{"version":2,"relationshipURLBase":"https://codingai-lec03.pages.dev/course/notebooks/b5ba27f2c5ba4ec0922208a7d5dc80a4.html","relationshipResolution":"Resolve relative relationship URLs against relationshipURLBase; complete records and absolute URLs are in blocks.","title":"LAB 08 · 图像重建：遮挡损失与表示评价","url":"https://codingai-lec03.pages.dev/course/notebooks/b5ba27f2c5ba4ec0922208a7d5dc80a4.html","markdown":"https://codingai-lec03.pages.dev/course/notebooks/b5ba27f2c5ba4ec0922208a7d5dc80a4.md","pages":[{"id":"reading-opening","title":"LAB 08 · 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page"},{"oldId":"b-1da0c79dd77af89e628e8ac0","targetId":"b-1da0c79dd77af89e628e8ac0","page":"reading-opening","operation":"move","beforeSHA256":"f22152fd55dcdb0090b5653d37145694fd19388d31e10226549464fe8b483578","afterSHA256":"f22152fd55dcdb0090b5653d37145694fd19388d31e10226549464fe8b483578","reason":"Same source body in authored semantic page"},{"oldId":"b-f5e753a83fb514adc2a12322","targetId":"b-f5e753a83fb514adc2a12322","page":"reading-opening","operation":"move","beforeSHA256":"8556351a0a818e5fa2ef8199862458dc0e7165abe7355c0f371bb6d655e593e9","afterSHA256":"8556351a0a818e5fa2ef8199862458dc0e7165abe7355c0f371bb6d655e593e9","reason":"Same source body in authored semantic page"},{"oldId":"b-4219d840bb68fb4c3c52e1be","targetId":"b-4219d840bb68fb4c3c52e1be","page":"reading-opening","operation":"move","beforeSHA256":"318354340f6400ba4b75531df2ac69ab8253c32abf1ae13da28809001b9464d2","afterSHA256":"318354340f6400ba4b75531df2ac69ab8253c32abf1ae13da28809001b9464d2","reason":"Same source body 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page"},{"oldId":"b-52f4350b839e39006fd72ebe","targetId":"b-52f4350b839e39006fd72ebe","page":"reading-opening","operation":"move","beforeSHA256":"53664b678211e118eca6cdb1b5f1ccda4bd021cefe38a6db4910fa6295ffd20d","afterSHA256":"53664b678211e118eca6cdb1b5f1ccda4bd021cefe38a6db4910fa6295ffd20d","reason":"Same source body in authored semantic page"},{"oldId":"b-b4042a02d301d2a192c865d6","targetId":"b-b4042a02d301d2a192c865d6","page":"reading-block-44a741e9a3334aa4b16f140a98179497","operation":"move","beforeSHA256":"c7ddd926153f66a12ff50445e4f0c91d5b1b3bdd966cdc956f2f98dfc816e780","afterSHA256":"c7ddd926153f66a12ff50445e4f0c91d5b1b3bdd966cdc956f2f98dfc816e780","reason":"Same source body in authored semantic page"},{"oldId":"b-04258a1d2b81b52b597d10f2","targetId":"b-04258a1d2b81b52b597d10f2","page":"reading-block-44a741e9a3334aa4b16f140a98179497","operation":"move","beforeSHA256":"87299f7751b6bc58e001a1cac6a5323dd0001ad6d4f42408db1fc6f2d370de31","afterSHA256":"87299f7751b6bc58e001a1cac6a5323dd0001ad6d4f42408db1fc6f2d370de31","reason":"Same source body in authored semantic page"},{"oldId":"b-bab1eb3ab23026199996fda2","targetId":"b-bab1eb3ab23026199996fda2","page":"reading-block-44a741e9a3334aa4b16f140a98179497","operation":"move","beforeSHA256":"9d9abf237d90a9561e1846c380f3fcdce763427c83b474ae06bfeb3ad755dcfb","afterSHA256":"9d9abf237d90a9561e1846c380f3fcdce763427c83b474ae06bfeb3ad755dcfb","reason":"Same source body in authored semantic page"},{"oldId":"b-1af15ccdc4aac1a3725ba738","targetId":"b-1af15ccdc4aac1a3725ba738","page":"reading-block-44a741e9a3334aa4b16f140a98179497","operation":"move","beforeSHA256":"a5e8ed9d3fedff981b54ed3173e41c010f861683fde366e467627a295d351e8f","afterSHA256":"a5e8ed9d3fedff981b54ed3173e41c010f861683fde366e467627a295d351e8f","reason":"Same source body in authored semantic page"},{"oldId":"b-991deecf2ba93db8af7e8037","targetId":"b-991deecf2ba93db8af7e8037","page":"reading-block-8430582e1f5e4998a68580017714e408","operation":"move","beforeSHA256":"9bbc7527a59bb35d5da5d51d5f5bc342082de91f94066bfa434ed27e431f230a","afterSHA256":"9bbc7527a59bb35d5da5d51d5f5bc342082de91f94066bfa434ed27e431f230a","reason":"Same source body in authored semantic page"},{"oldId":"b-23022adb8e0454d12772c269","targetId":"b-23022adb8e0454d12772c269","page":"reading-block-8430582e1f5e4998a68580017714e408","operation":"move","beforeSHA256":"7c7112f4f7442896ffc602e4bca9c95cb6afe323f539ecace8877e95dc68b391","afterSHA256":"7c7112f4f7442896ffc602e4bca9c95cb6afe323f539ecace8877e95dc68b391","reason":"Same source body in authored semantic page"},{"oldId":"b-d5a62b6b0c9452a3f06eb767","targetId":"b-d5a62b6b0c9452a3f06eb767","page":"reading-block-8430582e1f5e4998a68580017714e408","operation":"move","beforeSHA256":"45cb652f6dde94cb2e85f0a141910fa3060c64947b747c6458e059c5d93f374c","afterSHA256":"45cb652f6dde94cb2e85f0a141910fa3060c64947b747c6458e059c5d93f374c","reason":"Same source body in authored semantic page"},{"oldId":"b-ca29cc71ee4377418ef0a2b5","targetId":"b-ca29cc71ee4377418ef0a2b5","page":"reading-block-8430582e1f5e4998a68580017714e408","operation":"move","beforeSHA256":"d054471d9556f974200f75070e378e4dfc31a7a89323ebc8ceddb672b594ed38","afterSHA256":"d054471d9556f974200f75070e378e4dfc31a7a89323ebc8ceddb672b594ed38","reason":"Same source body in authored semantic page"},{"oldId":"b-85ab5341d597235675d7126b","targetId":"b-85ab5341d597235675d7126b","page":"reading-block-32b1e5f410384ab9a6ae63440ff4f425","operation":"move","beforeSHA256":"ffd82c5a863f2930d5cea61e508a508c1126c1a93e8bd026430f0daa4e1aa161","afterSHA256":"ffd82c5a863f2930d5cea61e508a508c1126c1a93e8bd026430f0daa4e1aa161","reason":"Same source body in authored semantic page"},{"oldId":"b-33cb1bdd26be6963673b5f3a","targetId":"b-33cb1bdd26be6963673b5f3a","page":"reading-block-32b1e5f410384ab9a6ae63440ff4f425","operation":"move","beforeSHA256":"b5b552b03d3348b7ffa5e79b679ae61d1637f36cb0126bd432501592ccf384e4","afterSHA256":"b5b552b03d3348b7ffa5e79b679ae61d1637f36cb0126bd432501592ccf384e4","reason":"Same source body in authored semantic page"},{"oldId":"b-24f0ed58d7093486c0b77b97","targetId":"b-24f0ed58d7093486c0b77b97","page":"reading-block-32b1e5f410384ab9a6ae63440ff4f425","operation":"move","beforeSHA256":"dddc3aa4792ce564399c5a9042008ef6961c269f1be67e70b99cb08f9ee8143d","afterSHA256":"dddc3aa4792ce564399c5a9042008ef6961c269f1be67e70b99cb08f9ee8143d","reason":"Same source body in authored semantic page"},{"oldId":"b-4dc20d6e2e3fe21e5c076420","targetId":"b-4dc20d6e2e3fe21e5c076420","page":"reading-block-32b1e5f410384ab9a6ae63440ff4f425","operation":"move","beforeSHA256":"e66f545513713358d5ac1cd3ddcc275038454a49c79c2353a84651c3442edbdd","afterSHA256":"e66f545513713358d5ac1cd3ddcc275038454a49c79c2353a84651c3442edbdd","reason":"Same source body in authored semantic page"},{"oldId":"b-689691a7ffdc6c0e50f59abc","targetId":"b-689691a7ffdc6c0e50f59abc","page":"reading-block-32b1e5f410384ab9a6ae63440ff4f425","operation":"move","beforeSHA256":"e69117b96838ca54dc91ac1267742da52d685277ae4d6dc3dbbec80d69fece9c","afterSHA256":"e69117b96838ca54dc91ac1267742da52d685277ae4d6dc3dbbec80d69fece9c","reason":"Same source body in authored semantic page"},{"oldId":"b-746882bf55072c50fe67278e","targetId":"b-746882bf55072c50fe67278e","page":"reading-block-f7555aca3cff49f492d243beb43d1bf6","operation":"move","beforeSHA256":"1e535ce05cfe5eb3201aafe7ef126832c60a9410ac57616575ebfde505c66446","afterSHA256":"1e535ce05cfe5eb3201aafe7ef126832c60a9410ac57616575ebfde505c66446","reason":"Same