精通 TensorFlow 1.x 中文版（初稿）

迈不过友情╰ 2024-04-19 09:03 52阅读 0赞

*  [TensorFlow 101][]
    
     *  [什么是 TensorFlow？][TensorFlow]
     *  [TensorFlow 核心][TensorFlow 1]
     *  [代码预热 - Hello TensorFlow][- Hello TensorFlow]
     *  [张量][Link 1]
     *  [常量][Link 2]
     *  [操作][Link 3]
     *  [占位符][Link 4]
     *  [从 Python 对象创建张量][Python]
     *  [变量][Link 5]
     *  [从库函数生成的张量][Link 6]
     *  [使用相同的值填充张量元素][Link 7]
     *  [用序列填充张量元素][Link 8]
     *  [使用随机分布填充张量元素][Link 9]
     *  [使用`tf.get_variable()`获取变量][tf.get_variable]
     *  [数据流图或计算图][Link 10]
     *  [执行顺序和延迟加载][Link 11]
     *  [跨计算设备执行图 - CPU 和 GPU][- CPU _ GPU]
     *  [将图节点放置在特定的计算设备上][Link 12]
     *  [简单放置][Link 13]
     *  [动态展示位置][Link 14]
     *  [软放置][Link 15]
     *  [GPU 内存处理][GPU]
     *  [多个图][Link 16]
     *  [TensorBoard][]
     *  [TensorBoard 最小的例子][TensorBoard 1]
     *  [TensorBoard 详情][TensorBoard 2]
     *  [总结][Link 17]
 *  [TensorFlow 的高级库][TensorFlow 2]
    
     *  [TF Estimator - 以前的 TF 学习][TF Estimator - _ TF]
     *  [TF Slim][]
     *  [TFLearn][]
     *  [创建 TFLearn 层][TFLearn 1]
     *  [TFLearn 核心层][TFLearn 2]
     *  [TFLearn 卷积层][TFLearn 3]
     *  [TFLearn 循环层][TFLearn 4]
     *  [TFLearn 正则化层][TFLearn 5]
     *  [TFLearn 嵌入层][TFLearn 6]
     *  [TFLearn 合并层][TFLearn 7]
     *  [TFLearn 估计层][TFLearn 8]
     *  [创建 TFLearn 模型][TFLearn 9]
     *  [TFLearn 模型的类型][TFLearn 10]
     *  [训练 TFLearn 模型][TFLearn 11]
     *  [使用 TFLearn 模型][TFLearn 12]
     *  [PrettyTensor][]
     *  [Sonnet][]
     *  [总结][Link 18]
 *  [Keras 101][]
    
     *  [安装 Keras][Keras]
     *  [Keras 中的神经网络模型][Keras 1]
     *  [在 Keras 建立模型的工作流程][Keras 2]
     *  [创建 Keras 模型][Keras 3]
     *  [用于创建 Keras 模型的顺序 API][Keras _ API]
     *  [用于创建 Keras 模型的函数式 API][Keras _ API 1]
     *  [Keras 层][Keras 4]
     *  [Keras 核心层][Keras 5]
     *  [Keras 卷积层][Keras 6]
     *  [Keras 池化层][Keras 7]
     *  [Keras 本地连接层][Keras 8]
     *  [Keras 循环层][Keras 9]
     *  [Keras 嵌入层][Keras 10]
     *  [Keras 合并层][Keras 11]
     *  [Keras 高级激活层][Keras 12]
     *  [Keras 正则化层][Keras 13]
     *  [Keras 噪音层][Keras 14]
     *  [将层添加到 Keras 模型][Keras 15]
     *  [用于将层添加到 Keras 模型的顺序 API][Keras _ API 2]
     *  [用于向 Keras 模型添加层的函数式 API][Keras _ API 3]
     *  [编译 Keras 模型][Keras 16]
     *  [训练 Keras 模型][Keras 17]
     *  [使用 Keras 模型进行预测][Keras 18]
     *  [Keras 的附加模块][Keras 19]
     *  [MNIST 数据集的 Keras 序列模型示例][MNIST _ Keras]
     *  [总结][Link 19]
 *  [使用 TensorFlow 进行经典机器学习][TensorFlow 3]
    
