color[3:5], 16), int(base_color[5:7], 16)
77
+
78
+ # Interpolate between the base color and white based on activation
79
+ r = r_base + (255 - r_base) * (1 - activation)
80
+ g = g_base + (255 - g_base) * (1 - activation)
81
+ b = b_base + (255 - b_base) * (1 - activation)
82
+
83
+ return f'#{int(r):02x}{int(g):02x}{int(b):02x}'
84
+
85
+
86
+ else:
87
+ if activation > 0:
88
+ return f"#0000FF{int(activation * 255):02X}" # Blue with varying intensity
89
+ return "#E0E0E0" # Light gray for inactive neurons
90
+
91
+
92
+ rd = lambda activation: ("\n"+"{:.2f}".format(torch.round(activation,decimals=2).item())) if activation!=1 else ''
93
+ #sigmoid = lambda x: 1 / (1 + torch.exp(-x)) if x!=1 else 1
94
+ softmax = lambda x: torch.exp(x) / torch.sum(torch.exp(x), axis=0) if all(x!=1) else x
95
+
96
+
97
+ rd = lambda activation: ("\n"+"{:.2f}".format(torch.round(activation,decimals=2).item())) if activation!=1 else ''
98
+ def visualize_network_with_weights(model, activations=False, norm='net', decision_boundary_images=None, width=1, height=1):
99
+ dot = Digraph()
100
+ if NETWORK_ORIENTAION=='h':
101
+ dot.attr(rankdir='LR')
102
+ pos_color = "blue"
103
+ neg_color = "orange"
104
+ layers_weights = {}
105
+ max_weight = 0
106
+ number_of_layer = 3
107
+ # Colors for different layers
108
+ input_color, hidden_color, output_color1,output_color2 = '#90EE90','#D3D3D3', '#FFB6C1' , '#ADD8E6' # light grey, light green,light red, light blue
109
+
110
+ # Extract weights for each layer and calculate max weight for normalization
111
+ for name, layer in model.named_children():
112
+ if isinstance(layer, torch.nn.Linear):
113
+ layer_weight = layer.weight.cpu().data.numpy()
114
+ layers_weights[name] = layer_weight
115
+ max_weight = max(max_weight, np.abs(layer_weight).max())
116
+ output_layer_name = name #this evantually save the output layer name
117
+ # Initialize activations if not provided
118
+ if not activations:
119
+ activations = {layer: [1] * weight.shape[0] for layer, weight in layers_weights.items()}
120
+
121
+ # Normalize weights for visualization purposes
122
+ layers_weights_norm = {layer: weight / (np.abs(weight).max() if norm == 'layer' else max_weight)
123
+ for layer, weight in layers_weights.items()}
124
+ def add_node_with_border(node_id, label, base_color, activation, image_path=None, shape='circle', border_color='black', border_width=1):
125
+ fill_color = get_color(activation, base_color)
126
+ if image_path:
127
+ dot.node(node_id, label, shape='box', style='filled', fillcolor=fill_color, color=border_color, penwidth=str(border_width),imagescale='both', width=str(width), height=str(height), image=image_path, fixedsize='true')
128
+ else:
129
+ dot.node(node_id, label, shape=shape, style='filled', fillcolor=fill_color, color=border_color, penwidth=str(border_width))
130
+ axis_names = ['X','Y']
131
+ # Add nodes and edges...
