{"id":20673024,"url":"https://github.com/relph1119/deeplearning-with-tensorflow-notes","last_synced_at":"2025-08-20T08:31:35.875Z","repository":{"id":61964400,"uuid":"240449020","full_name":"Relph1119/deeplearning-with-tensorflow-notes","owner":"Relph1119","description":"龙曲良《TensorFlow深度学习》学习笔记及代码，采用TensorFlow2.0.0版本","archived":false,"fork":false,"pushed_at":"2023-02-16T00:40:06.000Z","size":10425,"stargazers_count":171,"open_issues_count":12,"forks_count":54,"subscribers_count":4,"default_branch":"master","last_synced_at":"2024-12-12T08:36:40.513Z","etag":null,"topics":["deep-learning","tensorflow"],"latest_commit_sha":null,"homepage":"","language":"Jupyter 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Notebook","funding_links":[],"categories":[],"sub_categories":[],"readme":"# TensorFlow深度学习练习代码\n\u0026emsp;\u0026emsp;龙龙（龙曲良）老师的《TensorFlow深度学习》是TensorFlow2.0入门教材之一。  \n\u0026emsp;\u0026emsp;本书共15章，大体上可分为4个部份：第1-3章为第1部分，主要介绍人工智能的初步认知，并引出相关问题；第4-5章为第2部分，主要介绍TensorFlow相关基础，为后续算法实现铺垫；第6-9章为第3部分，主要介绍神经网络的核心理论和共性知识，让读者理解深度学习的本质；第10-15章为模型算法应用部分，主要介绍常见的算法与模型，让读者能够学有所用。  \n\u0026emsp;\u0026emsp;**申明：** 所有的代码都来源于《TensorFlow深度学习》，github地址：https://github.com/dragen1860/Deep-Learning-with-TensorFlow-book。\n\n## 使用说明\n1. 本练习代码是搭配龙龙老师的《TensorFlow深度学习》一书。\n2. 关于本笔记中的练习代码，已经消缺了书中代码的错误，可以很方便地执行程序。  \n3. 关于书中的很多图，已经写好了生成数据图的代码，在对应的目录下也有数据图。\n4. 关于书中很多用jupyter notebook写的代码示例，也在对应的目录下有对应章节的ipynb文档。\n5. 关于python包的版本问题，请详见requirements.txt文件，笔者采用的tensorflow-gpu==2.0.0，可以使用cpu版本，但是运行会特别慢。\n6. keras模型与数据下载地址：链接：https://pan.baidu.com/s/1Rt6KYWUAQ8MWKY9UVVDtmQ 提取码：wedp  \n7. 相关数据集和gym包，百度网盘的下载地址：链接：https://pan.baidu.com/s/1fZ748Xz3WrgQnIaxGsrZLQ，提取码：ea6u  \n\u003cimg src=\"./res/keras-dataset.png\" width=\"935\"\u003e\n\n\u0026emsp;\u0026emsp;使用windows平台的tensorflow，将keras中的datasets和models放入到C:\\\\Users\\\\{pcUserName}\\\\.keras路径下，其他的数据包，在对应的练习代码中有说明。\n\n## 选用的《TensorFlow深度学习》版本\n\u003cimg src=\"./res/deeplearning-with-tensorflow-book.jpg\" width=\"232\"\u003e\n\n\n\u003e 书名：TensorFlow深度学习\u003cbr/\u003e\n\u003e 作者：龙龙老师\u003cbr/\u003e\n\u003e 版次：2019年12月05日测试版第2版\u003cbr/\u003e\n\n电子书（带书签-无水印版）的百度网盘地址：链接：https://pan.baidu.com/s/1CPXZSrqVTJWHc3cYXIYjNg，提取码：mrhw\n\n## 主要贡献者（按首字母排名）\n[@胡锐锋-天国之影-Relph](https://github.com/Relph1119)\n\n## 总结\n\u0026emsp;\u0026emsp;本书总共用了16天（2020年2月14日-2020年3月1日）阅读完，对TensorFlow和Keras的使用有很大的收获，其中第11、13章和第15章的scratch训练，由于电脑的显卡不好，不能完成练习，但其他章节的练习均已完成。  \n  \n**注意：** 如果出现以下这个错误，说明显卡的显存太低，可以将代码和数据集放到Google Colab上面执行。\n\u003e tensorflow.python.framework.errors_impl.ResourceExhaustedError: OOM when allocating tensor with shape[500,500,500] and type float on /job:localhost/replica:0/task:0/device:GPU:0 by allocator GPU_0_bfc [Op:Sub] name: sub/  \n\n## LICENSE\n[GNU General Public License v3.0](https://github.com/relph1119/deeplearning-with-tensorflow-notes/blob/master/LICENSE)","project_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2Frelph1119%2Fdeeplearning-with-tensorflow-notes","html_url":"https://awesome.ecosyste.ms/projects/github.com%2Frelph1119%2Fdeeplearning-with-tensorflow-notes","lists_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2Frelph1119%2Fdeeplearning-with-tensorflow-notes/lists"}