     *  [简单的线性回归][Link 20]
     *  [数据准备][Link 21]
     *  [构建一个简单的回归模型][Link 22]
     *  [定义输入，参数和其他变量][Link 23]
     *  [定义模型][Link 24]
     *  [定义损失函数][Link 25]
     *  [定义优化器函数][Link 26]
     *  [训练模型][Link 27]
     *  [使用训练的模型进行预测][Link 28]
     *  [多元回归][Link 29]
     *  [正则化回归][Link 30]
     *  [套索正则化][Link 31]
     *  [岭正则化][Link 32]
     *  [ElasticNet 正则化][ElasticNet]
     *  [使用逻辑回归进行分类][Link 33]
     *  [二分类的逻辑回归][Link 34]
     *  [多类分类的逻辑回归][Link 35]
     *  [二分类][Link 36]
     *  [多类分类][Link 37]
     *  [总结][Link 38]
 *  [使用 TensorFlow 和 Keras 的神经网络和 MLP][TensorFlow _ Keras _ MLP]
    
     *  [感知机][Link 39]
     *  [多层感知机][Link 40]
     *  [用于图像分类的 MLP][MLP]
     *  [用于 MNIST 分类的基于 TensorFlow 的 MLP][MNIST _ TensorFlow _ MLP]
     *  [用于 MNIST 分类的基于 Keras 的 MLP][MNIST _ Keras _ MLP]
     *  [用于 MNIST 分类的基于 TFLearn 的 MLP][MNIST _ TFLearn _ MLP]
     *  [使用 TensorFlow，Keras 和 TFLearn 的 MLP 总结][TensorFlow_Keras _ TFLearn _ MLP]
     *  [用于时间序列回归的 MLP][MLP 1]
     *  [总结][Link 41]
 *  [使用 TensorFlow 和 Keras 的 RNN][TensorFlow _ Keras _ RNN]
    
     *  [简单循环神经网络][Link 42]
     *  [RNN 变种][RNN]
     *  [LSTM 网络][LSTM]
     *  [GRU 网络][GRU]
     *  [TensorFlow RNN][]
     *  [TensorFlow RNN 单元类][TensorFlow RNN 1]
     *  [TensorFlow RNN 模型构建类][TensorFlow RNN 2]
     *  [TensorFlow RNN 单元包装器类][TensorFlow RNN 3]
     *  [适用于 RNN 的 Keras][RNN _ Keras]
     *  [RNN 的应用领域][RNN 1]
     *  [用于 MNIST 数据的 Keras 中的 RNN][MNIST _ Keras _ RNN]
     *  [总结][Link 43]
 *  [使用 TensorFlow 和 Keras 的时间序列数据的 RNN][TensorFlow _ Keras _ RNN 1]
    
     *  [航空公司乘客数据集][Link 44]
     *  [加载 airpass 数据集][airpass]
     *  [可视化 airpass 数据集][airpass 1]
     *  [使用 TensorFlow RNN 模型预处理数据集][TensorFlow RNN 4]
     *  [TensorFlow 中的简单 RNN][TensorFlow _ RNN]
     *  [TensorFlow 中的 LSTM][TensorFlow _ LSTM]
     *  [TensorFlow 中的 GRU][TensorFlow _ GRU]
     *  [使用 Keras RNN 模型预处理数据集][Keras RNN]
     *  [使用 Keras 的简单 RNN][Keras _ RNN]
     *  [使用 Keras 的 LSTM][Keras _ LSTM]
     *  [使用 Keras 的 GRU][Keras _ GRU]
     *  [总结][Link 45]
 *  [使用 TensorFlow 和 Keras 的文本数据的 RNN][TensorFlow _ Keras _ RNN 2]
    
     *  [词向量表示][Link 46]
     *  [为 word2vec 模型准备数据][word2vec]
     *  [加载和准备 PTB 数据集][PTB]
     *  [加载和准备 text8 数据集][text8]
     *  [准备小验证集][Link 47]
     *  [使用 TensorFlow 的 skip-gram 模型][TensorFlow _ skip-gram]
     *  [使用 t-SNE 可视化单词嵌入][t-SNE]
     *  [keras 的 skip-gram 模型][keras _ skip-gram]
     *  [使用 TensorFlow 和 Keras 中的 RNN 模型生成文本][TensorFlow _ Keras _ RNN 3]
     *  [TensorFlow 中的 LSTM 文本生成][TensorFlow _ LSTM 1]
     *  [Keras 中的 LSTM 文本生成][Keras _ LSTM 1]
     *  [总结][Link 48]
 *  [使用 TensorFlow 和 Keras 的 CNN][TensorFlow _ Keras _ CNN]
    
