🕷️ Crawler Inspector

URL Lookup

Direct Parameter Lookup

Raw Queries and Responses

1. Shard Calculation

Query:

Response:

Calculated Shard: 50 (from laksa015)

2. Crawled Status Check

Query:

curl -X POST \
  'http://laksa050.int.ahrefs:8124/' \
  -H 'Content-Type: text/plain' \
  -H 'X-ClickHouse-Database: crawler3' \
  -H 'Authorization: Basic YXBpOg==' \
  -d 'SELECT getAhrefsURLFromUnparsed(src_unparsed) AS found_url, ifNull(toUnixTimestamp(download_stamp), 0) AS crawl_time, ifNull(toUnixTimestamp(props_url_first_seen), 0) AS first_indexed_time, download_http_code AS http_code, src_unparsed AS src_unparsed, src_root_hash AS src_root_hash, history_drop_reason AS history_drop_reason, meta_title AS meta_title, meta_descriptions AS meta_descriptions, attrs_boilerpipe_text AS attrs_boilerpipe_text, attrs_markdown AS attrs_markdown, attrs_readable_markdown AS attrs_readable_markdown, meta_canonical AS meta_canonical, ml_categories_json AS ml_categories_json, ml_types_json AS ml_types_json, ml_intent_types_json AS ml_intent_types_json, meta_language AS meta_language, attrs_author AS attrs_author, ifNull(toUnixTimestamp(attrs_publish_time), 0) AS attrs_publish_time, ifNull(toUnixTimestamp(attrs_original_publish_time), 0) AS attrs_original_publish_time, ifNull(attrs_is_republished, 0) AS attrs_is_republished, ifNull(attrs_nr_words, 0) AS attrs_nr_words, ifNull(attrs_boilerpipe_nr_words, 0) AS attrs_boilerpipe_nr_words, ifNull(body_ext_links_number, 0) AS body_ext_links_number, ifNull(body_int_links_number, 0) AS body_int_links_number, ifNull(meta_nofollow, 0) AS meta_nofollow, ifNull(meta_noarchive, 0) AS meta_noarchive, ifNull(props_was_rendered, 0) AS props_was_rendered, ifNull(src_redirect, \'\') AS src_redirect, ifNull(download_time_msec, 0) AS download_time_msec, ifNull(download_ttfb_msec, 0) AS download_ttfb_msec, ifNull(download_size, 0) AS download_size FROM crawler3.page_info_local FINAL PREWHERE (src_root_hash, src_unparsed) IN ((getAhrefsRootHashFromUnparsed(getAhrefsUnparsedNoserviceFromURL(\'https://cloud.tencent.com/developer/article/1491384\')), getAhrefsUnparsedNoserviceFromURL(\'https://cloud.tencent.com/developer/article/1491384\'))) FORMAT JSONEachRow'

Response:

{"found_url":"https:\/\/cloud.tencent.com\/developer\/article\/1491384","crawl_time":1766366540,"first_indexed_time":1580008165,"http_code":200,"src_unparsed":"com,tencent!cloud,\/developer\/article\/1491384 s443","src_root_hash":"6417570461941030050","history_drop_reason":null,"meta_title":"Tensorflow入门教程，TensorFlow-Examples on Github-腾讯云开发者社区-腾讯云","meta_descriptions":["TensorFlow-Examples是GitHub上3.3万星标的TensorFlow入门教程，涵盖机器学习基础到深度学习模型（如CNN、RNN、GAN等）。包含TF v1\/v2版本代码示例，详细讲解数据处理、模型训练、TensorBoard可视化等核心内容，特别适合初学者快速掌握TensorFlow框架的使用技巧和..."],"attrs_boilerpipe_text":"前言\nTensorflow作为深度学习框架的头把交椅，拥有大批量的用户，当然也有很多新手想要加入Tensorflow的大部队。大家都知道github已经成为机器学习各种知识资源分享学习的平台了。今天小编给大家介绍一个十分受欢迎的Tensorflow入门教程：TensorFlow-Examples\nhttps:\/\/github.com\/aymericdamien\/TensorFlow-Examples\n基本信息\n本教程旨在通过示例轻松地深入研究TensorFlow。为了可读性，它包括了笔记本和源代码的解释，为两个TF v1和v2版本。\n它适合初学者，提供关于TensorFlow清晰和简洁的例子。除了传统的“原始”TensorFlow实现之外，您还可以找到最新的TensorFlow API实践(例如层、估计器、数据集等)。\n目前该项目已经有33000+的star数目，受欢迎程度可见一斑。\n教程目录\n第零章 前提\n机器学习介绍\n介绍MNIST数据集\n第一章 介绍\nHello World\n基础运算\nTensorFlow Eager API\n注：Eager Execution是一个命令式、运行定义式的接口，其中，操作一旦从Python中调用便立刻得以执行。这样TensorFlow的入门使用就变得相对简单，并可以使研究和开发过程更为直观\n第二章 基础模型\n线性回归 (包括eager api)\n逻辑回归 (包括eager api)\n最近邻算法\nK-Means算法\n随机森林\nGradient Boosted Decision Tree (GBDT)\nWord Embedding\n第三章 神经网络\n监督学习\n简单神经网络 (包括tf.layers 和 eager api)\n卷积神经网络 (包括tf.layers)\n递归神经网络 (LSTM)\n双向递归神经网络 (LSTM)\n动态递归神经网络 (LSTM)\n非监督学习\nAuto-Encode\nVariational Auto-Encoder\nGAN\nDCGAN\n第四章 工具\n保存和储存一个模型\nTensorboard\n第五章 数据管理\n建立一个图像数据集\nTensorFlow Dataset API\n加载和解析数据\n建立和加载 TFRecords\n图像转换\n第六章 Multi GPU\nMulti-GPU基本操作\n用Multi-GPU训练一个神经网络\n内容分析\n这个教程有基本的机器学习模型，也有深度学习的基本模型，包括现在流行的GAN，在模型方面比较全面。\n在数据集方面，第五章非常重要，虽然我们有标准的MNIST数据集，但是还是需要学会处理数据(包括预处理)，把数据整合成可以放在网络训练的格式。\n最后第四章，在可视化以及存储模型方面给出了例子。可以供大家学习使用，让TensorFlow的效率提高。\n总体而言，这个教程基础扎实和全面，非常适合新手。\n文末的那啥帮忙点一下~\n本文参与 \n腾讯云自媒体同步曝光计划\n，分享自微信公众号。