source body in authored semantic page"},{"oldId":"b-8c82cc249dc215427d69bcf7","targetId":"b-8c82cc249dc215427d69bcf7","page":"reading-block-f7555aca3cff49f492d243beb43d1bf6","operation":"move","beforeSHA256":"9060158f67fb888087b1cfe7c5d2615563c150a54cca1810d4c56bc38e373a25","afterSHA256":"9060158f67fb888087b1cfe7c5d2615563c150a54cca1810d4c56bc38e373a25","reason":"Same source body in authored semantic page"},{"oldId":"b-fb1bb1aa51553f33f1a4f36d","targetId":"b-fb1bb1aa51553f33f1a4f36d","page":"reading-block-f7555aca3cff49f492d243beb43d1bf6","operation":"move","beforeSHA256":"753942ac9ea72b31d30c84cbe74029829e59d1719a4da85742d6b2c70eb963b4","afterSHA256":"753942ac9ea72b31d30c84cbe74029829e59d1719a4da85742d6b2c70eb963b4","reason":"Same source body in authored semantic page"},{"oldId":"b-643f6075d56c35c31b4929fe","targetId":"b-643f6075d56c35c31b4929fe","page":"reading-block-f7555aca3cff49f492d243beb43d1bf6","operation":"move","beforeSHA256":"42cabe6994680b53287da6245e3f0848e31c397c51a1e603a17a7b92beb42962","afterSHA256":"42cabe6994680b53287da6245e3f0848e31c397c51a1e603a17a7b92beb42962","reason":"Same source body in authored semantic page"},{"oldId":"b-da2d249c26bdf4f16abef717","targetId":"b-da2d249c26bdf4f16abef717","page":"reading-block-f7555aca3cff49f492d243beb43d1bf6","operation":"move","beforeSHA256":"79d395dacb4b5aa9e61ba1cfd4f2346b143578b0615df7f34834b5296f871c91","afterSHA256":"79d395dacb4b5aa9e61ba1cfd4f2346b143578b0615df7f34834b5296f871c91","reason":"Same source body in authored semantic page"},{"oldId":"b-1d940322ad6ba126cbb5c643","targetId":"b-1d940322ad6ba126cbb5c643","page":"reading-block-f7555aca3cff49f492d243beb43d1bf6","operation":"move","beforeSHA256":"1cbb6c7f4f25501bf57adf425d88f283db4606e507ed82c508475aa5bdc67df6","afterSHA256":"1cbb6c7f4f25501bf57adf425d88f283db4606e507ed82c508475aa5bdc67df6","reason":"Same source body in authored semantic page"},{"oldId":"b-0a3bacc4598f7497ce42a853","targetId":"b-0a3bacc4598f7497ce42a853","page":"reading-block-f7555aca3cff49f492d243beb43d1bf6","operation":"move","beforeSHA256":"cef23c50f292bcb2bca2815e5a828b3c6bc7aa17d2f02cb7bf45614e2ce86454","afterSHA256":"cef23c50f292bcb2bca2815e5a828b3c6bc7aa17d2f02cb7bf45614e2ce86454","reason":"Same source body in authored semantic page"},{"oldId":"b-7f7a8dab00b8fad81f5bdd6a","targetId":"b-7f7a8dab00b8fad81f5bdd6a","page":"reading-block-f7555aca3cff49f492d243beb43d1bf6","operation":"move","beforeSHA256":"fd7dacba87183ce44a34a20bde3f9f02f7f919850e060c6febd9cfde7935a1ef","afterSHA256":"fd7dacba87183ce44a34a20bde3f9f02f7f919850e060c6febd9cfde7935a1ef","reason":"Same source body in authored semantic page"},{"oldId":"b-7d4cb07c9a2f416e226947aa","targetId":"b-7d4cb07c9a2f416e226947aa","page":"reading-block-5dca5700ee4c417787a944e984502529","operation":"move","beforeSHA256":"66b2dace11fe3e1e1ac14aa4baad3bd70f35d66db29be4adb83a0909a35dad07","afterSHA256":"66b2dace11fe3e1e1ac14aa4baad3bd70f35d66db29be4adb83a0909a35dad07","reason":"Same source body in authored semantic page"},{"oldId":"b-0cb682a68c331ab373736858","targetId":"b-0cb682a68c331ab373736858","page":"reading-block-5dca5700ee4c417787a944e984502529","operation":"move","beforeSHA256":"91f9e054a47e5dff5f5eff0c1755d5af1d8d8a7561e448bf00f969d4963853e4","afterSHA256":"91f9e054a47e5dff5f5eff0c1755d5af1d8d8a7561e448bf00f969d4963853e4","reason":"Same source body in authored semantic page"},{"oldId":"b-d199e20593203b2eaf01ed8a","targetId":"b-d199e20593203b2eaf01ed8a","page":"reading-block-5dca5700ee4c417787a944e984502529","operation":"move","beforeSHA256":"a1d2c567d541da19ed6e84487f8acfe7004876cf28049f9f3f85db6ce856e984","afterSHA256":"a1d2c567d541da19ed6e84487f8acfe7004876cf28049f9f3f85db6ce856e984","reason":"Same source body in authored semantic page"},{"oldId":"b-69fb5985759e68b0d94547af","targetId":"b-69fb5985759e68b0d94547af","page":"reading-block-5dca5700ee4c417787a944e984502529","operation":"move","beforeSHA256":"1ed9fc03f7594779444088061d207504839c02ffaa69fda6d84d515f0192db5e","afterSHA256":"1ed9fc03f7594779444088061d207504839c02ffaa69fda6d84d515f0192db5e","reason":"Same source body in authored semantic page"},{"oldId":"b-c729675790ace5e7ea6abae8","targetId":"b-c729675790ace5e7ea6abae8","page":"reading-block-5dca5700ee4c417787a944e984502529","operation":"move","beforeSHA256":"346501991ef9efbc297a06f21a942a08d19a2941f1cd91dc0ee0f9ad87b2aec4","afterSHA256":"346501991ef9efbc297a06f21a942a08d19a2941f1cd91dc0ee0f9ad87b2aec4","reason":"Same source body in authored semantic page"},{"oldId":"b-10b60d1a51713756f78b8f9c","targetId":"b-10b60d1a51713756f78b8f9c","page":"reading-block-5dca5700ee4c417787a944e984502529","operation":"move","beforeSHA256":"494314c1f26aeeda8950fd4f9fa42aac5d81da1b3288f0e066ba185a85e42cfa","afterSHA256":"494314c1f26aeeda8950fd4f9fa42aac5d81da1b3288f0e066ba185a85e42cfa","reason":"Same source body in authored semantic page"},{"oldId":"b-8ae9a7adff920dc85598fd63","targetId":"b-8ae9a7adff920dc85598fd63","page":"reading-block-7653447b25ea4f148d99cc7e004b290b","operation":"move","beforeSHA256":"1fcf124d95292a66d0028aa8136225908b256c15dbd114cbfd1c90d2580c2076","afterSHA256":"1fcf124d95292a66d0028aa8136225908b256c15dbd114cbfd1c90d2580c2076","reason":"Same source body in authored semantic page"},{"oldId":"b-f353272ee4c9ea136e48b038","targetId":"b-f353272ee4c9ea136e48b038","page":"reading-block-7653447b25ea4f148d99cc7e004b290b","operation":"rewrite","beforeSHA256":"e665bdc8aa4b0203a7733b2bd0ea79a87b7389e50193d757313c0a22bb1169ad","afterSHA256":"8c1ead9c29c2954f6f3b19afcb9b79b9e0c0f6d06c2c0e0d2373f654ba32b840","reason":"Same source identity with figure semantics or reading context enriched"},{"oldId":"b-54b9e7c709fb5cff9fad91fb","targetId":"b-54b9e7c709fb5cff9fad91fb","page":"reading-block-7653447b25ea4f148d99cc7e004b290b","operation":"rewrite","beforeSHA256":"ec00088ad5db52899dd7efa5cd812ef2d1332e82a81ad4fd2c281044b6ed1373","afterSHA256":"f1cef90e363d220c1f87c8beae336e8029c68d18c99137d392955eb3b65a79a3","reason":"Same source identity with figure semantics or reading context enriched"},{"oldId":"b-515309f84cb4586d2e33faac","targetId":"b-515309f84cb4586d2e33faac","page":"reading-block-7653447b25ea4f148d99cc7e004b290b","operation":"move","beforeSHA256":"a482ae56cdefd9f6d63fc1937e9b4c285b7d967c3f106b66cf16b4ed29cf668b","afterSHA256":"52aa942b7ce525a5978e0c28331b8fc71484207ba7932fb99596d25a42856208","reason":"Same source body in authored semantic 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05](https://deepnote.com/project/38755e4e-23f6-4f5f-8c68-8a2e4114b1c1/notebook/de39b998077f44cca4b8407085547766)，不作为本册前置。","sha256":"53664b678211e118eca6cdb1b5f1ccda4bd021cefe38a6db4910fa6295ffd20d","chapter":{"id":"I","title":"学习信号与表示"},"unit":{"id":"i-masked-prediction","title":"限制可见信息，怎样构成可验证的重建任务？"},"page":{"id":"reading-opening","title":"LAB 08 · 图像重建：遮挡损失与表示评价","number":1},"sectionTitle":"LAB 08 · 图像重建：遮挡损失与表示评价","currentQuestion":"LAB 08 · 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索引热身仍然有用，但不能叫学到了语义","sha256":"c7ddd926153f66a12ff50445e4f0c91d5b1b3bdd966cdc956f2f98dfc816e780","chapter":{"id":"I","title":"学习信号与表示"},"unit":{"id":"i-masked-prediction","title":"限制可见信息，怎样构成可验证的重建任务？"