     *  [理解卷积][Link 49]
     *  [了解池化][Link 50]
     *  [CNN 架构模式 - LeNet][CNN _ - LeNet]
     *  [用于 MNIST 数据的 LeNet][MNIST _ LeNet]
     *  [使用 TensorFlow 的用于 MNIST 的 LeNet CNN][TensorFlow _ MNIST _ LeNet CNN]
     *  [使用 Keras 的用于 MNIST 的 LeNet CNN][Keras _ MNIST _ LeNet CNN]
     *  [用于 CIFAR10 数据的 LeNet][CIFAR10 _ LeNet]
     *  [使用 TensorFlow 的用于 CIFAR10 的 ConvNets][TensorFlow _ CIFAR10 _ ConvNets]
     *  [使用 Keras 的用于 CIFAR10 的 ConvNets][Keras _ CIFAR10 _ ConvNets]
     *  [总结][Link 51]
 *  [使用 TensorFlow 和 Keras 的自编码器][TensorFlow _ Keras]
    
     *  [自编码器类型][Link 52]
     *  [TensorFlow 中的栈式自编码器][TensorFlow 4]
     *  [Keras 中的栈式自编码器][Keras 20]
     *  [TensorFlow 中的去噪自编码器][TensorFlow 5]
     *  [Keras 中的去噪自编码器][Keras 21]
     *  [TensorFlow 中的变分自编码器][TensorFlow 6]
     *  [Keras 中的变分自编码器][Keras 22]
     *  [总结][Link 53]
 *  [TF 服务：生产中的 TensorFlow 模型][TF _ TensorFlow]
    
     *  [在 TensorFlow 中保存和恢复模型][TensorFlow 7]
     *  [使用保护程序类保存和恢复所有图变量][Link 54]
     *  [使用保护程序类保存和恢复所选变量][Link 55]
     *  [保存和恢复 Keras 模型][Keras 23]
     *  [TensorFlow 服务][TensorFlow 8]
     *  [安装 TF 服务][TF]
     *  [保存 TF 服务的模型][TF 1]
     *  [提供 TF 服务模型][TF 2]
     *  [在 Docker 容器中提供 TF 服务][Docker _ TF]
     *  [安装 Docker][Docker]
     *  [为 TF 服务构建 Docker 镜像][TF _ Docker]
     *  [在 Docker 容器中提供模型][Docker 1]
     *  [Kubernetes 中的 TensorFlow 服务][Kubernetes _ TensorFlow]
     *  [安装 Kubernetes][Kubernetes]
     *  [将 Docker 镜像上传到 dockerhub][Docker _ dockerhub]
     *  [在 Kubernetes 部署][Kubernetes 1]
     *  [总结][Link 56]
 *  [迁移学习和预训练模型][Link 57]
    
     *  [ImageNet 数据集][ImageNet]
     *  [再训练或微调模型][Link 58]
     *  [COCO 动物数据集和预处理图像][COCO]
     *  [TensorFlow 中的 VGG16][TensorFlow _ VGG16]
     *  [使用 TensorFlow 中预训练的 VGG16 进行图像分类][TensorFlow _ VGG16 1]
     *  [TensorFlow 中的图像预处理，用于预训练的 VGG16][TensorFlow _ VGG16 2]
     *  [使用 TensorFlow 中的再训练的 VGG16 进行图像分类][TensorFlow _ VGG16 3]
     *  [Keras 的 VGG16][Keras _ VGG16]
     *  [使用 Keras 中预训练的 VGG16 进行图像分类][Keras _ VGG16 1]
     *  [使用 Keras 中再训练的 VGG16 进行图像分类][Keras _ VGG16 2]
     *  [TensorFlow 中的 Inception v3][TensorFlow _ Inception v3]
     *  [使用 TensorFlow 中的 Inception v3 进行图像分类][TensorFlow _ Inception v3 1]
     *  [使用 TensorFlow 中的再训练的 Inception v3 进行图像分类][TensorFlow _ Inception v3 2]
     *  [总结][Link 59]
 *  [深度强化学习][Link 60]
    
     *  [OpenAI Gym 101][]
     *  [将简单的策略应用于 cartpole 游戏][cartpole]
     *  [强化学习 101][101]
     *  [Q 函数（在模型不可用时学习优化）][Q]
     *  [RL 算法的探索与开发][RL]
     *  [V 函数（模型可用时学习优化）][V]
     *  [强化学习技巧][Link 61]
     *  [强化学习的朴素神经网络策略][Link 62]
     *  [实现 Q-Learning][Q-Learning]
     *  [Q-Learning 的初始化和离散化][Q-Learning 1]
     *  [使用 Q-Table 进行 Q-Learning][Q-Table _ Q-Learning]
     *  [Q-Network 或深 Q 网络（DQN）的 Q-Learning][Q-Network _ Q _DQN_ Q-Learning]
     *  [总结][Link 63]
 *  [生成性对抗网络][Link 64]
    