\n原始发表：2019-08-20，如有侵权请联系 \ncloudcommunity@tencent.com\n删除","attrs_markdown":"[HuangWeiAI](https:\/\/cloud.tencent.com\/developer\/user\/5908637)\n\n## Tensorflow入门教程，TensorFlow-Examples on Github\n\n关注作者\n\n[*腾讯云*](https:\/\/cloud.tencent.com\/?from=20060&from_column=20060)\n\n[*开发者社区*](https:\/\/cloud.tencent.com\/developer)\n\n[文档](https:\/\/cloud.tencent.com\/document\/product?from=20702&from_column=20702)[建议反馈](https:\/\/cloud.tencent.com\/voc\/?from=20703&from_column=20703)[控制台](https:\/\/console.cloud.tencent.com\/?from=20063&from_column=20063)\n\n登录\/注册\n\n[首页](https:\/\/cloud.tencent.com\/developer)\n\n学习\n\n活动\n\n专区\n\n圈层\n\n工具\n\n[MCP广场![](https:\/\/qccommunity.qcloudimg.com\/image\/new.png)](https:\/\/cloud.tencent.com\/developer\/mcp)\n\n文章\/答案\/技术大牛\n\n搜索\n\n搜索\n\n关闭\n\n发布\n\nHuangWeiAI\n\n[社区首页](https:\/\/cloud.tencent.com\/developer) \\>[专栏](https:\/\/cloud.tencent.com\/developer\/column) \\>Tensorflow入门教程，TensorFlow-Examples on Github\n\n# Tensorflow入门教程，TensorFlow-Examples on Github\n![作者头像](https:\/\/ask.qcloudimg.com\/custom-avatar\/5908637\/2fekxnent3.jpg)\n\nHuangWeiAI\n\n关注\n\n发布于 2019-08-21 15:37:17\n\n发布于 2019-08-21 15:37:17\n\n852\n\n0\n\n举报\n\n文章被收录于专栏：[浊酒清味](https:\/\/cloud.tencent.com\/developer\/column\/79156)[浊酒清味]()\n\n前言\n\nTensorflow作为深度学习框架的头把交椅，拥有大批量的用户，当然也有很多新手想要加入Tensorflow的大部队。大家都知道github已经成为机器学习各种知识资源分享学习的平台了。今天小编给大家介绍一个十分受欢迎的Tensorflow入门教程：TensorFlow-Examples\n\nhttps:\/\/github.com\/aymericdamien\/TensorFlow-Examples\n\n基本信息\n\n本教程旨在通过示例轻松地深入研究TensorFlow。为了可读性，它包括了笔记本和源代码的解释，为两个TF v1和v2版本。\n\n它适合初学者，提供关于TensorFlow清晰和简洁的例子。除了传统的“原始”TensorFlow实现之外，您还可以找到最新的TensorFlow API实践(例如层、估计器、数据集等)。\n\n目前该项目已经有33000+的star数目，受欢迎程度可见一斑。\n\n![](https:\/\/ask.qcloudimg.com\/http-save\/yehe-5908637\/5ul6smhfd5.jpeg)\n\n教程目录\n\n第零章 前提\n\n- 机器学习介绍\n- 介绍MNIST数据集\n\n第一章 介绍\n\n- Hello World\n- 基础运算\n- TensorFlow Eager API\n\n注：Eager Execution是一个命令式、运行定义式的接口，其中，操作一旦从Python中调用便立刻得以执行。