132
+ for i in range(layers_weights['fc1'].shape[1]):
133
+ add_node_with_border(f'h0_{i}' , f'X{i} - {axis_names[i]} Axis', input_color, 1.0) # Input nodes are always 'active'
134
+
135
+ for layer_i in range(1,number_of_layer):
136
+ layer_name = 'fc'+str(layer_i)
137
+ for i, activation in enumerate(activations[layer_name]):
138
+ image_path = decision_boundary_images[layer_name][i] if decision_boundary_images and layer_name in decision_boundary_images and len(decision_boundary_images[layer_name]) > i else None
139
+ add_node_with_border(f'h{layer_i}_{i}', f'H{layer_i}_{i}{rd(activation)}', hidden_color, activation, image_path=image_path)
140
+ norm_output_activations = softmax(torch.tensor([activations[output_layer_name][0],activations[output_layer_name][1]]))
141
+ activation_label1,activation_label2 = norm_output_activations
142
+ add_node_with_border(f'h{number_of_layer}_0', f"Y0 - Label 0{rd(activation_label1)}", output_color1, activation_label1,shape='doublecircle')
143
+ add_node_with_border(f'h{number_of_layer}_1', f"Y1 - Label 1{rd(activation_label2)}", output_color2, activation_label2,shape='doublecircle')
144
+
145
+
146
+ # Adding edges between layers
147
+ prev_layer_size = layers_weights[list(layers_weights.keys())[0]].shape[1] # Size of the input layer
148
+ prev_layer_name = 'h0'
149
+
150
+ for layer_idx, (layer_name, weight_matrix) in enumerate(layers_weights.items(), start=1):
151
+ current_layer_size = weight_matrix.shape[0]
152
+
153
+ for i in range(prev_layer_size):
154
+ for j in range(current_layer_size):
155
+ color = pos_color if weight_matrix[j, i] >= 0 else neg_color
156
+ dot.edge(f'{prev_layer_name}_{i}', f'h{layer_idx}_{j}', penwidth=str(abs(layers_weights_norm[layer_name][j, i]) * 5), color=color)
157
+
158
+ prev_layer_size = current_layer_size
159
+ prev_layer_name = f'h{layer_idx}'
160
+
161
+ return dot
162
+
163
+ # @title Plots (learning curve and decision boundary)
164
+ def plot_decision_boundary(model, X_train, y_train, X_test, y_test, show=True, epoch=''):
165
+ # Set model to evaluation mode
166
+ model.eval()
167
+
168
+ # Set min and max values and give it some padding
169
+ x_min, x_max = min(X_train[:, 0].min(), X_test[:, 0].min()) - 1, max(X_train[:, 0].max(), X_test[:, 0].max()) + 1
170
+ y_min, y_max = min(X_train[:, 1].min(), X_test[:, 1].min()) - 1, max(X_train[:, 1].max(), X_test[:, 1].max()) + 1
171
+ h = 0.01
172
+
173
+ # Generate a grid of points with distance h between them
174
+ xx, yy = np.meshgrid(np.arange(x_min, x_max, h), np.arange(y_min, y_max, h))
175
+
176
+ # Flatten the grid so the values match expected input
177
+ grid = np.c_[xx.ravel(), yy.ravel()]
178
+ grid_tensor = torch.FloatTensor(grid)
179
+ with torch.no_grad():
180
+ predictions = model(grid_tensor.to(model.device)).argmax(1).to('cpu')
181
+ Z = predictions.numpy().reshape(xx.shape)
182
+
183
+ # Create the contour plot
184
+ contour = go.Contour(
185
+ x=np.arange(x_min, x_max, h),
186
+ y=np.arange(y_min, y_max, h),
187
+ z=Z,
188
+ colorscale='RdYlBu', # Light colors for background
189
+ showscale=False # Hide the colorbar
190
+ )
191
+
192
+ # Separate data based on labels
193
+ train_0 = X_train[y_train == 0]
194
+ train_1 = X_train[y_train == 1]
195
+ test_0 = X_test[y_test == 0]
196
+ test_1 = X_test[y_test == 1]
197
+
198
+ # Create scatter plots for each category
199
+ train_0_scatter = go.Scatter(x=train_0[:, 0], y=train_0[:, 1], mode='markers',
200
+ marker=dict(color='red', line=dict(color='black', width=1)),
201
+ name='Train - Label 0')
202
+ train_1_scatter = go.Scatter(x=train_1[:, 0], y=train_1[:, 1], mode='markers',
