实例介绍
【实例简介】
机器学习，tensorflow，不错的资料。机器学习，tensorflow，不错的资料。机器学习，tensorflow，不错的资料。
Building Machine Learning Projects with TensorFlow Copyright o 2016 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 author, nor Packt Publishing, and its dealers and distributors will be held liable for any damages caused or alleged to be 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 First published: November 2016 Production reference: 2220317 Published by Packt Publishing Ltd Livery Place 35 Livery Street Birmingham B3 2PB. UK ISBN978-1-78646-658-7 www.packtpub.com Credits uthor Copy Editor Rodolfo bonnin Safis Editing Reviewer Project Coordinator Niko gamulin Nidhi joshi Commissioning editor Proofreader eena lagare Safis Editing Acquisition editor Indexer Namrata patil Mariamman Chettiyar Content Development Editor Graphics Siddhesh salvi Disha haria Technical editor Production coordinator Danish shaikh Arvindkumar gupta Dharmendra yadav About the author Rodolfo bonnin is a systems engineer and PhD student at Universidad Tecnologica Nacional, Argentina. He also pursued parallel programming and image understanding postgraduate courses at Uni stuttgart, Germany le has done research on high performance computing since 2005 and began studying and implementing convolutional neural networks in 2008, writing a CPU and GPU-supporting neural network feed forward stage. more recently he's been working in the field of fraud pattern detection with Neural Networks, and is currently working on signal classification using ml techniques To my wife and kids and the patience they demonstrated during the writing of this book Also to the reviewers, who helped give professionalism to this work, and marcos boaglio for facilitating equipment to cover the installation chapter. Ad maiorem Dei gloriam About the reviewer Niko Gamulin is a senior software engineer at CloudMondo, a US-based startup, where he develops and implements predictive behavior models for humans and systems. Previously he has developed deep learning models to solve various challenges he received his phd in Electrical Engineering from University of Ljubljana in 2015. His research focused on creation of machine learning models for churn prediction I would like to thank my wonderful daughter Agata, who inspires me to gain more understanding about the learning process and Ana for being the best wife in the world Www.pacKtpub.com Forsupportfilesanddownloadsrelatedtoyourbook,pleasevisitwww.packtpub.cOm Did you know that packt offers ebook versions of every book published, with pdf and epubfilesavailableyoUcanupgradetotheebooKversionatwww.packtpub.comandasa print book customer, you are entitled to a discount on the eBook 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 B eBooKs Mapt https://www.packtpub.com/mapt Get the most in-demand software skills with Mapt. Mapt gives you full access to all Packt books and video courses, as well as industry-leading tools to help you plan your personal development and advance your career Why subscribe? Fully searchable across every book published by Packt Copy and paste, print, and bookmark content On demand and accessible via a web browser Customer Feedback Thanks for purchasing this Packt book. At Packt, quality is at the heart of our editorial process. To help us improve, please leave us an honest review on this book's Amazon page athttps://www.amazoncom/dp/1786466589/ If you'd like to join our team of regular reviewers you can e-mail us at customerreviewsepacktpub com We award our regular reviewers with free e Books and videos in exchange for their valuable feedback. Help us be relentless in improving our products Table of contents Preface Chapter 1: Exploring and Transforming Data TensorFlow' s main data structure tensors 8 Tensor properties-ranks, shapes, and types 8 ensor rank 8 Tensor shape data types 10 Creating new tensors 10 From numpy to tensors and vice versa Getting things done-interacting with TensorFlow 11 Handling the computing workflow -TensorFlow's data flow graph 12 Computation graph building 13 Useful operation object methods 14 Feeding 14 Variables 14 Variable initialization 14 Saving data flow graphs 15 Graph serialization language-protocol buffers 15 Useful methods 15 Example graph building 15 Running our programs-Sessions 19 Basic tensor methods 20 Simple matrix operations 20 Reduction Tensor segmentation 22 Sequences 24 Tensor shape transformations 25 Tensor slicing and joining 26 Dataflow structure and results visualization - Tensor board 28 Command line use 29 How Tensor board works 29 Adding Summary nodes 30 Common Summary operations 31 Special Summary functions 31 Interacting with TensorBoard's GUI 32 Reading information from disk 33 abulated formats-csV 33 The Iris dataset 33 Reading image data 35 Loading and processing the images 36 Reading from the standard tensorFlow format Summary Chapter 2: Clustering Learning from data-unsupervised learning 38 Clustering 39 k-means 40 Mechanics of k-means 40 Algorithm iteration criterion 40 (-means algorithm breakdown Pros and cons of k-means 42 k-nearest neighbors 43 Mechanics of k-nearest neighbors 44 Pros and cons of k-nn 45 Practical examples for Useful libraries 45 matplotlib plotting library 45 Sample synthetic data plotting scikit-learn dataset module About the scikit-learn library 47 Synthetic dataset types 47 Blobs dataset 48 Employed method 8 Circle dataset 48 Employed method 48 Moon dataset 48 Project 1-k-means clustering on synthetic datasets Dataset description and loading 49 Generating the dataset 49 odel architecture LosS function description and optimizer loop 50 Stop condition 51 Results description 51 Full source code 52 k-means on circle synthetic data Project 2-nearest neighbor on synthetic datasets 56 Dataset generation 56 Model architecture Loss function description 57 Stop condition 57 Results description 57 Full source code 58 [i] 【实例截图】
【核心代码】
