精通TensorFlow1.x(影印版)(英文版)

作为一本综合指南，本书将带领你探究 TensorFlow 1.x的高级特性。深入了解TensorFlow Core、Keras、TF Estimators、TFLearn、TF-Slim 、Pretty Tensor以及Sonnet。通过TensorFlow和 Keras的强大功能，利用转移学习、生成式对抗网络、 深度强化学习等概念构建深度学习模型。在本书中， 你将获得各种数据集（如MNIST、CIFAR-10、PTB、 text8、COCO-Images）的实践经验。你将学习到 TensorFlow1.x的高级特性，例如带有TF-Clusters的 分布式TensorFlow、使用TensorFlow Serving部署生 产模型、在Android和iOS平台上为移动和嵌入式设备 构建和部署TensorFlow模型。你还会看到如何在R统计 软件中调用TensorFlow和Keras API，了解在基于 TensorFlow API的代码无法按预期工作时所需的调试 技术。

Preface Chapter 1: TensorFlow 101 What is TensorFIow? TensorFlow core Code warm-up - Hello TensorFIow Tensors Constants Operations Placeholders Creating tensors from Python objects Variables Tensors generated from library functions Populating tensor elements with the same values Populating tensor elements with sequences Populating tensor elements with a random distribution Getting Variables with tf.get_variable（） Data flow graph or computation graph Order of execution and lazy loading Executing graphs across compute devices - CPU and GPGPU Placing graph nodes on specific compute devices Simple placement Dynamic placement Soft placement GPU memory handling Multiple graphs TensorBoard A TensorBoard minimal example TensorBoard details Summary Chapter 2: High-Level Libraries for TensorFlow TF Estimator - previously TF Learn TF Slim TFLearn Creating the TFLearn Layers TFLearn core layers TFLearn convolutional layers TFLearn recurrent layers TFLearn normalization layers TFLearn embedding layers TFLearn merge layers TFLearn estimator layers Creating the TFLearn Model Types of TFLearn models Training the TFLearn Model Using the TFLearn Model PrettyTensor Sonnet Summary Chapter 3: Keras 101 Installing Keras