203
+ marker=dict(color='green', line=dict(color='black', width=1)),
204
+ name='Train - Label 1')
205
+ test_0_scatter = go.Scatter(x=test_0[:, 0], y=test_0[:, 1], mode='markers',
206
+ marker=dict(color='rgba(255, 200, 200, 1)', symbol='circle-open', line=dict(color='black', width=1)),
207
+ name='Test - Label 0')
208
+ test_1_scatter = go.Scatter(x=test_1[:, 0], y=test_1[:, 1], mode='markers',
209
+ marker=dict(color='rgba(200, 255, 200, 1)', symbol='circle-open', line=dict(color='black', width=1)),
210
+ name='Test - Label 1')
211
+
212
+ # Define the layout
213
+ layout = go.Layout(
214
+ title='Decision Boundary ' + epoch,
215
+ xaxis=dict(title='Feature 1'),
216
+ yaxis=dict(title='Feature 2'),
217
+ showlegend=True
218
+ )
219
+ # Create the figure and add the contour and scatter plots
220
+ fig = go.Figure(data=[contour, train_0_scatter, train_1_scatter, test_0_scatter, test_1_scatter], layout=layout)
221
+
222
+ # Show the plot
223
+ if show: fig.show()
224
+ return fig
225
+
226
+
227
+ def generate_learning_curve(loss_hist, loss_val_hist, hidden_units, noise, epochs, lr,metric):
228
+ with torch.no_grad():
229
+ metric = 'Loss' if metric.lower()=='loss' else "Accuracy"
230
+ # Create traces for the training and validation loss
231
+ trace_train = go.Scatter(
232
+ x=list(range(1, epochs + 1)),
233
+ y=loss_hist,
234
+ mode='lines',
235
+ name=f'Training {metric}'
236
+ )
237
+ trace_val = go.Scatter(
238
+ x=list(range(1, epochs + 1)),
239
+ y=loss_val_hist,
240
+ mode='lines',
241
+ name=f'Validation {metric}'
242
+ )
243
+
244
+ # Combine traces
245
+ data = [trace_train, trace_val]
246
+
247
+ # Layout for the plot
248
+ layout = go.Layout(
249
+ title=f'Learning Curve - Hidden Units: {hidden_units}, Noise: {noise}, Learning Rate: {lr}',
250
+ xaxis=dict(title='Epochs'),
251
+ yaxis=dict(title=metric),
252
+
253
+ )
254
+
255
+ # Create the figure and show it
256
+ fig = go.Figure(data=data, layout=layout)
257
+ return fig
258
+
259
+ def save_plot_as_image(fig, remove_axes=True, remove_title=True, remove_colorbar=True, transparent_background=True):
260
+ """
261
+ Saves a Matplotlib figure as an image and returns the path to the image.
262
+
263
+ Args:
264
+ fig (matplotlib.figure.Figure): The Matplotlib figure to save.
265
+ remove_axes (bool): If True, removes the axes from the plot.
266
+ remove_title (bool): If True, removes the title and header from the plot.
267
+ remove_colorbar (bool): If True, removes the colorbar from the plot.
268
+ transparent_background (bool): If True, saves the image with a transparent background.
269
+
270
+ Returns:
271
+ str: Path to the saved image file.
272
+ """
273
+ # Check if fig is a valid Matplotlib figure
274
+ if not isinstance(fig, plt.Figure):
275
+ raise ValueError("The provided object is not a Matplotlib figure.")
276
+
277
+ # Remove axes if requested
278
+ if remove_axes:
279
+ for ax in fig.axes:
280
+ ax.get_xaxis().set_visible(False)
281
+ ax.get_yaxis().set_visible(False)
282
+ ax.set_frame_on(False)
283
+
284
+ # Remove title and header if requested
285
+ if remove_title:
286
+ fig.suptitle("")
287
+ for ax in fig.axes:
288
+ ax.title.set_visible(False)
289
+
290
+ # Remove colorbar if requested
291
+ if remove_colorbar:
292
+ for ax in fig.axes:
293
+ if hasattr(ax, 'collections') and ax.collections:
294
+ # Check for the presence of a colorbar in this axis
295
+ for im in ax.get_images():
296
+ if hasattr(im, 'colorbar') and im.colorbar:
297
+ im.colorbar.remove()
298
+
299
+ # Set transparent background if requested
300
+ if transparent_background:
301
+ fig.patch.set_alpha(0)