机器学习，tensorflow，不错的资料。机器学习，tensorflow，不错的资料。机器学习，tensorflow，不错的资料。
Building Machine Learning Projects with TensorFlow Copyright o 2016 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 author, nor Packt Publishing, and its dealers and distributors will be held liable for any damages caused or alleged to be 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 First published: November 2016 Production reference: 2220317 Published by Packt Publishing Ltd Livery Place 35 Livery Street Birmingham B3 2PB. UK ISBN978-1-78646-658-7 www.packtpub.com Credits uthor Copy Editor Rodolfo bonnin Safis Editing Reviewer Project Coordinator Niko gamulin Nidhi joshi Commissioning editor Proofreader eena lagare Safis Editing Acquisition editor Indexer Namrata patil Mariamman Chettiyar Content Development Editor Graphics Siddhesh salvi Disha haria Technical editor Production coordinator Danish shaikh Arvindkumar gupta Dharmendra yadav About the author Rodolfo bonnin is a systems engineer and PhD student at Universidad Tecnologica Nacional, Argentina. He also pursued parallel programming and image understanding postgraduate courses at Uni stuttgart, Germany le has done research on high performance computing since 2005 and began studying and implementing convolutional neural networks in 2008, writing a CPU and GPU-supporting neural network feed forward stage. more recently he's been working in the field of fraud pattern detection with Neural Networks, and is currently working on signal classification using ml techniques To my wife and kids and the patience they demonstrated during the writing of this book Also to the reviewers, who helped give professionalism to this work, and marcos boaglio for facilitating equipment to cover the installation chapter. Ad maiorem Dei gloriam About the reviewer Niko Gamulin is a senior software engineer at CloudMondo, a US-based startup, where he develops and implements predictive behavior models for humans and systems. Previously he has developed deep learning models to solve various challenges he received his phd in Electrical Engineering from University of Ljubljana in 2015. His research focused on creation of machine learning models for churn prediction I would like to thank my wonderful daughter Agata, who inspires me to gain more understanding about the learning process and Ana for being the best wife in the world Www.pacKtpub.com Forsupportfilesanddownloadsrelatedtoyourbook,pleasevisitwww.packtpub.cOm Did you know that packt offers ebook versions of every book published, with pdf and epubfilesavailableyoUcanupgradetotheebooKversionatwww.packtpub.comandasa print book customer, you are entitled to a discount on the eBook 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 B eBooKs Mapt https://www.packtpub.com/mapt Get the most in-demand software skills with Mapt. Mapt gives you full access to all Packt books and video courses, as well as industry-leading tools to help you plan your personal development and advance your career Why subscribe? Fully searchable across every book published by Packt Copy and paste, print, and bookmark content On demand and accessible via a web browser Customer Feedback Thanks for purchasing this Packt book. At Packt, quality is at the heart of our editorial process. To help us improve, please leave us an honest review on this book's Amazon page athttps://www.amazoncom/dp/1786466589/ If you'd like to join our team of regular reviewers you can e-mail us at customerreviewsepacktpub com We award our regular reviewers with free e Books and videos in exchange for their valuable feedback. Help us be relentless in improving our products Table of contents Preface Chapter 1: Exploring and Transforming Data TensorFlow' s main data structure tensors 8 Tensor properties-ranks, shapes, and types 8 ensor rank 8 Tensor shape data types 10 Creating new tensors 10 From numpy to tensors and vice versa Getting things done-interacting with TensorFlow 11 Handling the computing workflow -TensorFlow's data flow graph 12 Computation graph building 13 Useful operation object methods 14 Feeding 14 Variables 14 Variable initialization 14 Saving data flow graphs 15 Graph serialization language-protocol buffers 15 Useful methods 15 Example graph building 15 Running our programs-Sessions 19 Basic tensor methods 20 Simple matrix operations 20 Reduction Tensor segmentation 22 Sequences 24 Tensor shape transformations 25 Tensor slicing and joining 26 Dataflow structure and results visualization - Tensor board 28 Command line use 29 How Tensor board works 29 Adding Summary nodes 30 Common Summary operations 31 Special Summary functions 31 Interacting with TensorBoard's GUI 32 Reading information from disk 33 abulated formats-csV 33 The Iris dataset 33 Reading image data 35 Loading and processing the images 36 Reading from the standard tensorFlow format Summary Chapter 2: Clustering Learning from data-unsupervised learning 38 Clustering 39 k-means 40 Mechanics of k-means 40 Algorithm iteration criterion 40 (-means algorithm breakdown Pros and cons of k-means 42 k-nearest neighbors 43 Mechanics of k-nearest neighbors 44 Pros and cons of k-nn 45 Practical examples for Useful libraries 45 matplotlib plotting library 45 Sample synthetic data plotting scikit-learn dataset module About the scikit-learn library 47 Synthetic dataset types 47 Blobs dataset 48 Employed method 8 Circle dataset 48 Employed method 48 Moon dataset 48 Project 1-k-means clustering on synthetic datasets Dataset description and loading 49 Generating the dataset 49 odel architecture LosS function description and optimizer loop 50 Stop condition 51 Results description 51 Full source code 52 k-means on circle synthetic data Project 2-nearest neighbor on synthetic datasets 56 Dataset generation 56 Model architecture Loss function description 57 Stop condition 57 Results description 57 Full source code 58 [i] 【实例截图】
【核心代码】
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