内容简介: 作为一本综合指南,本书将带领你探究TensorFlow 1.x的不错特性。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的不错特性,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。 目录: 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 handlin ...
以下为对购买帮助不大的评价