302
+ for ax in fig.axes:
303
+ ax.patch.set_alpha(0)
304
+
305
+
306
+ # Generate a unique filename for the image
307
+ filename = f"plot_{uuid.uuid4()}.png"
308
+ file_path = os.path.join(TEMP_DIR, filename)
309
+
310
+ # Save the figure with a transparent background if requested
311
+ fig.savefig(file_path, bbox_inches='tight', pad_inches=0, transparent=transparent_background)
312
+
313
+ return file_path
314
+
315
+ def plot_neuron_decision_boundaries(model, X, step=0.01):
316
+ # Ensure X is a NumPy array
317
+ if isinstance(X, torch.Tensor):
318
+ X = X.cpu().numpy()
319
+ mesh_border_expansion = 0.5 # the mesh is calculted between the highest and lowest values in each axis, with `mesh_border_expansion` additional space
320
+ # Generate mesh grid for decision boundaries
321
+ x_min, x_max = X[:, 0].min() - mesh_border_expansion , X[:, 0].max() + mesh_border_expansion
322
+ y_min, y_max = X[:, 1].min() - mesh_border_expansion , X[:, 1].max() + mesh_border_expansion
323
+ xx, yy = np.meshgrid(np.arange(x_min, x_max, step), np.arange(y_min, y_max, step))
324
+ mesh_inputs = torch.Tensor(np.c_[xx.ravel(), yy.ravel()])
325
+
326
+ model.eval()
327
+ figures_dict = {}
328
+ layer_outputs = mesh_inputs
329
+ with torch.no_grad():
330
+ for name, layer in model.named_children():
331
+ # Apply the layer
332
+ layer_outputs = layer(layer_outputs.to(model.device))
333
+
334
+ # Check if the layer is ReLU or the last layer
335
+ if isinstance(layer, nn.Linear) or (name == list(model.named_children())[-1][0]):
336
+ # Convert to NumPy for plotting
337
+ outputs_np = layer_outputs.cpu().numpy()
338
+ for neuron_idx in range(outputs_np.shape[1]):
339
+ Z = outputs_np[:, neuron_idx].reshape(xx.shape)
340
+
341
+ Z_min, Z_max = Z.min(), Z.max()
342
+ levels = sorted([Z_min, 0, Z_max]) if Z_min < 0 < Z_max else [Z_min, Z_max]
343
+
344
+ fig, ax = plt.subplots()
345
+ # ax.contourf(xx, yy, Z, levels=np.linspace(Z.min(), Z.max(), 200), cmap=plt.cm.RdBu, alpha=0.8)
346
+ ax.contourf(xx, yy, Z, levels=levels, cmap=plt.cm.RdBu, alpha=0.8)
347
+ # ax.set_title(f"Decision boundary of Neuron {neuron_idx+1} in {name}")
348
+ # ax.set_xlabel('Feature 1')
349
+ # ax.set_ylabel('Feature 2')
350
+ plt.show()
351
+ plt.close(fig)
352
+ if name not in figures_dict:
353
+ figures_dict[name]=[]
354
+ figures_dict[name] += [fig]
355
+
356
+ return figures_dict
357
+
358
+
359
+ # plot_neuron_decision_boundaries( fc_model, X_train)
360
+
361
+ # step=0.01
362
+ # x_min, x_max = X_train[:, 0].min() - 1, X_train[:, 0].max() + 1
363
+ # y_min, y_max = X_train[:, 1].min() - 1, X_train[:, 1].max() + 1
364
+ # xx, yy = np.meshgrid(np.arange(x_min, x_max, step), np.arange(y_min, y_max, step))
365
+ # mesh_inputs = torch.Tensor(np.c_[xx.ravel(), yy.ravel()])
366
+ # mesh_inputs
367
+
368
+ # @title network architecture and training
369
+
370
+ # Global variables to hold model and data
371
+ global fc_model_hist, X_train, y_train, X_test, y_test
372
+ fc_model_hist, X_train, y_train, X_test, y_test = None, None, None, None, None
373
+
374
+ class FCNet(nn.Module):
375
+ def __init__(self,hidden_units,device):
376
+ super(FCNet, self).__init__()
377
+ self.fc1 = nn.Linear(2, hidden_units) # Input layer with 2 features
378
+ self.act_func1 = nn.ReLU() # it is important to declare on each relu layer, becuase some of the plotting functions uses model.named_layers() and the ReLU won't be there without explicit declration here
379
+ self.fc2 = nn.Linear(hidden_units, hidden_units)
380
+ self.act_func2 = nn.ReLU()
381
+ self.fc3 = nn.Linear(hidden_units, 2) # Output layer with 2 neurons (for 2 classes)