     *  [生成性对抗网络 101][101 1]
     *  [建立和训练 GAN 的最佳实践][GAN]
     *  [使用 TensorFlow 的简单的 GAN][TensorFlow _ GAN]
     *  [使用 Keras 的简单的 GAN][Keras _ GAN]
     *  [使用 TensorFlow 和 Keras 的深度卷积 GAN][TensorFlow _ Keras _ GAN]
     *  [总结][Link 65]
 *  [使用 TensorFlow 集群的分布式模型][TensorFlow 9]
    
     *  [分布式执行策略][Link 66]
     *  [TensorFlow 集群][TensorFlow 10]
     *  [定义集群规范][Link 67]
     *  [创建服务器实例][Link 68]
     *  [定义服务器和设备之间的参数和操作][Link 69]
     *  [定义并训练图以进行异步更新][Link 70]
     *  [定义并训练图以进行同步更新][Link 71]
     *  [总结][Link 72]
 *  [移动和嵌入式平台上的 TensorFlow 模型][TensorFlow 11]
    
     *  [移动平台上的 TensorFlow][TensorFlow 12]
     *  [Android 应用中的 TF Mobile][Android _ TF Mobile]
     *  [Android 上的 TF Mobile 演示][Android _ TF Mobile 1]
     *  [iOS 应用中的 TF Mobile][iOS _ TF Mobile]
     *  [iOS 上的 TF Mobile 演示][iOS _ TF Mobile 1]
     *  [TensorFlow Lite][]
     *  [Android 上的 TF Lite 演示][Android _ TF Lite]
     *  [iOS 上的 TF Lite 演示][iOS _ TF Lite]
     *  [总结][Link 73]
 *  [R 中的 TensorFlow 和 Keras][R _ TensorFlow _ Keras]
    
     *  [在 R 中安装 TensorFlow 和 Keras 软件包][R _ TensorFlow _ Keras 1]
     *  [R 中的 TF 核心 API][R _ TF _ API]
     *  [R 中的 TF 估计器 API][R _ TF _ API 1]
     *  [R 中的 Keras API][R _ Keras API]
     *  [R 中的 TensorBoard][R _ TensorBoard]
     *  [R 中的 tfruns 包][R _ tfruns]
     *  [总结][Link 74]
 *  [调试 TensorFlow 模型][TensorFlow 13]
    
     *  [使用`tf.Session.run()`获取张量值][tf.Session.run]
     *  [使用`tf.Print()`打印张量值][tf.Print]
     *  [用`tf.Assert()`断言条件][tf.Assert]
     *  [使用 TensorFlow 调试器（tfdbg）进行调试][TensorFlow _tfdbg]
     *  [总结][Link 75]
 *  [张量处理单元][Link 76]