},"page":{"id":"reading-block-44a741e9a3334aa4b16f140a98179497","title":"索引热身仍然有用，但不能叫学到了语义","number":2},"sectionTitle":"索引热身仍然有用，但不能叫学到了语义","currentQuestion":"索引热身仍然有用，但不能叫学到了语义","sourceSlides":[],"knowledgeBlock":null,"formulas":[],"previousBlock":null,"nextBlock":"b-04258a1d2b81b52b597d10f2","prerequisites":[{"id":"b-04258a1d2b81b52b597d10f2","url":"#b-04258a1d2b81b52b597d10f2","role":"problem-context"}],"relatedBlocks":[]},{"id":"b-04258a1d2b81b52b597d10f2","type":"p","scope":"block-44a741e9a3334aa4b16f140a98179497","url":"https://codingai-lec03.pages.dev/course/notebooks/b5ba27f2c5ba4ec0922208a7d5dc80a4.html#b-04258a1d2b81b52b597d10f2","content":"先保留旧版0到15序列的可见均值填充。4个可见值时隐藏MSE为19.9167，8个可见值时为21.25；可见数更多却不一定降低这个均值代理，因为选中的位置和值都改变了，没有模型学习。","sha256":"87299f7751b6bc58e001a1cac6a5323dd0001ad6d4f42408db1fc6f2d370de31","chapter":{"id":"I","title":"学习信号与表示"},"unit":{"id":"i-masked-prediction","title":"限制可见信息，怎样构成可验证的重建任务？"},"page":{"id":"reading-block-44a741e9a3334aa4b16f140a98179497","title":"索引热身仍然有用，但不能叫学到了语义","number":2},"sectionTitle":"索引热身仍然有用，但不能叫学到了语义","currentQuestion":"索引热身仍然有用，但不能叫学到了语义","sourceSlides":[],"knowledgeBlock":null,"formulas":[],"previousBlock":"b-b4042a02d301d2a192c865d6","nextBlock":"b-bab1eb3ab23026199996fda2","prerequisites":[{"id":"b-b4042a02d301d2a192c865d6","url":"#b-b4042a02d301d2a192c865d6","role":"section-title"}],"relatedBlocks":[]},{"id":"b-bab1eb3ab23026199996fda2","type":"p","scope":"block-44a741e9a3334aa4b16f140a98179497","url":"https://codingai-lec03.pages.dev/course/notebooks/b5ba27f2c5ba4ec0922208a7d5dc80a4.html#b-bab1eb3ab23026199996fda2","content":"接着随机打乱16个索引，保留4个，其余放mask标记−1；用逆排列恢复原位置。验证恢复后可见值位于正确坐标。这个断言证明的是位置对应，不是重建质量。","sha256":"9d9abf237d90a9561e1846c380f3fcdce763427c83b474ae06bfeb3ad755dcfb","chapter":{"id":"I","title":"学习信号与表示"},"unit":{"id":"i-masked-prediction","title":"限制可见信息，怎样构成可验证的重建任务？"},"page":{"id":"reading-block-44a741e9a3334aa4b16f140a98179497","title":"索引热身仍然有用，但不能叫学到了语义","number":2},"sectionTitle":"索引热身仍然有用，但不能叫学到了语义","currentQuestion":"索引热身仍然有用，但不能叫学到了语义","sourceSlides":[],"knowledgeBlock":null,"formulas":[],"previousBlock":"b-04258a1d2b81b52b597d10f2","nextBlock":"b-1af15ccdc4aac1a3725ba738","prerequisites":[{"id":"b-b4042a02d301d2a192c865d6","url":"#b-b4042a02d301d2a192c865d6","role":"section-title"},{"id":"b-04258a1d2b81b52b597d10f2","url":"#b-04258a1d2b81b52b597d10f2","role":"problem-context"}],"relatedBlocks":[]},{"id":"b-1af15ccdc4aac1a3725ba738","type":"pre","scope":"block-70b5b57303494570a973d1cf27053299","url":"https://codingai-lec03.pages.dev/course/notebooks/b5ba27f2c5ba4ec0922208a7d5dc80a4.html#b-1af15ccdc4aac1a3725ba738","content":"```\nimport os, tempfile\nLAB_WORKDIR = tempfile.mkdtemp(prefix='lecture03-')\nos.chdir(LAB_WORKDIR)\nprint('Ephemeral experiment workspace ready')\nimport numpy as np\nx=np.arange(16,dtype=float).reshape(4,4)\nfor visible in ([0,3,9,14],[0,5,10,15,3,6,9,12]):\n mask=np.ones(16,bool); mask[list(visible)]=False\n pred=np.full(16,x.ravel()[list(visible)].mean())\n mse=((pred[mask]-x.ravel()[mask])**2).mean()\n print('visible',len(visible),'masked',mask.sum(),'masked MSE',round(float(mse),3))\nrng=np.random.default_rng(7);ids_shuffle=rng.permutation(16)\nids_restore=np.argsort(ids_shuffle);visible=ids_shuffle[:4]\nshuffled=np.concatenate([x.ravel()[visible],np.full(12,-1.)])\nrestored=shuffled[ids_restore]\nassert np.array_equal(restored[visible],x.ravel()[visible])\nprint('shuffle',ids_shuffle,'restore',ids_restore,'restored',restored)\n```","sha256":"a5e8ed9d3fedff981b54ed3173e41c010f861683fde366e467627a295d351e8f","chapter":{"id":"I","title":"学习信号与表示"},"unit":{"id":"i-masked-prediction","title":"限制可见信息，怎样构成可验证的重建任务？"},"page":{"id":"reading-block-44a741e9a3334aa4b16f140a98179497","title":"索引热身仍然有用，但不能叫学到了语义","number":2},"sectionTitle":"索引热身仍然有用，但不能叫学到了语义","currentQuestion":"索引热身仍然有用，但不能叫学到了语义","sourceSlides":[],"knowledgeBlock":null,"formulas":[],"previousBlock":"b-bab1eb3ab23026199996fda2","nextBlock":null,"prerequisites":[{"id":"b-b4042a02d301d2a192c865d6","url":"#b-b4042a02d301d2a192c865d6","role":"section-title"},{"id":"b-04258a1d2b81b52b597d10f2","url":"#b-04258a1d2b81b52b597d10f2","role":"problem-context"}],"relatedBlocks":[]},{"id":"b-991deecf2ba93db8af7e8037","type":"h2","scope":"block-8430582e1f5e4998a68580017714e408","url":"https://codingai-lec03.pages.dev/course/notebooks/b5ba27f2c5ba4ec0922208a7d5dc80a4.html#b-991deecf2ba93db8af7e8037","content":"## 运行准备与结果身份","sha256":"9bbc7527a59bb35d5da5d51d5f5bc342082de91f94066bfa434ed27e431f230a","chapter":{"id":"I","title":"学习信号与表示"},"unit":{"id":"i-masked-prediction","title":"限制可见信息，怎样构成可验证的重建任务？"},"page":{"id":"reading-block-8430582e1f5e4998a68580017714e408","title":"运行准备与结果身份","number":3},"sectionTitle":"运行准备与结果身份","currentQuestion":"运行准备与结果身份","sourceSlides":[],"knowledgeBlock":null,"formulas":[],"previousBlock":null,"nextBlock":"b-23022adb8e0454d12772c269","prerequisites":[{"id":"b-23022adb8e0454d12772c269","url":"#b-23022adb8e0454d12772c269","role":"problem-context"}],"relatedBlocks":[]},{"id":"b-23022adb8e0454d12772c269","type":"p","scope":"block-8430582e1f5e4998a68580017714e408","url":"https://codingai-lec03.pages.dev/course/notebooks/b5ba27f2c5ba4ec0922208a7d5dc80a4.html#b-23022adb8e0454d12772c269","content":"本册包含全部生成数据、模型、损失、优化与评价代码。按顺序在**新内核**运行；不读另一册的变量，不需要下载图像或预训练权重。依赖 Python 3.12、NumPy、matplotlib、scikit-learn 和 CPU PyTorch。本次实际版本见输出 JSON；若环境缺少 PyTorch，先运行 `python -m pip install torch==2.14.0 --index-url https://download.pytorch.org/whl/cpu`，再启动内核。NumPy、matplotlib、scikit-learn 可用 `python -m pip install numpy matplotlib scikit-learn` 安装。","sha256":"7c7112f4f7442896ffc602e4bca9c95cb6afe323f539ecace8877e95dc68b391","chapter":{"id":"I","title":"学习信号与表示"},"unit":{"id":"i-masked-prediction","title":"限制可见信息，怎样构成可验证的重建任务？"},"page":{"id":"reading-block-8430582e1f5e4998a68580017714e408","title":"运行准备与结果身份","number":3},"sectionTitle":"运行准备与结果身份","currentQuestion":"运行准备与结果身份","sourceSlides":[],"knowledgeBlock":null,"formulas":[],"previousBlock":"b-991deecf2ba93db8af7e8037","nextBlock":"b-d5a62b6b0c9452a3f06eb767","prerequisites":[{"id":"b-991deecf2ba93db8af7e8037","url":"#b-991deecf2ba93db8af7e8037","role":"section-title"}],"relatedBlocks":[]},{"id":"b-d5a62b6b0c9452a3f06eb767","type":"p","scope":"block-8430582e1f5e4998a68580017714e408","url":"https://codingai-lec03.pages.dev/course/notebooks/b5ba27f2c5ba4ec0922208a7d5dc80a4.html#b-d5a62b6b0c9452a3f06eb767","content":"下方表格和静态图来自 **2026-09-22 本地 CPU 完整训练**；它们不是原来的数值热身输出，也不代表 Deepnote 云端已重跑。运行本册会另建 `lab08-notebook-output`，保存 inputs.npz、config.json、权重、图与 results.json。CPU 时间受机器负载影响；本次每册约 10–25 秒，不能当作跨设备速度基准。单种子、小任务的结果用来检验路径和对照，不作方法排名。","sha256":"45cb652f6dde94cb2e85f0a141910fa3060c64947b747c6458e059c5d93f374c","chapter":{"id":"I","title":"学习信号与表示"},"unit":{"id":"i-masked-prediction","title":"限制可见信息，怎样构成可验证的重建任务？"