Mastering.TensorFlow.1.x

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tensorflow最新教程,packt2018年出版。Packt.Mastering.TensorFlow.1.x.2018.1.pdf
Mastering Tensor Flow 1.X Copyright o 2018 Packt Publishing All rights reserved. No part of this book may be reproduced, stored in a retrieval system, or transmitted in any form or by any means, without the prior written permission of the publisher, except in the case of brief quotations embedded in critical articles or reviews Every effort has been made in the preparation of this book to ensure the accuracy of the information presented However, the information contained in this book is sold without warranty, either express or implied. Neither the authors, nor Packt Publishing or its dealers and distributors, will be held liable for any damages caused or alleged to have been caused directly or indirectly by this book Packt Publishing has endeavored to provide trademark information about all of the companies and products mentioned in this book by the appropriate use of capitals. However, Packt Publishing cannot guarantee the accuracy of this information Commissioning Editor: Sunith Shetty Acquisition Editor: Tushar gupta Content Development editor: Tejas limkar Technical Editor: Danish shaikh Copy Editors: Safis Editing Project Coordinator: Manthan Patel Proofreader: Safis Editing Indexer: Rekha nair Graphics: Tania Dutta Production Coordinator: Aparna Bhagat First published January 2018 Production reference: 1190118 Published by Packt Publishing Ltd Livery place 35 Livery Street Birmingham B 3 2PB, UK ISBN978-1-78829206-1 www.packtpub.com Mapt mapt. io Mapt is an online digital library that gives you full access to over 5,000 books and videos, as well as industry leading tools to help you plan your personal development and advance our career. For more information please visit our website Why subscribe? Spend less time learning and more time coding with practical eBooks and Videos from over 4, 000 industry professionals Improve your learning with skill plans built especially for you Get a free eBook or video every month Mapt is fully searchable Copy and paste, print, and bookmark content PacktPub, com Did you know that packt offers e Book versions of every book published, with PdF and epubfilesavailableYoucanupgradetotheeboOkversionatwww.packtpub.comandasa print book customer, you are entitled to a discount on the e book copy. Get in touch with us at service@packtpub com for more details Atwww.packtpub.comyoucanalsoreadacollectionoffreetechnicalarticlessignupfora range of free newsletters and receive exclusive discounts and offers on packt books and eBooKs Foreword TensorFlow and Keras are a key part of the"Data Science for Internet of Things"course, which i teach at the university of Oxford My TensorFlow journey started with Keras O time,in our course, we increasingly gravitated towards core TensorFlow in addition tover Keras. I believe many people's Tensor Flow journey'will follow this trajectory Armando Fandango's book" Mastering TensorFlow 1 x"provides a road map for this journey. The book is an ambitious undertaking interweaving Keras and core TensorFlow libraries. It delves into complex themes and libraries such as sonnet distributed TensorFlow with TF Clusters, deploying production models with TensorFlow Serving TensorFlow mobile, and tensor Flow for embedded devices In that sense, this is an advanced book. But the author covers deep learning models such as RNN, CNN, autoencoders, generative adversarial models, and deep reinforcement learning through Keras. Armando has clearly drawn upon his experience to make this complex Journey easier for readers look forward to increased adoption of this book and learning from it Ajit Jaokar Data Science for loT Course creator and lead tutor at the University of Oxford/ Principal Data Scientist Contributors About the author Armando Fandango creates Al-empowered products by leveraging his expertise in deep learning, computational methods, and distributed computing. He advises Owen. ai Inc on AI product strategy. He founded NeuraSights Inc with the goal of creating insights using neural networks. He is the founder of Vets2Data Inc, a non-profit organization assisting US military veterans in building ai skill Armando has authored books titled Python Data Analysis -2nd Edition and Mastering TensorFlow and published research in international journals and conferences I would like to thank dr. Paul wiegand (uce), Dr Brian goldiez (ucf, Tejas limkar (Packt), and Tushar Gupta (Packt) for being able to complete this book. This work would not be possible without their