382
+ self.device = device
383
+ def forward(self, x):
384
+ x = self.act_func1(self.fc1(x))
385
+ x = self.act_func2(self.fc2(x))
386
+ x = self.fc3(x)
387
+ return x
388
+ def forward_with_activation(self, x):
389
+ inputs = x
390
+ x1 = self.act_func1(self.fc1(x))
391
+ x2 = self.act_func2(self.fc2(x1))
392
+ x3 = self.fc3(x2)
393
+ return x,{'inputs':inputs,'fc1':x1,'fc2':x2,'fc3':x3}
394
+ def to(self, device):
395
+ super().to(device)
396
+ self.device = device
397
+ return self
398
+
399
+ def init_net_and_train(hidden_units = 4,noise = 0.2,epochs = 30,data_points = 1000,lr=0.01,device='cpu',metric='acc'):
400
+ global fc_model_hist, X_train, y_train, X_test, y_test
401
+ # Simulate the dataset
402
+ X_train,y_train,X_test,y_test = simulate_clusters(noise,data_points)
403
+
404
+ # Create TensorDataset and DataLoader
405
+ train_dataset_adj = TensorDataset(X_train, y_train)
406
+ train_loader_adj = DataLoader(train_dataset_adj, batch_size=64, shuffle=True)
407
+ test_dataset_adj = TensorDataset(X_test, y_test)
408
+ test_loader_adj = DataLoader(test_dataset_adj, batch_size=64, shuffle=True)
409
+ # Define a simple Fully Connected network with fewer neurons
410
+ # Initialize the simple fully connected neural network
411
+ fc_model = FCNet(hidden_units,device=device)
412
+ fc_model.to(device)
413
+ # Loss and optimizer for the FC network
414
+ fc_criterion = nn.CrossEntropyLoss()
415
+ fc_optimizer = optim.Adam(fc_model.parameters(), lr=lr)
416
+
417
+ # Training loop for the simple FC network
418
+ fc_model_hist = []
419
+
420
+ # loss_hist = []
421
+ # loss_val_hist = []
422
+
423
+ # for epoch in range(epochs):
424
+ # cur_epoch_loss=torch.tensor(0.,device=fc_model.device)
425
+ # inputs_len = 0
426
+ # for inputs, labels in train_loader_adj:
427
+ # # Forward pass
428
+ # outputs = fc_model(inputs.to(device))
429
+ # loss = fc_criterion(outputs, labels.to(device))
430
+ # cur_epoch_loss+=loss
431
+ # inputs_len += labels.shape[0]
432
+ # # Backward and optimize
433
+ # fc_optimizer.zero_grad()
434
+ # loss.backward()
435
+ # fc_optimizer.step()
436
+ # train_loss = cur_epoch_loss.cpu()/inputs_len
437
+ # loss_hist.append(train_loss)
438
+ # fc_model_hist.append(copy.deepcopy(fc_model).to('cpu'))
439
+ # with torch.no_grad():
440
+ # cur_epoch_loss=torch.tensor(0.,device=device)
441
+ # inputs_len = 0
442
+ # for inputs, labels in test_loader_adj:
443
+ # outputs = fc_model(inputs.to(device))
444
+ # loss = fc_criterion(outputs, labels.to(device))
445
+ # cur_epoch_loss+=loss
446
+ # inputs_len += labels.shape[0]
447
+ # test_loss = cur_epoch_loss.cpu()/inputs_len
448
+ # loss_val_hist.append(test_loss)
449
+
450
+ loss_hist = []
451
+ loss_val_hist = []
452
+ acc_hist = []
453
+ acc_val_hist = []
454
+
455
+ device = fc_model.device
456
+
457
+ for epoch in range(epochs):
458
+ fc_model.train() # Set model to training mode
459
+ cur_epoch_loss = 0
460
+ correct_train = 0
461
+ total_train = 0
462
+
463
+ for inputs, labels in train_loader_adj:
464
+ inputs, labels = inputs.to(device), labels.to(device)
465
+ fc_optimizer.zero_grad()
466
+ outputs = fc_model(inputs)
467
+ loss = fc_criterion(outputs, labels)
468
+ loss.backward()
469
+ fc_optimizer.step()
470
+
471
+ cur_epoch_loss += loss.item() * inputs.size(0)
472
+ _, predicted = torch.max(outputs.data, 1)
473
+ total_train += labels.size(0)
474
+ correct_train += (predicted == labels).sum().item()
475
+
476
+ train_loss = cur_epoch_loss / total_train
477
+ train_accuracy = correct_train / total_train
478
+ loss_hist.append(train_loss)
479
+ acc_hist.append(train_accuracy)
480