},"page":{"id":"reading-block-8430582e1f5e4998a68580017714e408","title":"运行准备与结果身份","number":3},"sectionTitle":"运行准备与结果身份","currentQuestion":"运行准备与结果身份","sourceSlides":[],"knowledgeBlock":null,"formulas":[],"previousBlock":"b-23022adb8e0454d12772c269","nextBlock":"b-ca29cc71ee4377418ef0a2b5","prerequisites":[{"id":"b-991deecf2ba93db8af7e8037","url":"#b-991deecf2ba93db8af7e8037","role":"section-title"},{"id":"b-23022adb8e0454d12772c269","url":"#b-23022adb8e0454d12772c269","role":"problem-context"}],"relatedBlocks":[]},{"id":"b-ca29cc71ee4377418ef0a2b5","type":"pre","scope":"block-4bb149c8de264021b5927384daedb5f7","url":"https://codingai-lec03.pages.dev/course/notebooks/b5ba27f2c5ba4ec0922208a7d5dc80a4.html#b-ca29cc71ee4377418ef0a2b5","content":"```\n\"\"\"Actual denoising and masked convolutional AE on held-out digit images; not original ViT MAE.\"\"\"\nfrom pathlib import Path\nimport copy, hashlib, json, platform, time\nimport numpy as np\nimport torch\nfrom torch import nn\nfrom torch.nn import functional as F\nfrom sklearn.datasets import load_digits\nfrom sklearn.model_selection import train_test_split\nfrom sklearn.neighbors import KNeighborsClassifier\nimport matplotlib\nmatplotlib.use('Agg')\nimport matplotlib.pyplot as plt\n\ntorch.set_num_threads(2)\nSEED=20260922\n\nSOURCE_SHA256='9f7f2ed888f5ea118ad1fc622ff64e1aa1e53d5ae162698fcbfb005f45201825'\nprint(\"source\",SOURCE_SHA256,\"torch\",torch.__version__)\n```","sha256":"d054471d9556f974200f75070e378e4dfc31a7a89323ebc8ceddb672b594ed38","chapter":{"id":"I","title":"学习信号与表示"},"unit":{"id":"i-masked-prediction","title":"限制可见信息，怎样构成可验证的重建任务？"},"page":{"id":"reading-block-8430582e1f5e4998a68580017714e408","title":"运行准备与结果身份","number":3},"sectionTitle":"运行准备与结果身份","currentQuestion":"运行准备与结果身份","sourceSlides":[],"knowledgeBlock":null,"formulas":[],"previousBlock":"b-d5a62b6b0c9452a3f06eb767","nextBlock":null,"prerequisites":[{"id":"b-991deecf2ba93db8af7e8037","url":"#b-991deecf2ba93db8af7e8037","role":"section-title"},{"id":"b-23022adb8e0454d12772c269","url":"#b-23022adb8e0454d12772c269","role":"problem-context"}],"relatedBlocks":[]},{"id":"b-85ab5341d597235675d7126b","type":"h2","scope":"block-32b1e5f410384ab9a6ae63440ff4f425","url":"https://codingai-lec03.pages.dev/course/notebooks/b5ba27f2c5ba4ec0922208a7d5dc80a4.html#b-85ab5341d597235675d7126b","content":"## 数据、破坏与瓶颈","sha256":"ffd82c5a863f2930d5cea61e508a508c1126c1a93e8bd026430f0daa4e1aa161","chapter":{"id":"I","title":"学习信号与表示"},"unit":{"id":"i-masked-prediction","title":"限制可见信息，怎样构成可验证的重建任务？"},"page":{"id":"reading-block-32b1e5f410384ab9a6ae63440ff4f425","title":"数据、破坏与瓶颈","number":4},"sectionTitle":"数据、破坏与瓶颈","currentQuestion":"数据、破坏与瓶颈","sourceSlides":[],"knowledgeBlock":null,"formulas":[],"previousBlock":null,"nextBlock":"b-33cb1bdd26be6963673b5f3a","prerequisites":[{"id":"b-33cb1bdd26be6963673b5f3a","url":"#b-33cb1bdd26be6963673b5f3a","role":"problem-context"}],"relatedBlocks":[]},{"id":"b-33cb1bdd26be6963673b5f3a","type":"p","scope":"block-32b1e5f410384ab9a6ae63440ff4f425","url":"https://codingai-lec03.pages.dev/course/notebooks/b5ba27f2c5ba4ec0922208a7d5dc80a4.html#b-33cb1bdd26be6963673b5f3a","content":"按数字标签分层分出1257张训练图和540张保留图，之后才产生破坏。标签只用于分层和最终近邻评价，AE优化只读取像素。像素原值0–16除以16，统一到[0,1]；这里不是MNIST的28×28输入。","sha256":"b5b552b03d3348b7ffa5e79b679ae61d1637f36cb0126bd432501592ccf384e4","chapter":{"id":"I","title":"学习信号与表示"},"unit":{"id":"i-masked-prediction","title":"限制可见信息，怎样构成可验证的重建任务？"},"page":{"id":"reading-block-32b1e5f410384ab9a6ae63440ff4f425","title":"数据、破坏与瓶颈","number":4},"sectionTitle":"数据、破坏与瓶颈","currentQuestion":"数据、破坏与瓶颈","sourceSlides":[],"knowledgeBlock":null,"formulas":[],"previousBlock":"b-85ab5341d597235675d7126b","nextBlock":"b-24f0ed58d7093486c0b77b97","prerequisites":[{"id":"b-85ab5341d597235675d7126b","url":"#b-85ab5341d597235675d7126b","role":"section-title"}],"relatedBlocks":[]},{"id":"b-24f0ed58d7093486c0b77b97","type":"p","scope":"block-32b1e5f410384ab9a6ae63440ff4f425","url":"https://codingai-lec03.pages.dev/course/notebooks/b5ba27f2c5ba4ec0922208a7d5dc80a4.html#b-24f0ed58d7093486c0b77b97","content":"去噪输入按25%概率把像素置0，再叠加标准差0.15高斯噪声并裁剪到[0,1]。目标仍是原始清洁图像。两层stride2卷积将8×8压到16×2×2，线性层得到16维瓶颈；解码器从瓶颈扩展，再用转置卷积回到1×8×8，经sigmoid输出。","sha256":"dddc3aa4792ce564399c5a9042008ef6961c269f1be67e70b99cb08f9ee8143d","chapter":{"id":"I","title":"学习信号与表示"},"unit":{"id":"i-masked-prediction","title":"限制可见信息，怎样构成可验证的重建任务？"},"page":{"id":"reading-block-32b1e5f410384ab9a6ae63440ff4f425","title":"数据、破坏与瓶颈","number":4},"sectionTitle":"数据、破坏与瓶颈","currentQuestion":"数据、破坏与瓶颈","sourceSlides":[],"knowledgeBlock":null,"formulas":[],"previousBlock":"b-33cb1bdd26be6963673b5f3a","nextBlock":"b-4dc20d6e2e3fe21e5c076420","prerequisites":[{"id":"b-85ab5341d597235675d7126b","url":"#b-85ab5341d597235675d7126b","role":"section-title"},{"id":"b-33cb1bdd26be6963673b5f3a","url":"#b-33cb1bdd26be6963673b5f3a","role":"problem-context"}],"relatedBlocks":[]},{"id":"b-4dc20d6e2e3fe21e5c076420","type":"p","scope":"block-32b1e5f410384ab9a6ae63440ff4f425","url":"https://codingai-lec03.pages.dev/course/notebooks/b5ba27f2c5ba4ec0922208a7d5dc80a4.html#b-4dc20d6e2e3fe21e5c076420","content":"遮挡实验把8×8图像切为16个2×2patch。训练每图恰好隐藏8个patch，输入为两个通道：可见像素和二值mask。mask=1代表隐藏，能够区分“真黑像素”与“未知像素”。隐藏目标只出现在损失端，不能被送入编码器。","sha256":"e66f545513713358d5ac1cd3ddcc275038454a49c79c2353a84651c3442edbdd","chapter":{"id":"I","title":"学习信号与表示"},"unit":{"id":"i-masked-prediction","title":"限制可见信息，怎样构成可验证的重建任务？"},"page":{"id":"reading-block-32b1e5f410384ab9a6ae63440ff4f425","title":"数据、破坏与瓶颈","number":4},"sectionTitle":"数据、破坏与瓶颈","currentQuestion":"数据、破坏与瓶颈","sourceSlides":[],"knowledgeBlock":null,"formulas":[],"previousBlock":"b-24f0ed58d7093486c0b77b97","nextBlock":"b-689691a7ffdc6c0e50f59abc","prerequisites":[{"id":"b-85ab5341d597235675d7126b","url":"#b-85ab5341d597235675d7126b","role":"section-title"},{"id":"b-33cb1bdd26be6963673b5f3a","url":"#b-33cb1bdd26be6963673b5f3a","role":"problem-context"}],"relatedBlocks":[]},{"id":"b-689691a7ffdc6c0e50f59abc","type":"pre","scope":"block-18c1d267b0f04e81bb9b3759cd9fafa6","url":"https://codingai-lec03.pages.dev/course/notebooks/b5ba27f2c5ba4ec0922208a7d5dc80a4.html#b-689691a7ffdc6c0e50f59abc","content":"```\ndef digit_data():\n    data=load_digits(); x=torch.tensor(data.images/16,dtype=torch.float32)[:,None]\n    train,test=train_test_split(np.arange(len(x)),test_size=.3,random_state=SEED,stratify=data.target)\n    assert set(train).isdisjoint(test)\n    return x,data.target,train,test\n\ndef noise_corrupt(x):\n    return (x*(torch.rand_like(x)>.25)+torch.randn_like(x)*.15).clamp(0,1)\n\ndef patch_mask(n,ratio=.5):\n    # Grid is 4x4 patches, each 2x2 pixels. 1 means HIDDEN.\n    order=torch.rand(n,16).argsort(dim=1)\n    keep=int(16*(1-ratio))\n    patch=torch.ones(n,16);patch.scatter_(1,order[:,:keep],0)\n    return patch.reshape(n,1,4,4).repeat_interleave(2,2).repeat_interleave(2,3)\n\nclass ConvAE(nn.Module):\n    def __init__(self,channels=1):\n        super().