这样TensorFlow的入门使用就变得相对简单，并可以使研究和开发过程更为直观\n\n第二章 基础模型\n\n- 线性回归 (包括eager api)\n- 逻辑回归 (包括eager api)\n- 最近邻算法\n- K-Means算法\n- 随机森林\n- Gradient Boosted Decision Tree (GBDT)\n- Word Embedding\n\n第三章 神经网络\n\n监督学习\n\n- 简单神经网络 (包括tf.layers 和 eager api)\n- 卷积神经网络 (包括tf.layers)\n- 递归神经网络 (LSTM)\n- 双向递归神经网络 (LSTM)\n- 动态递归神经网络 (LSTM)\n\n非监督学习\n\n- Auto-Encode\n- Variational Auto-Encoder\n- GAN\n- DCGAN\n\n第四章 工具\n\n- 保存和储存一个模型\n- Tensorboard\n\n第五章 数据管理\n\n- 建立一个图像数据集\n- TensorFlow Dataset API\n- 加载和解析数据\n- 建立和加载 TFRecords\n- 图像转换\n\n第六章 Multi GPU\n\n- Multi-GPU基本操作\n- 用Multi-GPU训练一个神经网络\n\n内容分析\n\n这个教程有基本的机器学习模型，也有深度学习的基本模型，包括现在流行的GAN，在模型方面比较全面。\n\n在数据集方面，第五章非常重要，虽然我们有标准的MNIST数据集，但是还是需要学会处理数据(包括预处理)，把数据整合成可以放在网络训练的格式。\n\n最后第四章，在可视化以及存储模型方面给出了例子。可以供大家学习使用，让TensorFlow的效率提高。\n\n总体而言，这个教程基础扎实和全面，非常适合新手。\n\n***文末的那啥帮忙点一下~***\n\n本文参与 [腾讯云自媒体同步曝光计划](https:\/\/cloud.tencent.com\/developer\/support-plan)，分享自微信公众号。\n\n原始发表：2019-08-20，如有侵权请联系 [cloudcommunity@tencent.com](mailto:cloudcommunity@tencent.com) 删除\n\n[tensorflow](https:\/\/cloud.tencent.com\/developer\/tag\/10150)\n\n[神经网络](https:\/\/cloud.tencent.com\/developer\/tag\/10332)\n\n[机器学习](https:\/\/cloud.tencent.com\/developer\/tag\/10149)\n\n[api](https:\/\/cloud.tencent.com\/developer\/tag\/10292)\n\n[人工智能](https:\/\/cloud.tencent.com\/developer\/tag\/10539)\n\n本文分享自 浊酒清味 微信公众号，前往查看\n\n如有侵权，请联系 [cloudcommunity@tencent.com](mailto:cloudcommunity@tencent.com) 删除。\n\n本文参与 [腾讯云自媒体同步曝光计划](https:\/\/cloud.tencent.com\/developer\/support-plan) ，欢迎热爱写作的你一起参与！\n\n[tensorflow](https:\/\/cloud.tencent.com\/developer\/tag\/10150)\n\n[神经网络](https:\/\/cloud.tencent.com\/developer\/tag\/10332)\n\n[机器学习](https:\/\/cloud.tencent.com\/developer\/tag\/10149)\n\n[api](https:\/\/cloud.tencent.com\/developer\/tag\/10292)\n\n[人工智能](https:\/\/cloud.tencent.com\/developer\/tag\/10539)\n\n评论\n\n登录后参与评论\n\n0 条评论\n\n热度\n\n最新\n\n登录 后参与评论\n\n推荐阅读\n\n领券\n\n- ### 社区\n  - [技术文章](https:\/\/cloud.tencent.com\/developer\/column)\n  - 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3. Robots.txt Check