inspiration About the reviewer Nick Mcclure is currently a senior data scientist at PayScale Inc in Seattle, Washington, USA. Previously he worked at Zillow and Caesar's entertainment. he has degrees in applied mathematics from the University of Montana and the College of Saint Benedict and Saint John's University He has also authored TensorFlow Machine Learning Cookbook by Packt He has a passion for learning and advocating for analytics, machine learning, and artificial intelligence. he occasionally puts his thoughts and musings on his blog, fromdata. org, or through his twitter account at anfmcclure Packt is searching for authors like you If you're interested in becoming an author for Packt, please visit authors. packtpub com and apply today. We have worked with thousands of developers and tech professionals, just like you, to help them share their insight with the global tech community. You can make a general application, apply for a specific hot topic that we are recruiting an author for, or submit your own idea Table of contents Preface Chapter 1: TensorFlow 101 What is TensorFlow? TensorFlow core 7899 Code warm-up-Hello TensorFlow Tensors 10 Constants 12 Operations 14 Placeholders 15 Creating tensors from Python objects Variables 19 Tensors generated from library functions Populating tensor elements with the same values 21 Populating tensor elements with sequences 22 Populating tensor elements with a random distribution Getting Variables with tf. get variableo 24 Data flow graph or computation graph 25 Order of execution and lazy loading 27 Executing graphs across compute devices-CPU and GPGPU 27 Placing graph nodes on specific compute devices 29 Simple placement 31 Dynamic placement 31 Soft placement GPU memory handling 32 Multiple graphs 33 Tensor board 33 A Tensor Board minimal example Tensor board details 36 Summary 37 Chapter 2: High-Level Libraries for TensorFlow 38 TF Estimator- previously TF Learn 39 TE SIim 2 RELearn 44 Creating the TFLearn Layers 45 TFLearn core layers 45 Table of Contents FLearn convolutional layers 46 TFLearn recurrent layers 47 TFLearn normalization layers 47 TFLearn embedding layers 47 LEarn merge layers 48 TFLearn estimator layers 48 Creating the TFLearn model 50 Types of TFLearn models 50 Training the TFLearn Model 50 Using the tflearn model 51 Pretty Tensor 51 Sonnet 53 Summary 55 Chapter 3: Keras 101 57 Installing Keras 58 Neural Network Models in Keras 58 Workflow for building models in Keras 58 Creating the Keras model 59 Sequential aPi for creating the Keras model 59 Functional aPI for creating the Keras model 59 Keras Layers 60 Keras core layers 60 Keras convolutional layers 61 Keras pooling layers 62 Keras locally-connected layers 63 Keras recurrent layers 63 Keras embedding layers 63 Keras merge layers Keras advanced activation layers 64 Keras normalization layers 65 Keras noise layers 65 Adding Layers to the Keras Model 65 Sequential aPI to add layers to the Keras model 66 Functional aPi to add layers to the Keras model 66 Compiling the Keras model Training the Keras model 68 Predicting with the Keras model 68 Additional modules in Keras 69 Keras sequential model example for Mnist dataset 70 Summary 72 [i] Table of Contents Chapter 4: classical Machine learning with TensorFlow Simple linear regression 76 Data preparation 76 Building a simple regression model Defining the inputs, parameters, and other variables 78 Defining the model 78 Defining the loss function 79 Defining the optimizer function 80 Training the model 80 Using the trained model to predict 85 Multi-regression 85 Regularized regression 89 Lasso regularization 91 Ridge regularization ElasticNet regularization 8 Classification using logistic regression 99 Logistic regression for binary classification 100 Logistic regression for multiclass classification Binary classification 102 Multiclass classification 106 Summary 111 Chapter 5: Neural Networks and MLP with TensorFlow and Keras 112 The perceptron 113 MultiLayer Perceptron 115 MLP for image classification 117 TensorFlow-based mlp for mnist classification 117 Keras-based mlp for mnist classification 125 TFLearn-based mlp for mnist classification 128 Summary of MLP with TensorFlow, Keras, and TFLearn 129 MLP for time series regression 130 Summary 134 Chapter 6: RNN with TensorFlow and Keras 135 Simple Recurrent Neural Network 136 RNN variants 139 LSTM network 140 GRU network 143 Tensorflow for rnn 144 Tensor Flow rnn cell classes 145

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习可染 还可以,算是对tensorlfow 1.x版本的一个很好的参考
2021-04-28
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