__init__()\n        self.encoder=nn.Sequential(nn.Conv2d(channels,8,3,stride=2,padding=1),nn.ReLU(),\n          nn.Conv2d(8,16,3,stride=2,padding=1),nn.ReLU(),nn.Flatten(),nn.Linear(64,16))\n        self.expand=nn.Linear(16,64)\n        self.decoder=nn.Sequential(nn.ConvTranspose2d(16,8,4,stride=2,padding=1),nn.ReLU(),\n          nn.ConvTranspose2d(8,1,4,stride=2,padding=1),nn.Sigmoid())\n    def forward(self,x):\n        h=self.encoder(x);return self.decoder(self.expand(h).reshape(-1,16,2,2)),h\n```","sha256":"e69117b96838ca54dc91ac1267742da52d685277ae4d6dc3dbbec80d69fece9c","chapter":{"id":"I","title":"学习信号与表示"},"unit":{"id":"i-masked-prediction","title":"限制可见信息，怎样构成可验证的重建任务？"},"page":{"id":"reading-block-32b1e5f410384ab9a6ae63440ff4f425","title":"数据、破坏与瓶颈","number":4},"sectionTitle":"数据、破坏与瓶颈","currentQuestion":"数据、破坏与瓶颈","sourceSlides":[],"knowledgeBlock":null,"formulas":[],"previousBlock":"b-4dc20d6e2e3fe21e5c076420","nextBlock":null,"prerequisites":[{"id":"b-85ab5341d597235675d7126b","url":"#b-85ab5341d597235675d7126b","role":"section-title"},{"id":"b-33cb1bdd26be6963673b5f3a","url":"#b-33cb1bdd26be6963673b5f3a","role":"problem-context"}],"relatedBlocks":[]},{"id":"b-746882bf55072c50fe67278e","type":"h2","scope":"block-f7555aca3cff49f492d243beb43d1bf6","url":"https://codingai-lec03.pages.dev/course/notebooks/b5ba27f2c5ba4ec0922208a7d5dc80a4.html#b-746882bf55072c50fe67278e","content":"## 两个目标，两个分母","sha256":"1e535ce05cfe5eb3201aafe7ef126832c60a9410ac57616575ebfde505c66446","chapter":{"id":"I","title":"学习信号与表示"},"unit":{"id":"i-masked-prediction","title":"限制可见信息，怎样构成可验证的重建任务？"},"page":{"id":"reading-block-f7555aca3cff49f492d243beb43d1bf6","title":"两个目标，两个分母","number":5},"sectionTitle":"两个目标，两个分母","currentQuestion":"两个目标，两个分母","sourceSlides":[],"knowledgeBlock":null,"formulas":[],"previousBlock":null,"nextBlock":"b-8c82cc249dc215427d69bcf7","prerequisites":[{"id":"b-8c82cc249dc215427d69bcf7","url":"#b-8c82cc249dc215427d69bcf7","role":"problem-context"}],"relatedBlocks":[]},{"id":"b-8c82cc249dc215427d69bcf7","type":"p","scope":"block-f7555aca3cff49f492d243beb43d1bf6","url":"https://codingai-lec03.pages.dev/course/notebooks/b5ba27f2c5ba4ec0922208a7d5dc80a4.html#b-8c82cc249dc215427d69bcf7","content":"去噪的损失对所有像素平均：","sha256":"9060158f67fb888087b1cfe7c5d2615563c150a54cca1810d4c56bc38e373a25","chapter":{"id":"I","title":"学习信号与表示"},"unit":{"id":"i-masked-prediction","title":"限制可见信息，怎样构成可验证的重建任务？"},"page":{"id":"reading-block-f7555aca3cff49f492d243beb43d1bf6","title":"两个目标，两个分母","number":5},"sectionTitle":"两个目标，两个分母","currentQuestion":"两个目标，两个分母","sourceSlides":[],"knowledgeBlock":null,"formulas":[],"previousBlock":"b-746882bf55072c50fe67278e","nextBlock":"b-fb1bb1aa51553f33f1a4f36d","prerequisites":[{"id":"b-746882bf55072c50fe67278e","url":"#b-746882bf55072c50fe67278e","role":"section-title"}],"relatedBlocks":[]},{"id":"b-fb1bb1aa51553f33f1a4f36d","type":"p","scope":"block-f7555aca3cff49f492d243beb43d1bf6","url":"https://codingai-lec03.pages.dev/course/notebooks/b5ba27f2c5ba4ec0922208a7d5dc80a4.html#b-fb1bb1aa51553f33f1a4f36d","content":"$L_{DAE}=\\frac1{BHW}\\sum_{b,p}(\\hat x_{bp}-x_{bp})^2.$","sha256":"753942ac9ea72b31d30c84cbe74029829e59d1719a4da85742d6b2c70eb963b4","chapter":{"id":"I","title":"学习信号与表示"},"unit":{"id":"i-masked-prediction","title":"限制可见信息，怎样构成可验证的重建任务？"},"page":{"id":"reading-block-f7555aca3cff49f492d243beb43d1bf6","title":"两个目标，两个分母","number":5},"sectionTitle":"两个目标，两个分母","currentQuestion":"两个目标，两个分母","sourceSlides":[],"knowledgeBlock":null,"formulas":[{"latex":"L_{DAE}=\\frac1{BHW}\\sum_{b,p}(\\hat x_{bp}-x_{bp})^2.","definitionContext":"两个目标，两个分母"}],"previousBlock":"b-8c82cc249dc215427d69bcf7","nextBlock":"b-643f6075d56c35c31b4929fe","prerequisites":[{"id":"b-746882bf55072c50fe67278e","url":"#b-746882bf55072c50fe67278e","role":"section-title"},{"id":"b-8c82cc249dc215427d69bcf7","url":"#b-8c82cc249dc215427d69bcf7","role":"problem-context"}],"relatedBlocks":[]},{"id":"b-643f6075d56c35c31b4929fe","type":"p","scope":"block-f7555aca3cff49f492d243beb43d1bf6","url":"https://codingai-lec03.pages.dev/course/notebooks/b5ba27f2c5ba4ec0922208a7d5dc80a4.html#b-643f6075d56c35c31b4929fe","content":"遮挡模型只在隐藏集合M上平均：","sha256":"42cabe6994680b53287da6245e3f0848e31c397c51a1e603a17a7b92beb42962","chapter":{"id":"I","title":"学习信号与表示"},"unit":{"id":"i-masked-prediction","title":"限制可见信息，怎样构成可验证的重建任务？"},"page":{"id":"reading-block-f7555aca3cff49f492d243beb43d1bf6","title":"两个目标，两个分母","number":5},"sectionTitle":"两个目标，两个分母","currentQuestion":"两个目标，两个分母","sourceSlides":[],"knowledgeBlock":null,"formulas":[],"previousBlock":"b-fb1bb1aa51553f33f1a4f36d","nextBlock":"b-da2d249c26bdf4f16abef717","prerequisites":[{"id":"b-746882bf55072c50fe67278e","url":"#b-746882bf55072c50fe67278e","role":"section-title"},{"id":"b-8c82cc249dc215427d69bcf7","url":"#b-8c82cc249dc215427d69bcf7","role":"problem-context"}],"relatedBlocks":[]},{"id":"b-da2d249c26bdf4f16abef717","type":"p","scope":"block-f7555aca3cff49f492d243beb43d1bf6","url":"https://codingai-lec03.pages.dev/course/notebooks/b5ba27f2c5ba4ec0922208a7d5dc80a4.html#b-da2d249c26bdf4f16abef717","content":"$L_{mask}=\\frac{\\sum_{b,p}M_{bp}(\\hat x_{bp}-x_{bp})^2}{\\sum_{b,p}M_{bp}}.$","sha256":"79d395dacb4b5aa9e61ba1cfd4f2346b143578b0615df7f34834b5296f871c91","chapter":{"id":"I","title":"学习信号与表示"},"unit":{"id":"i-masked-prediction","title":"限制可见信息，怎样构成可验证的重建任务？"},"page":{"id":"reading-block-f7555aca3cff49f492d243beb43d1bf6","title":"两个目标，两个分母","number":5},"sectionTitle":"两个目标，两个分母","currentQuestion":"两个目标，两个分母","sourceSlides":[],"knowledgeBlock":null,"formulas":[{"latex":"L_{mask}=\\frac{\\sum_{b,p}M_{bp}(\\hat x_{bp}-x_{bp})^2}{\\sum_{b,p}M_{bp}}.","definitionContext":"两个目标，两个分母"}],"previousBlock":"b-643f6075d56c35c31b4929fe","nextBlock":"b-1d940322ad6ba126cbb5c643","prerequisites":[{"id":"b-746882bf55072c50fe67278e","url":"#b-746882bf55072c50fe67278e","role":"section-title"},{"id":"b-8c82cc249dc215427d69bcf7","url":"#b-8c82cc249dc215427d69bcf7","role":"problem-context"}],"relatedBlocks":[]},{"id":"b-1d940322ad6ba126cbb5c643","type":"p","scope":"block-f7555aca3cff49f492d243beb43d1bf6","url":"https://codingai-lec03.pages.dev/course/notebooks/b5ba27f2c5ba4ec0922208a7d5dc80a4.html#b-1d940322ad6ba126cbb5c643","content":"如果对整图算MSE，再把大量可见真值复制到输出，会人为改善分母。这也是重建面板与指标要分开说明的原因：图上为方便阅读将已知像素原样保留，数值只评价隐藏部分。","sha256":"1cbb6c7f4f25501bf57adf425d88f283db4606e507ed82c508475aa5bdc67df6","chapter":{"id":"I","title":"学习信号与表示"},"unit":{"id":"i-masked-prediction","title":"限制可见信息，怎样构成可验证的重建任务？"},"page":{"id":"reading-block-f7555aca3cff49f492d243beb43d1bf6","title":"两个目标，两个分母","number":5},"sectionTitle":"两个目标，两个分母","currentQuestion":"两个目标，两个分母","sourceSlides":[],"knowledgeBlock":null,"formulas":[],"previousBlock":"b-da2d249c26bdf4f16abef717","nextBlock":"b-0a3bacc4598f7497ce42a853","prerequisites":[{"id":"b-746882bf55072c50fe67278e","url":"#b-746882bf55072c50fe67278e","role":"section-title"},{"id":"b-8c82cc249dc215427d69bcf7","url":"#b-8c82cc249dc215427d69bcf7","role":"problem-context"}],"relatedBlocks":[]},{"id":"b-0a3bacc4598f7497ce42a853","type":"p","scope":"block-f7555aca3cff49f492d243beb43d1bf6","url":"https://codingai-lec03.pages.dev/course/notebooks/b5ba27f2c5ba4ec0922208a7d5dc80a4.html#b-0a3bacc4598f7497ce42a853","content":"两模型各自独立初始化并保存对应随机权重，Adam学习率0.003；去噪40轮，遮挡50轮。