[TensorFlow 101]: https://gitee.com/it-ebooks/dsai-trans-proj/blob/master/mastering-tf-1x-zh/8.md
[TensorFlow]: https://gitee.com/it-ebooks/dsai-trans-proj/blob/master/mastering-tf-1x-zh/9.md
[TensorFlow 1]: https://gitee.com/it-ebooks/dsai-trans-proj/blob/master/mastering-tf-1x-zh/10.md
[- Hello TensorFlow]: https://gitee.com/it-ebooks/dsai-trans-proj/blob/master/mastering-tf-1x-zh/11.md
[Link 1]: https://gitee.com/it-ebooks/dsai-trans-proj/blob/master/mastering-tf-1x-zh/12.md
[Link 2]: https://gitee.com/it-ebooks/dsai-trans-proj/blob/master/mastering-tf-1x-zh/13.md
[Link 3]: https://gitee.com/it-ebooks/dsai-trans-proj/blob/master/mastering-tf-1x-zh/14.md
[Link 4]: https://gitee.com/it-ebooks/dsai-trans-proj/blob/master/mastering-tf-1x-zh/15.md
[Python]: https://gitee.com/it-ebooks/dsai-trans-proj/blob/master/mastering-tf-1x-zh/16.md
[Link 5]: https://gitee.com/it-ebooks/dsai-trans-proj/blob/master/mastering-tf-1x-zh/17.md
[Link 6]: https://gitee.com/it-ebooks/dsai-trans-proj/blob/master/mastering-tf-1x-zh/18.md
[Link 7]: https://gitee.com/it-ebooks/dsai-trans-proj/blob/master/mastering-tf-1x-zh/19.md
[Link 8]: https://gitee.com/it-ebooks/dsai-trans-proj/blob/master/mastering-tf-1x-zh/20.md
[Link 9]: https://gitee.com/it-ebooks/dsai-trans-proj/blob/master/mastering-tf-1x-zh/21.md
[tf.get_variable]: https://gitee.com/it-ebooks/dsai-trans-proj/blob/master/mastering-tf-1x-zh/22.md
[Link 10]: https://gitee.com/it-ebooks/dsai-trans-proj/blob/master/mastering-tf-1x-zh/23.md
[Link 11]: https://gitee.com/it-ebooks/dsai-trans-proj/blob/master/mastering-tf-1x-zh/24.md
[- CPU _ GPU]: https://gitee.com/it-ebooks/dsai-trans-proj/blob/master/mastering-tf-1x-zh/25.md
[Link 12]: https://gitee.com/it-ebooks/dsai-trans-proj/blob/master/mastering-tf-1x-zh/26.md
[Link 13]: https://gitee.com/it-ebooks/dsai-trans-proj/blob/master/mastering-tf-1x-zh/27.md
[Link 14]: https://gitee.com/it-ebooks/dsai-trans-proj/blob/master/mastering-tf-1x-zh/28.md
[Link 15]: https://gitee.com/it-ebooks/dsai-trans-proj/blob/master/mastering-tf-1x-zh/29.md
[GPU]: https://gitee.com/it-ebooks/dsai-trans-proj/blob/master/mastering-tf-1x-zh/30.md
[Link 16]: https://gitee.com/it-ebooks/dsai-trans-proj/blob/master/mastering-tf-1x-zh/31.md
[TensorBoard]: https://gitee.com/it-ebooks/dsai-trans-proj/blob/master/mastering-tf-1x-zh/32.md
[TensorBoard 1]: https://gitee.com/it-ebooks/dsai-trans-proj/blob/master/mastering-tf-1x-zh/33.md
[TensorBoard 2]: https://gitee.com/it-ebooks/dsai-trans-proj/blob/master/mastering-tf-1x-zh/34.md
[Link 17]: https://gitee.com/it-ebooks/dsai-trans-proj/blob/master/mastering-tf-1x-zh/35.md
[TensorFlow 2]: https://gitee.com/it-ebooks/dsai-trans-proj/blob/master/mastering-tf-1x-zh/36.md
[TF Estimator - _ TF]: https://gitee.com/it-ebooks/dsai-trans-proj/blob/master/mastering-tf-1x-zh/37.md
[TF Slim]: https://gitee.com/it-ebooks/dsai-trans-proj/blob/master/mastering-tf-1x-zh/38.md
[TFLearn]: https://gitee.com/it-ebooks/dsai-trans-proj/blob/master/mastering-tf-1x-zh/39.md
[TFLearn 1]: https://gitee.com/it-ebooks/dsai-trans-proj/blob/master/mastering-tf-1x-zh/40.md
[TFLearn 2]: https://gitee.com/it-ebooks/dsai-trans-proj/blob/master/mastering-tf-1x-zh/41.md
[TFLearn 3]: https://gitee.com/it-ebooks/dsai-trans-proj/blob/master/mastering-tf-1x-zh/42.md
[TFLearn 4]: https://gitee.com/it-ebooks/dsai-trans-proj/blob/master/mastering-tf-1x-zh/43.md
[TFLearn 5]: https://gitee.com/it-ebooks/dsai-trans-proj/blob/master/mastering-tf-1x-zh/44.md
[TFLearn 6]: https://gitee.com/it-ebooks/dsai-trans-proj/blob/master/mastering-tf-1x-zh/45.md