Query:

Response:

4. Spam/Ban Check

Query:

Response:

5. Seen Status Check

ℹ️ Skipped - page is already crawled

📄

INDEXABLE

✅

CRAWLED

4 months ago

🤖

ROBOTS ALLOWED

Page Info Filters

Filter	Status	Condition	Details
HTTP status	PASS	`download_http_code = 200`	HTTP 200
Age cutoff	PASS	`download_stamp > now() - 6 MONTH`	4.1 months ago
History drop	PASS	`isNull(history_drop_reason)`	No drop reason
Spam/ban	PASS	`fh_dont_index != 1 AND ml_spam_score = 0`	ml_spam_score=0
Canonical	PASS	`meta_canonical IS NULL OR = '' OR = src_unparsed`	Not set

Page Details

Property	Value
URL	https://cloud.tencent.com/developer/article/1491384
Last Crawled	2025-12-22 01:22:20 (4 months ago)
First Indexed	2020-01-26 03:09:25 (6 years ago)
HTTP Status Code	200
Content
Meta Title	Tensorflow入门教程，TensorFlow-Examples on Github-腾讯云开发者社区-腾讯云
Meta Description	TensorFlow-Examples是GitHub上3.3万星标的TensorFlow入门教程，涵盖机器学习基础到深度学习模型（如CNN、RNN、GAN等）。包含TF v1/v2版本代码示例，详细讲解数据处理、模型训练、TensorBoard可视化等核心内容，特别适合初学者快速掌握TensorFlow框架的使用技巧和...
Meta Canonical	null
Boilerpipe Text	前言 Tensorflow作为深度学习框架的头把交椅，拥有大批量的用户，当然也有很多新手想要加入Tensorflow的大部队。大家都知道github已经成为机器学习各种知识资源分享学习的平台了。今天小编给大家介绍一个十分受欢迎的Tensorflow入门教程：TensorFlow-Examples https://github.com/aymericdamien/TensorFlow-Examples 基本信息本教程旨在通过示例轻松地深入研究TensorFlow。为了可读性，它包括了笔记本和源代码的解释，为两个TF v1和v2版本。它适合初学者，提供关于TensorFlow清晰和简洁的例子。除了传统的“原始”TensorFlow实现之外，您还可以找到最新的TensorFlow API实践(例如层、估计器、数据集等)。目前该项目已经有33000+的star数目，受欢迎程度可见一斑。教程目录第零章前提机器学习介绍介绍MNIST数据集第一章介绍 Hello World 基础运算 TensorFlow Eager API 注：Eager Execution是一个命令式、运行定义式的接口，其中，操作一旦从Python中调用便立刻得以执行。这样TensorFlow的入门使用就变得相对简单，并可以使研究和开发过程更为直观第二章基础模型线性回归 (包括eager api) 逻辑回归 (包括eager api) 最近邻算法 K-Means算法随机森林 Gradient Boosted Decision Tree (GBDT) Word Embedding 第三章神经网络监督学习简单神经网络 (包括tf.layers 和 eager api) 卷积神经网络 (包括tf.layers) 递归神经网络 (LSTM) 双向递归神经网络 (LSTM) 动态递归神经网络 (LSTM) 非监督学习 Auto-Encode Variational Auto-Encoder GAN DCGAN 第四章工具保存和储存一个模型 Tensorboard 第五章数据管理建立一个图像数据集 TensorFlow Dataset API 加载和解析数据建立和加载 TFRecords 图像转换第六章 Multi GPU Multi-GPU基本操作用Multi-GPU训练一个神经网络内容分析这个教程有基本的机器学习模型，也有深度学习的基本模型，包括现在流行的GAN，在模型方面比较全面。在数据集方面，第五章非常重要，虽然我们有标准的MNIST数据集，但是还是需要学会处理数据(包括预处理)，把数据整合成可以放在网络训练的格式。最后第四章，在可视化以及存储模型方面给出了例子。可以供大家学习使用，让TensorFlow的效率提高。总体而言，这个教程基础扎实和全面，非常适合新手。文末的那啥帮忙点一下~ 本文参与腾讯云自媒体同步曝光计划，分享自微信公众号。原始发表：2019-08-20，如有侵权请联系 cloudcommunity@tencent.com 删除
Markdown	[HuangWeiAI](https://cloud.tencent.com/developer/user/5908637) ## Tensorflow入门教程，TensorFlow-Examples on Github 关注作者 [腾讯云](https://cloud.tencent.com/?from=20060&from_column=20060) [开发者社区](https://cloud.tencent.com/developer) [文档](https://cloud.tencent.com/document/product?from=20702&from_column=20702)[建议反馈](https://cloud.tencent.com/voc/?from=20703&from_column=20703)[控制台](https://console.cloud.tencent.com/?from=20063&from_column=20063) 登录/注册 [首页](https://cloud.tencent.com/developer) 学习活动专区圈层工具 [MCP广场![](https://qccommunity.qcloudimg.com/image/new.png)](https://cloud.tencent.com/developer/mcp) 文章/答案/技术大牛搜索搜索关闭发布 HuangWeiAI [社区首页](https://cloud.tencent.com/developer) \>[专栏](https://cloud.tencent.com/developer/column) \>Tensorflow入门教程，TensorFlow-Examples on Github # Tensorflow入门教程，TensorFlow-Examples on Github ![作者头像](https://ask.qcloudimg.com/custom-avatar/5908637/2fekxnent3.jpg) HuangWeiAI 关注发布于 2019-08-21 15:37:17 发布于 2019-08-21 15:37:17 852 0 举报文章被收录于专栏：[浊酒清味](https://cloud.tencent.com/developer/column/79156)[浊酒清味]() 前言 Tensorflow作为深度学习框架的头把交椅，拥有大批量的用户，当然也有很多新手想要加入Tensorflow的大部队。大家都知道github已经成为机器学习各种知识资源分享学习的平台了。今天小编给大家介绍一个十分受欢迎的Tensorflow入门教程：TensorFlow-Examples https://github.com/aymericdamien/TensorFlow-Examples 基本信息本教程旨在通过示例轻松地深入研究TensorFlow。为了可读性，它包括了笔记本和源代码的解释，为两个TF v1和v2版本。它适合初学者，提供关于TensorFlow清晰和简洁的例子。除了传统的“原始”TensorFlow实现之外，您还可以找到最新的TensorFlow API实践(例如层、估计器、数据集等)。目前该项目已经有33000+的star数目，受欢迎程度可见一斑。 ![](https://ask.qcloudimg.com/http-save/yehe-5908637/5ul6smhfd5.jpeg) 教程目录第零章前提 - 机器学习介绍 - 介绍MNIST数据集第一章介绍 - Hello World - 基础运算 - TensorFlow Eager API 注：Eager Execution是一个命令式、运行定义式的接口，其中，操作一旦从Python中调用便立刻得以执行。这样TensorFlow的入门使用就变得相对简单，并可以使研究和开发过程更为直观第二章基础模型 - 线性回归 (包括eager api) - 逻辑回归 (包括eager api) - 最近邻算法 - K-Means算法 - 随机森林 - Gradient Boosted Decision Tree (GBDT) - Word Embedding 第三章神经网络监督学习 - 简单神经网络 (包括tf.layers 和 eager api) - 卷积神经网络 (包括tf.layers) - 递归神经网络 (LSTM) - 双向递归神经网络 (LSTM) - 动态递归神经网络 (LSTM) 非监督学习 - Auto-Encode - Variational Auto-Encoder - GAN - DCGAN 第四章工具 - 保存和储存一个模型 - Tensorboard 第五章数据管理 - 建立一个图像数据集 - TensorFlow Dataset API - 加载和解析数据 - 建立和加载 TFRecords - 图像转换第六章 Multi GPU - Multi-GPU基本操作 - 用Multi-GPU训练一个神经网络内容分析这个教程有基本的机器学习模型，也有深度学习的基本模型，包括现在流行的GAN，在模型方面比较全面。在数据集方面，第五章非常重要，虽然我们有标准的MNIST数据集，但是还是需要学会处理数据(包括预处理)，把数据整合成可以放在网络训练的格式。最后第四章，在可视化以及存储模型方面给出了例子。可以供大家学习使用，让TensorFlow的效率提高。总体而言，这个教程基础扎实和全面，非常适合新手。 *文末的那啥帮忙点一下~* 本文参与 [腾讯云自媒体同步曝光计划](https://cloud.tencent.com/developer/support-plan)，分享自微信公众号。原始发表：2019-08-20，如有侵权请联系 [cloudcommunity@tencent.com](mailto:cloudcommunity@tencent.com) 删除 [tensorflow](https://cloud.tencent.com/developer/tag/10150) [神经网络](https://cloud.tencent.com/developer/tag/10332) [机器学习](https://cloud.tencent.com/developer/tag/10149) [api](https://cloud.tencent.com/developer/tag/10292) [人工智能](https://cloud.tencent.com/developer/tag/10539) 本文分享自浊酒清味微信公众号，前往查看如有侵权，请联系 [cloudcommunity@tencent.com](mailto:cloudcommunity@tencent.com) 删除。本文参与 [腾讯云自媒体同步曝光计划](https://cloud.tencent.com/developer/support-plan) ，欢迎热爱写作的你一起参与！ [tensorflow](https://cloud.tencent.com/developer/tag/10150) [神经网络](https://cloud.tencent.com/developer/tag/10332) [机器学习](https://cloud.tencent.com/developer/tag/10149) [api](https://cloud.tencent.com/developer/tag/10292) [人工智能](https://cloud.tencent.com/developer/tag/10539) 评论登录后参与评论 0 条评论热度最新登录后参与评论推荐阅读领券 - 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