+
481
+ fc_model.eval() # Set model to evaluation mode for validation
482
+ fc_model_hist.append(copy.deepcopy(fc_model).to('cpu'))
483
+ cur_epoch_loss = 0
484
+ correct_test = 0
485
+ total_test = 0
486
+
487
+ with torch.no_grad():
488
+ for inputs, labels in test_loader_adj:
489
+ inputs, labels = inputs.to(device), labels.to(device)
490
+ outputs = fc_model(inputs)
491
+ loss = fc_criterion(outputs, labels)
492
+
493
+ cur_epoch_loss += loss.item() * inputs.size(0)
494
+ _, predicted = torch.max(outputs.data, 1)
495
+ total_test += labels.size(0)
496
+ correct_test += (predicted == labels).sum().item()
497
+
498
+ test_loss = cur_epoch_loss / total_test
499
+ test_accuracy = correct_test / total_test
500
+ loss_val_hist.append(test_loss)
501
+ acc_val_hist.append(test_accuracy)
502
+
503
+
504
+ # print(f'Epoch [{epoch+1}/{epochs}], Train Loss: {train_loss:.4f}, Test Loss: {test_loss:.4f}')
505
+
506
+ # return fc_model,fc_model_hist,loss_hist,X_train,y_train,X_test,y_test
507
+ if metric=='acc':
508
+ reported_metric_train,reported_metric_val = acc_hist,acc_val_hist
509
+ else:
510
+ reported_metric_train,reported_metric_val = loss_hist,loss_val_hist
511
+ return generate_learning_curve(reported_metric_train,reported_metric_val,hidden_units,noise,epochs,lr,metric)
512
+
513
+ # @title functions for retriving app images
514
+ def get_network_with_inputs(epoch, input_x, input_y,output_type = "HTML"):
515
+ if epoch>len(fc_model_hist):
516
+ epoch = len(fc_model_hist)
517
+ with torch.no_grad():
518
+ cur_model = fc_model_hist[epoch - 1]
519
+ out, activations = cur_model.forward_with_activation(torch.tensor([input_x, input_y], dtype=torch.float32,device=cur_model.device))
520
+ network_dot = visualize_network_with_weights(cur_model, activations=activations)
521
+ if output_type=='PNG':
522
+ cur_path = f'network_with_weights_activation_{epoch}'
523
+ network_dot.render(cur_path, format='png', cleanup=True)
524
+ return cur_path + ".png"
525
+ else:
526
+ svg_content = network_dot.pipe(format='svg').decode('utf-8')
527
+ # Create HTML content embedding the SVG
528
+ html_content = f'<div style="width:100%; height:100%;">{svg_content}</div>'
529
+ return html_content
530
+
531
+
532
+ get_plots_as_png = lambda des_list: [save_plot_as_image(plot) for plot in des_list]
533
+
534
+
535
+ as_HTML=False
536
+
537
+ def generate_images(epoch,net_with_unit_decisions=True):
538
+ global fc_model_hist
539
+ if epoch>len(fc_model_hist):
540
+ epoch = len(fc_model_hist)
541
+ fig = plot_decision_boundary(fc_model_hist[epoch-1], X_train, y_train, X_test, y_test, show=False,epoch=f'Epoch:{epoch}')
542
+ # network_html = network_dot_paths_list[epoch]
543
+ if not net_with_unit_decisions:
544
+ network_dot = visualize_network_with_weights(fc_model_hist[epoch-1])
545
+ else:
546
+ decision_plots = plot_neuron_decision_boundaries(fc_model_hist[epoch-1], X_train)
547
+ decision_boundary_images = {k:get_plots_as_png(decision_plots[k]) for k in decision_plots}
548
+ network_dot = visualize_network_with_weights(fc_model_hist[epoch-1], activations=False, decision_boundary_images=decision_boundary_images)
549
+ if as_HTML:
550
+ svg_content = network_dot.pipe(format='svg').decode('utf-8')
551
+ network_proccessed = f'<div style="width:100%; height:100%;">{svg_content}</div>'
552
+ else:
553
+ cur_path = f'{TEMP_DIR}/network_with_weights_activation_{epoch}'
554
+ network_dot.render(cur_path, format='png', cleanup=True)
555
+ network_proccessed = cur_path+".png"
556
+
557
+ return fig, network_proccessed
558
+
559
+ @contextmanager
560
+ def dummy_context():
561
+ yield
562
+