训练循环中每轮重新生成破坏，只有训练源图参与参数更新。","sha256":"cef23c50f292bcb2bca2815e5a828b3c6bc7aa17d2f02cb7bf45614e2ce86454","chapter":{"id":"I","title":"学习信号与表示"},"unit":{"id":"i-masked-prediction","title":"限制可见信息，怎样构成可验证的重建任务？"},"page":{"id":"reading-block-f7555aca3cff49f492d243beb43d1bf6","title":"两个目标，两个分母","number":5},"sectionTitle":"两个目标，两个分母","currentQuestion":"两个目标，两个分母","sourceSlides":[],"knowledgeBlock":null,"formulas":[],"previousBlock":"b-1d940322ad6ba126cbb5c643","nextBlock":"b-7f7a8dab00b8fad81f5bdd6a","prerequisites":[{"id":"b-746882bf55072c50fe67278e","url":"#b-746882bf55072c50fe67278e","role":"section-title"},{"id":"b-8c82cc249dc215427d69bcf7","url":"#b-8c82cc249dc215427d69bcf7","role":"problem-context"}],"relatedBlocks":[]},{"id":"b-7f7a8dab00b8fad81f5bdd6a","type":"pre","scope":"block-e8bd60f8abd5443a9b5b04756ca65676","url":"https://codingai-lec03.pages.dev/course/notebooks/b5ba27f2c5ba4ec0922208a7d5dc80a4.html#b-7f7a8dab00b8fad81f5bdd6a","content":"```\ndef train_ae(x,train,kind,epochs=40):\n    torch.manual_seed(SEED+(kind=='masked'))\n    model=ConvAE(2 if kind=='masked' else 1);initial=copy.deepcopy(model)\n    opt=torch.optim.Adam(model.parameters(),lr=.003);history=[]\n    before=next(model.encoder.parameters()).detach().clone()\n    for epoch in range(epochs):\n        model.train();losses=[]\n        for ids in torch.as_tensor(train)[torch.randperm(len(train))].split(64):\n            clean=x[ids]\n            if kind=='denoise':\n                pred,_=model(noise_corrupt(clean));loss=F.mse_loss(pred,clean)\n            else:\n                mask=patch_mask(len(ids),.5)\n                inp=torch.cat([clean*(1-mask),mask],dim=1)\n                pred,_=model(inp);loss=(((pred-clean)**2)*mask).sum()/mask.sum()\n            opt.zero_grad();loss.backward();opt.step();losses.append(float(loss.detach()))\n        history.append(float(np.mean(losses)))\n    delta=float((next(model.encoder.parameters()).detach()-before).norm());assert delta>0\n    return model,initial,history,delta\n\ndef frozen_probe(model,x,y,train,test,masked=False):\n    model.eval()\n    with torch.no_grad():\n        # Clean image readout; mask channel zero. No reconstruction targets at inference.\n        inp=torch.cat([x,torch.zeros_like(x)],1) if masked else x\n        h=model(inp)[1]\n        a=F.normalize(h,dim=1).numpy()\n    probe=KNeighborsClassifier(n_neighbors=5,metric='cosine').fit(a[train],y[train])\n    return float(probe.score(a[test],y[test]))\n\ndef mask_index_warmup():\n    rng=np.random.default_rng(7);tokens=np.arange(16);shuffle=rng.permutation(16)\n    restore=np.argsort(shuffle);keep=shuffle[:4]\n    shuffled=np.concatenate([tokens[keep],np.full(12,-1)])\n    restored=shuffled[restore]\n    assert np.array_equal(restored[keep],tokens[keep]);assert (restored==-1).sum()==12\n    proxy=[]\n    for visible in ([0,3,9,14],[0,5,10,15,3,6,9,12]):\n        mask=np.ones(16,bool);mask[visible]=False\n        pred=np.full(16,tokens[visible].mean())\n        proxy.append({'visible':len(visible),'masked':int(mask.sum()),'constant_predictor_masked_mse':float(((pred[mask]-tokens[mask])**2).mean())})\n    return {'shuffle':shuffle.tolist(),'ids_restore':restore.tolist(),'restored':restored.tolist(),'constant_proxy':proxy}\n```","sha256":"fd7dacba87183ce44a34a20bde3f9f02f7f919850e060c6febd9cfde7935a1ef","chapter":{"id":"I","title":"学习信号与表示"},"unit":{"id":"i-masked-prediction","title":"限制可见信息，怎样构成可验证的重建任务？"},"page":{"id":"reading-block-f7555aca3cff49f492d243beb43d1bf6","title":"两个目标，两个分母","number":5},"sectionTitle":"两个目标，两个分母","currentQuestion":"两个目标，两个分母","sourceSlides":[],"knowledgeBlock":null,"formulas":[],"previousBlock":"b-0a3bacc4598f7497ce42a853","nextBlock":null,"prerequisites":[{"id":"b-746882bf55072c50fe67278e","url":"#b-746882bf55072c50fe67278e","role":"section-title"},{"id":"b-8c82cc249dc215427d69bcf7","url":"#b-8c82cc249dc215427d69bcf7","role":"problem-context"}],"relatedBlocks":[]},{"id":"b-7d4cb07c9a2f416e226947aa","type":"h2","scope":"block-5dca5700ee4c417787a944e984502529","url":"https://codingai-lec03.pages.dev/course/notebooks/b5ba27f2c5ba4ec0922208a7d5dc80a4.html#b-7d4cb07c9a2f416e226947aa","content":"## PCA、均值基线与冻结评价","sha256":"66b2dace11fe3e1e1ac14aa4baad3bd70f35d66db29be4adb83a0909a35dad07","chapter":{"id":"I","title":"学习信号与表示"},"unit":{"id":"i-masked-prediction","title":"限制可见信息，怎样构成可验证的重建任务？"},"page":{"id":"reading-block-5dca5700ee4c417787a944e984502529","title":"PCA、均值基线与冻结评价","number":6},"sectionTitle":"PCA、均值基线与冻结评价","currentQuestion":"PCA、均值基线与冻结评价","sourceSlides":[],"knowledgeBlock":null,"formulas":[],"previousBlock":null,"nextBlock":"b-0cb682a68c331ab373736858","prerequisites":[{"id":"b-0cb682a68c331ab373736858","url":"#b-0cb682a68c331ab373736858","role":"problem-context"}],"relatedBlocks":[]},{"id":"b-0cb682a68c331ab373736858","type":"p","scope":"block-5dca5700ee4c417787a944e984502529","url":"https://codingai-lec03.pages.dev/course/notebooks/b5ba27f2c5ba4ec0922208a7d5dc80a4.html#b-0cb682a68c331ab373736858","content":"PCA只在清洁训练图上拟合均值与16个主轴，再把保留集的噪声图投影回低秩空间。受约束的线性自编码器与PCA子空间有联系，所以它是有意义的透明比较；SVD没有替代这里的非线性神经网络训练。二者瓶颈同为16维，但参数量、目标和优化不同，不能把这一结果解释成严格的架构消融。","sha256":"91f9e054a47e5dff5f5eff0c1755d5af1d8d8a7561e448bf00f969d4963853e4","chapter":{"id":"I","title":"学习信号与表示"},"unit":{"id":"i-masked-prediction","title":"限制可见信息，怎样构成可验证的重建任务？"},"page":{"id":"reading-block-5dca5700ee4c417787a944e984502529","title":"PCA、均值基线与冻结评价","number":6},"sectionTitle":"PCA、均值基线与冻结评价","currentQuestion":"PCA、均值基线与冻结评价","sourceSlides":[],"knowledgeBlock":null,"formulas":[],"previousBlock":"b-7d4cb07c9a2f416e226947aa","nextBlock":"b-d199e20593203b2eaf01ed8a","prerequisites":[{"id":"b-7d4cb07c9a2f416e226947aa","url":"#b-7d4cb07c9a2f416e226947aa","role":"section-title"}],"relatedBlocks":[]},{"id":"b-d199e20593203b2eaf01ed8a","type":"p","scope":"block-5dca5700ee4c417787a944e984502529","url":"https://codingai-lec03.pages.dev/course/notebooks/b5ba27f2c5ba4ec0922208a7d5dc80a4.html#b-d199e20593203b2eaf01ed8a","content":"遮挡评估固定模型权重，在25%、50%、75%三个比例上使用同一批保留图和每图同一随机排列；隐藏集合随比例嵌套增加。对照是训练集均值图和填零，不是用保留图计算的均值。卷积模型在三个比例下仍处理完整8×8网格，不能把可见patch数当作本模型的加速比。","sha256":"a1d2c567d541da19ed6e84487f8acfe7004876cf28049f9f3f85db6ce856e984","chapter":{"id":"I","title":"学习信号与表示"},"unit":{"id":"i-masked-prediction","title":"限制可见信息，怎样构成可验证的重建任务？"},"page":{"id":"reading-block-5dca5700ee4c417787a944e984502529","title":"PCA、均值基线与冻结评价","number":6},"sectionTitle":"PCA、均值基线与冻结评价","currentQuestion":"PCA、均值基线与冻结评价","sourceSlides":[],"knowledgeBlock":null,"formulas":[],"previousBlock":"b-0cb682a68c331ab373736858","nextBlock":"b-69fb5985759e68b0d94547af","prerequisites":[{"id":"b-7d4cb07c9a2f416e226947aa","url":"#b-7d4cb07c9a2f416e226947aa","role":"section-title"},{"id":"b-0cb682a68c331ab373736858","url":"#b-0cb682a68c331ab373736858","role":"problem-context"}],"relatedBlocks":[]},{"id":"b-69fb5985759e68b0d94547af","type":"p","scope":"block-5dca5700ee4c417787a944e984502529","url":"https://codingai-lec03.pages.dev/course/notebooks/b5ba27f2c5ba4ec0922208a7d5dc80a4.html#b-69fb5985759e68b0d94547af","content":"表示评价输入清洁图像，遮挡模型的mask通道全零；分别提取已训练/随机初始化的16维h，L2归一化后做余弦5-NN。