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图像重建：遮挡损失与表示评价](/course/notebooks/b5ba27f2c5ba4ec0922208a7d5dc80a4.html)","sha256":"4730e90285e279b2564c179018b9df76aafd9db5dd2729f0f687ac380af591ed","chapter":{"id":"course","title":"运行准备与结果身份 · 实现代码"},"unit":{"id":"notebook-blocks/4bb149c8de264021b5927384daedb5f7","title":"运行准备与结果身份 · 实现代码"},"page":{"id":"reading-opening","title":"运行准备与结果身份 · 实现代码","number":1},"sectionTitle":"运行准备与结果身份 · 实现代码","currentQuestion":"运行准备与结果身份 · 实现代码","sourceSlides":[],"knowledgeBlock":null,"formulas":[],"previousBlock":null,"nextBlock":"b-cb0292ef8e58bb065033480e","prerequisites":[],"relatedBlocks":[]},{"id":"b-cb0292ef8e58bb065033480e","type":"h1","scope":"document","url":"https://codingai-lec03.pages.dev/course/notebook-blocks/4bb149c8de264021b5927384daedb5f7.html#b-cb0292ef8e58bb065033480e","content":"# 运行准备与结果身份 · 实现代码","sha256":"64e0a911bed15226b4c1020da8c8fabd32e53ab4f0e735df28d2a05dd9d14850","chapter":{"id":"course","title":"运行准备与结果身份 · 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实现代码","number":1},"sectionTitle":"运行准备与结果身份 · 实现代码","currentQuestion":"运行准备与结果身份 · 实现代码","sourceSlides":[],"knowledgeBlock":null,"formulas":[],"previousBlock":"b-647bcf8dcbcebcd23ae9751b","nextBlock":"b-cdddb87f946c2cec0cf77d35","prerequisites":[{"id":"b-cb0292ef8e58bb065033480e","url":"#b-cb0292ef8e58bb065033480e","role":"section-title"},{"id":"b-aa520142bec9c60cfdc8ff03","url":"#b-aa520142bec9c60cfdc8ff03","role":"problem-context"}],"relatedBlocks":[]},{"id":"b-cdddb87f946c2cec0cf77d35","type":"pre","scope":"document","url":"https://codingai-lec03.pages.dev/course/notebook-blocks/4bb149c8de264021b5927384daedb5f7.html#b-cdddb87f946c2cec0cf77d35","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":"course","title":"运行准备与结果身份 · 实现代码"},"unit":{"id":"notebook-blocks/4bb149c8de264021b5927384daedb5f7","title":"运行准备与结果身份 · 实现代码"},"page":{"id":"reading-opening","title":"运行准备与结果身份 · 实现代码","number":1},"sectionTitle":"运行准备与结果身份 · 实现代码","currentQuestion":"运行准备与结果身份 · 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