[TFLearn 7]: https://gitee.com/it-ebooks/dsai-trans-proj/blob/master/mastering-tf-1x-zh/46.md
[TFLearn 8]: https://gitee.com/it-ebooks/dsai-trans-proj/blob/master/mastering-tf-1x-zh/47.md
[TFLearn 9]: https://gitee.com/it-ebooks/dsai-trans-proj/blob/master/mastering-tf-1x-zh/48.md
[TFLearn 10]: https://gitee.com/it-ebooks/dsai-trans-proj/blob/master/mastering-tf-1x-zh/49.md
[TFLearn 11]: https://gitee.com/it-ebooks/dsai-trans-proj/blob/master/mastering-tf-1x-zh/50.md
[TFLearn 12]: https://gitee.com/it-ebooks/dsai-trans-proj/blob/master/mastering-tf-1x-zh/51.md
[PrettyTensor]: https://gitee.com/it-ebooks/dsai-trans-proj/blob/master/mastering-tf-1x-zh/52.md
[Sonnet]: https://gitee.com/it-ebooks/dsai-trans-proj/blob/master/mastering-tf-1x-zh/53.md
[Link 18]: https://gitee.com/it-ebooks/dsai-trans-proj/blob/master/mastering-tf-1x-zh/54.md
[Keras 101]: https://gitee.com/it-ebooks/dsai-trans-proj/blob/master/mastering-tf-1x-zh/55.md
[Keras]: https://gitee.com/it-ebooks/dsai-trans-proj/blob/master/mastering-tf-1x-zh/56.md
[Keras 1]: https://gitee.com/it-ebooks/dsai-trans-proj/blob/master/mastering-tf-1x-zh/57.md
[Keras 2]: https://gitee.com/it-ebooks/dsai-trans-proj/blob/master/mastering-tf-1x-zh/58.md
[Keras 3]: https://gitee.com/it-ebooks/dsai-trans-proj/blob/master/mastering-tf-1x-zh/59.md
[Keras _ API]: https://gitee.com/it-ebooks/dsai-trans-proj/blob/master/mastering-tf-1x-zh/60.md
[Keras _ API 1]: https://gitee.com/it-ebooks/dsai-trans-proj/blob/master/mastering-tf-1x-zh/61.md
[Keras 4]: https://gitee.com/it-ebooks/dsai-trans-proj/blob/master/mastering-tf-1x-zh/62.md
[Keras 5]: https://gitee.com/it-ebooks/dsai-trans-proj/blob/master/mastering-tf-1x-zh/63.md
[Keras 6]: https://gitee.com/it-ebooks/dsai-trans-proj/blob/master/mastering-tf-1x-zh/64.md
[Keras 7]: https://gitee.com/it-ebooks/dsai-trans-proj/blob/master/mastering-tf-1x-zh/65.md
[Keras 8]: https://gitee.com/it-ebooks/dsai-trans-proj/blob/master/mastering-tf-1x-zh/66.md
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[R _ TensorFlow _ Keras]: https://gitee.com/it-ebooks/dsai-trans-proj/blob/master/mastering-tf-1x-zh/245.md
[R _ TensorFlow _ Keras 1]: https://gitee.com/it-ebooks/dsai-trans-proj/blob/master/mastering-tf-1x-zh/246.md
[R _ TF _ API]: https://gitee.com/it-ebooks/dsai-trans-proj/blob/master/mastering-tf-1x-zh/247.md
[R _ TF _ API 1]: https://gitee.com/it-ebooks/dsai-trans-proj/blob/master/mastering-tf-1x-zh/248.md
[R _ Keras API]: https://gitee.com/it-ebooks/dsai-trans-proj/blob/master/mastering-tf-1x-zh/249.md
[R _ TensorBoard]: https://gitee.com/it-ebooks/dsai-trans-proj/blob/master/mastering-tf-1x-zh/250.md
[R _ tfruns]: https://gitee.com/it-ebooks/dsai-trans-proj/blob/master/mastering-tf-1x-zh/251.md
[Link 74]: https://gitee.com/it-ebooks/dsai-trans-proj/blob/master/mastering-tf-1x-zh/252.md
[TensorFlow 13]: https://gitee.com/it-ebooks/dsai-trans-proj/blob/master/mastering-tf-1x-zh/253.md
[tf.Session.run]: https://gitee.com/it-ebooks/dsai-trans-proj/blob/master/mastering-tf-1x-zh/254.md
[tf.Print]: https://gitee.com/it-ebooks/dsai-trans-proj/blob/master/mastering-tf-1x-zh/255.md
[tf.Assert]: https://gitee.com/it-ebooks/dsai-trans-proj/blob/master/mastering-tf-1x-zh/256.md
[TensorFlow _tfdbg]: https://gitee.com/it-ebooks/dsai-trans-proj/blob/master/mastering-tf-1x-zh/257.md
[Link 75]: https://gitee.com/it-ebooks/dsai-trans-proj/blob/master/mastering-tf-1x-zh/258.md
[Link 76]: https://gitee.com/it-ebooks/dsai-trans-proj/blob/master/mastering-tf-1x-zh/259.md