参考库只有训练图及其标签。重建评价和数字识别有不同分母、单位和目标，不互相替代。","sha256":"1ed9fc03f7594779444088061d207504839c02ffaa69fda6d84d515f0192db5e","chapter":{"id":"I","title":"学习信号与表示"},"unit":{"id":"i-masked-prediction","title":"限制可见信息，怎样构成可验证的重建任务？"},"page":{"id":"reading-block-5dca5700ee4c417787a944e984502529","title":"PCA、均值基线与冻结评价","number":6},"sectionTitle":"PCA、均值基线与冻结评价","currentQuestion":"PCA、均值基线与冻结评价","sourceSlides":[],"knowledgeBlock":null,"formulas":[],"previousBlock":"b-d199e20593203b2eaf01ed8a","nextBlock":"b-c729675790ace5e7ea6abae8","prerequisites":[{"id":"b-7d4cb07c9a2f416e226947aa","url":"#b-7d4cb07c9a2f416e226947aa","role":"section-title"},{"id":"b-0cb682a68c331ab373736858","url":"#b-0cb682a68c331ab373736858","role":"problem-context"}],"relatedBlocks":[]},{"id":"b-c729675790ace5e7ea6abae8","type":"pre","scope":"block-41ffe8f7d5284c27aec5c2474b234a1f","url":"https://codingai-lec03.pages.dev/course/notebooks/b5ba27f2c5ba4ec0922208a7d5dc80a4.html#b-c729675790ace5e7ea6abae8","content":"```\ndef run_reconstruction(out_dir='lab08-output'):\n    started=time.time();out=Path(out_dir);out.mkdir(parents=True,exist_ok=True)\n    x,y,train,test=digit_data()\n    np.savez_compressed(out/'inputs.npz',images=x.numpy(),labels=y,train_ids=train,test_ids=test)\n    denoise,random_d,hist_d,delta_d=train_ae(x,train,'denoise',40)\n    masked,random_m,hist_m,delta_m=train_ae(x,train,'masked',50)\n    torch.save(denoise.state_dict(),out/'denoise.pt');torch.save(masked.state_dict(),out/'masked-conv-ae.pt')\n    torch.save({'denoise':random_d.state_dict(),'masked':random_m.state_dict()},out/'random-init.pt')\n    denoise.eval();masked.eval();torch.manual_seed(SEED+19)\n    clean=x[test];corrupted=noise_corrupt(clean)\n    with torch.no_grad():recon=denoise(corrupted)[0]\n    mean_image=x[train].mean(0,keepdim=True)\n    # PCA fits clean training images only. It is a rank-16 linear projection baseline.\n    a=x[train].flatten(1).numpy();mu=a.mean(0);_,_,vt=np.linalg.svd(a-mu,full_matrices=False)\n    v=vt[:16];noisy=corrupted.flatten(1).numpy();pca=np.clip((noisy-mu)@v.T@v+mu,0,1)\n    metrics={'denoise':{'corrupted_input_mse':float(F.mse_loss(corrupted,clean)),\n      'train_mean_mse':float(((clean-mean_image)**2).mean()),\n      'rank16_pca_noisy_mse':float(np.mean((pca-clean.flatten(1).numpy())**2)),\n      'learned_conv_ae_mse':float(F.mse_loss(recon,clean))}}\n    masked_outputs={}\n    for ratio in [.25,.5,.75]:\n        torch.manual_seed(SEED+25)  # Same permutation at all ratios: nested hidden sets.\n        mask=patch_mask(len(clean),ratio)\n        inp=torch.cat([clean*(1-mask),mask],1)\n        with torch.no_grad():pred=masked(inp)[0]\n        loss=(((pred-clean)**2)*mask).sum()/mask.sum()\n        mean_loss=(((mean_image-clean)**2)*mask).sum()/mask.sum()\n        zero_loss=((clean**2)*mask).sum()/mask.sum()\n        metrics[f'mask_{ratio}']={'hidden_pixels_mse':float(loss),'train_mean_hidden_mse':float(mean_loss),\n          'zero_fill_hidden_mse':float(zero_loss),'visible_patches':int(16*(1-ratio)),\n          'conv_encoder_grid_pixels':64}\n        masked_outputs[ratio]=(mask,pred)\n    probes={}\n    for name,model,is_masked in [('random-denoise',random_d,False),('trained-denoise',denoise,False),('random-masked',random_m,True),('trained-masked',masked,True)]:\n        probes[name]=frozen_probe(model,x,y,train,test,is_masked)\n    # Save complete inference evidence, including corruption and masks.\n    output_arrays={'clean':clean.numpy(),'corrupted':corrupted.numpy(),'denoised':recon.numpy(),'pca':pca.reshape(-1,1,8,8)}\n    for ratio,(mask,pred) in masked_outputs.items():output_arrays[f'mask_{ratio}']=mask.numpy();output_arrays[f'pred_{ratio}']=pred.numpy()\n    np.savez_compressed(out/'predictions.npz',**output_arrays)\n    # Examples selected BEFORE seeing predictions: first eight test IDs, never cherry-picked.\n    mask,pred=masked_outputs[.5]\n    fig,axes=plt.subplots(8,6,figsize=(9,12))\n    titles=['clean','corrupted','denoised','50% masked','predicted hidden','absolute error']\n    for i in range(8):\n        completed=clean[i]*(1-mask[i])+pred[i]*mask[i]\n        ims=[clean[i],corrupted[i],recon[i],clean[i]*(1-mask[i]),completed,abs(completed-clean[i])]\n        for j,im in enumerate(ims):\n            axes[i,j].imshow(im[0],cmap='magma' if j==5 else 'gray',vmin=0,vmax=1,interpolation='nearest')\n            axes[i,j].set_xticks([]);axes[i,j].set_yticks([])\n            if i==0:axes[i,j].set_title(titles[j],fontsize=9)\n        axes[i,0].set_ylabel(f'id {test[i]}\\ny={y[test[i]]}',fontsize=8)\n    fig.tight_layout();fig.savefig(out/'reconstruction-grid.png',dpi=150);plt.close(fig)\n    fig,axes=plt.subplots(1,3,figsize=(14,4))\n    axes[0].plot(hist_d,label='denoise: all pixels');axes[0].plot(hist_m,label='masked: hidden only');axes[0].legend(fontsize=8)\n    axes[0].set(xlabel='epoch (0-based)',ylabel='train MSE',title='Objectives have different pixel sets')\n    ratios=[.25,.5,.75]\n    axes[1].plot(ratios,[metrics[f'mask_{r}']['hidden_pixels_mse'] for r in ratios],'o-',label='trained at 50%')\n    axes[1].plot(ratios,[metrics[f'mask_{r}']['train_mean_hidden_mse'] for r in ratios],'o--',label='train mean')\n    axes[1].set(xlabel='evaluation mask ratio',ylabel='held-out hidden MSE');axes[1].legend()\n    axes[2].bar(list(probes),list(probes.values()));axes[2].tick_params(axis='x',rotation=25)\n    axes[2].set(ylim=(0,1),ylabel='held-out accuracy',title='Frozen clean h: cosine 5-NN')\n    fig.tight_layout();fig.savefig(out/'loss-mask-probe.png',dpi=140);plt.close(fig)\n    config={'seed':SEED,'dataset':'sklearn load_digits, 1797 grayscale 8x8, pixels divided by16','train_n':len(train),'test_n':len(test),'split':'stratified random source-image split before corruption','denoise_epochs':40,'masked_epochs':50,'batch_size':64,'adam_lr':.003,'latent_dim':16,'denoise_corruption':{'pixel_drop':.25,'gaussian_sigma':.15,'clip':[0,1]},'mask_training_ratio':.5,'mask_patch_pixels':[2,2],'labels_used':'stratify/probe only; never AE loss','mask_encoder':'dense two-channel convolutional autoencoder; NOT ViT MAE','test_tuning':'none; predetermined epochs/mask ratios'}\n    (out/'config.json').write_text(json.dumps(config,indent=2))\n    payload={'code_sha256':globals().get('SOURCE_SHA256') or hashlib.sha256(Path(__file__).read_bytes()).hexdigest(),'kind':'local CPU full training and held-out evaluation','utc':time.strftime('%Y-%m-%dT%H:%M:%SZ',time.gmtime()),'seconds':time.time()-started,'torch':torch.__version__,'numpy':np.__version__,'python':platform.python_version(),'metrics':metrics,'frozen_clean_5nn':probes,'loss_history':{'denoise':hist_d,'masked':hist_m},'encoder_weight_l2_change':{'denoise':delta_d,'masked':delta_m},'index_warmup':mask_index_warmup(),'inputs_sha256':hashlib.sha256((out/'inputs.npz').read_bytes()).hexdigest()}\n    (out/'results.json').write_text(json.dumps(payload,indent=2))\n    print(json.dumps({'metrics':metrics,'frozen_clean_5nn':probes,'seconds':payload['seconds']},indent=2))\n    return payload\n```","sha256":"346501991ef9efbc297a06f21a942a08d19a2941f1cd91dc0ee0f9ad87b2aec4","chapter":{"id":"I","title":"学习信号与表示"},"unit":{"id":"i-masked-prediction","title":"限制可见信息，怎样构成可验证的重建任务？"},"page":{"id":"reading-block-5dca5700ee4c417787a944e984502529","title":"PCA、均值基线与冻结评价","number":6},"sectionTitle":"PCA、均值基线与冻结评价","currentQuestion":"PCA、均值基线与冻结评价","sourceSlides":[],"knowledgeBlock":null,"formulas":[],"previousBlock":"b-69fb5985759e68b0d94547af","nextBlock":"b-10b60d1a51713756f78b8f9c","prerequisites":[{"id":"b-7d4cb07c9a2f416e226947aa","url":"#b-7d4cb07c9a2f416e226947aa","role":"section-title"},{"id":"b-0cb682a68c331ab373736858","url":"#b-0cb682a68c331ab373736858","role":"problem-context"}],"relatedBlocks":[]},{"id":"b-10b60d1a51713756f78b8f9c","type":"pre","scope":"block-8c988cd2947b4474b4d633c2620028a7","url":"https://codingai-lec03.pages.dev/course/notebooks/b5ba27f2c5ba4ec0922208a7d5dc80a4.html#b-10b60d1a51713756f78b8f9c","content":"```\nresult=run_reconstruction('lab08-notebook-output')\nfrom IPython.display import Image,display\ndisplay(Image(filename='lab08-notebook-output/reconstruction-grid.png'))\ndisplay(Image(filename='lab08-notebook-output/loss-mask-probe.png'))\n```","sha256":"494314c1f26aeeda8950fd4f9fa42aac5d81da1b3288f0e066ba185a85e42cfa","chapter":{"id":"I","title":"学习信号与表示"},"unit":{"id":"i-masked-prediction","title":"限制可见信息，怎样构成可验证的重建任务？"},"page":{"id":"reading-block-5dca5700ee4c417787a944e984502529","title":"PCA、均值基线与冻结评价","number":6},"sectionTitle":"PCA、均值基线与冻结评价","currentQuestion":"PCA、均值基线与冻结评价","sourceSlides":[],"knowledgeBlock":null,"formulas":[],"previousBlock":"b-c729675790ace5e7ea6abae8","nextBlock":null,"prerequisites":[{"id":"b-7d4cb07c9a2f416e226947aa","url":"#b-7d4cb07c9a2f416e226947aa","role":"section-title"},{"id":"b-0cb682a68c331ab373736858","url":"#b-0cb682a68c331ab373736858","role":"problem-context"}],"relatedBlocks":[]},{"id":"b-8ae9a7adff920dc85598fd63","type":"h2","scope":"block-7653447b25ea4f148d99cc7e004b290b","url":"https://codingai-lec03.pages.dev/course/notebooks/b5ba27f2c5ba4ec0922208a7d5dc80a4.html#b-8ae9a7adff920dc85598fd63","content":"## 已运行结果：能恢复笔画，也会模糊或补错","sha256":"1fcf124d95292a66d0028aa8136225908b256c15dbd114cbfd1c90d2580c2076","chapter":{"id":"I","title":"学习信号与表示"},"unit":{"id":"i-masked-prediction","title":"限制可见信息，怎样构成可验证的重建任务？"},"page":{"id":"reading-block-7653447b25ea4f148d99cc7e004b290b","title":"已运行结果：能恢复笔画，也会模糊或补错","number":7},"sectionTitle":"已运行结果：能恢复笔画，也会模糊或补错","currentQuestion":"已运行结果：能恢复笔画，也会模糊或补错","sourceSlides":[],"knowledgeBlock":null,"formulas":[],"previousBlock":null,"nextBlock":"b-f353272ee4c9ea136e48b038","prerequisites":[{"id":"b-515309f84cb4586d2e33faac","url":"#b-515309f84cb4586d2e33faac","role":"problem-context"}],"relatedBlocks":[]},{"id":"b-f353272ee4c9ea136e48b038","type":"table","scope":"block-7653447b25ea4f148d99cc7e004b290b","url":"https://codingai-lec03.pages.dev/course/notebooks/b5ba27f2c5ba4ec0922208a7d5dc80a4.html#b-f353272ee4c9ea136e48b038","content":"| 去噪保留集指标 | 全像素MSE（越低越好） |\n| --- | --- |\n| 直接使用破坏图像 | 0.06244 |\n| 训练集均值图 | 0.07351 |\n| 16维PCA投影噪声图 | 0.04160 |\n| 16维卷积去噪AE | 0.03217 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patch的像素重建目标"],["计算量随mask变化","本实现基本不减少","可见token更少会减少编码器计算"],["表示评价","小数字集冻结5-NN","论文有大规模分类与迁移评价"]]},"previousBlock":"b-582e48a746d27f71e4967fed","nextBlock":"b-5d297dbbb6757d7d10b4c54b","prerequisites":[{"id":"b-582e48a746d27f71e4967fed","url":"#b-582e48a746d27f71e4967fed","role":"section-title"},{"id":"b-5d297dbbb6757d7d10b4c54b","url":"#b-5d297dbbb6757d7d10b4c54b","role":"problem-context"}],"relatedBlocks":[]},{"id":"b-5d297dbbb6757d7d10b4c54b","type":"p","scope":"block-18ba37010b894b2893332630cf8b3070","url":"https://codingai-lec03.pages.dev/course/notebooks/b5ba27f2c5ba4ec0922208a7d5dc80a4.html#b-5d297dbbb6757d7d10b4c54b","content":"因此本册完成了真实图像遮挡重建与单独的表示评价，但没有把卷积模型重命名为原MAE复现。索引热身、masked ConvAE和MAE论文提供三种不同粒度的证据。","sha256":"f36623b4a66bc23f3e6de0948398eac4da9de12f2b1cb7dee15f169d61a3c0af","chapter":{"id":"I","title":"学习信号与表示"},"unit":{"id":"i-masked-prediction","title":"限制可见信息，怎样构成可验证的重建任务？"},"page":{"id":"reading-block-18ba37010b894b2893332630cf8b3070","title":"与原MAE逐项对齐","number":9},"sectionTitle":"与原MAE逐项对齐","currentQuestion":"与原MAE逐项对齐","sourceSlides":[],"knowledgeBlock":null,"formulas":[],"previousBlock":"b-d2fad667a3aabe4fc7f8d8fd","nextBlock":"b-f4ccf58a102c150640c5ce57","prerequisites":[{"id":"b-582e48a746d27f71e4967fed","url":"#b-582e48a746d27f71e4967fed","role":"section-title"}],"relatedBlocks":[]},{"id":"b-f4ccf58a102c150640c5ce57","type":"p","scope":"block-18ba37010b894b2893332630cf8b3070","url":"https://codingai-lec03.pages.dev/course/notebooks/b5ba27f2c5ba4ec0922208a7d5dc80a4.html#b-f4ccf58a102c150640c5ce57","content":"可做的条件修改：保持训练权重，先改变 `patch_mask` 的评估遮挡比例，维持相同随机排列；再选择一个新实验，把训练遮挡率改为75%，另存新目录。先预测低可见证据会怎样改变细笔画和读出，不要只寻找一张好看的重建图。","sha256":"a0876f8de075bc82b9ef180984eea28985c8ea153021910f079bdd919e4c0538","chapter":{"id":"I","title":"学习信号与表示"},"unit":{"id":"i-masked-prediction","title":"限制可见信息，怎样构成可验证的重建任务？"},"page":{"id":"reading-block-18ba37010b894b2893332630cf8b3070","title":"与原MAE逐项对齐","number":9},"sectionTitle":"与原MAE逐项对齐","currentQuestion":"与原MAE逐项对齐","sourceSlides":[],"knowledgeBlock":null,"formulas":[],"previousBlock":"b-5d297dbbb6757d7d10b4c54b","nextBlock":"b-738dd2c454df3589d0779fb3","prerequisites":[{"id":"b-582e48a746d27f71e4967fed","url":"#b-582e48a746d27f71e4967fed","role":"section-title"},{"id":"b-5d297dbbb6757d7d10b4c54b","url":"#b-5d297dbbb6757d7d10b4c54b","role":"problem-context"}],"relatedBlocks":[]},{"id":"b-738dd2c454df3589d0779fb3","type":"p","scope":"block-18ba37010b894b2893332630cf8b3070","url":"https://codingai-lec03.pages.dev/course/notebooks/b5ba27f2c5ba4ec0922208a7d5dc80a4.html#b-738dd2c454df3589d0779fb3","content":"来源：[去噪自编码器原论文](https://www.cs.toronto.edu/~larocheh/publications/icml-2008-denoising-autoencoders.pdf)、[MAE原论文](https://openaccess.thecvf.com/content/CVPR2022/html/He_Masked_Autoencoders_Are_Scalable_Vision_Learners_CVPR_2022_paper.html)、[MAE作者实现的mask/restore/loss](https://github.com/facebookresearch/mae/blob/main/models_mae.py)、[scikit-learn digits数据说明](https://scikit-learn.org/stable/modules/generated/sklearn.datasets.load_digits.html)。本册模型结构、训练权重、图和测量来自本轮CPU运行。","sha256":"1d07670d7c0674d4a921d9458ac6aca1e7f8c2c46deb6309e83d78c9c8146c58","chapter":{"id":"I","title":"学习信号与表示"},"unit":{"id":"i-masked-prediction","title":"限制可见信息，怎样构成可验证的重建任务？"},"page":{"id":"reading-block-18ba37010b894b2893332630cf8b3070","title":"与原MAE逐项对齐","number":9},"sectionTitle":"与原MAE逐项对齐","currentQuestion":"与原MAE逐项对齐","sourceSlides":[],"knowledgeBlock":null,"formulas":[],"previousBlock":"b-f4ccf58a102c150640c5ce57","nextBlock":null,"prerequisites":[{"id":"b-582e48a746d27f71e4967fed","url":"#b-582e48a746d27f71e4967fed","role":"section-title"},{"id":"b-5d297dbbb6757d7d10b4c54b","url":"#b-5d297dbbb6757d7d10b4c54b","role":"problem-context"}],"relatedBlocks":[]}]}