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Home > Data > Deep Learning > TensorFlow 1.x Deep Learning Cookbook
TensorFlow 1.x Deep Learning Cookbook
TensorFlow 1.x Deep Learning Cookbook

TensorFlow 1.x Deep Learning Cookbook: Over 90 unique recipes to solve artificial-intelligence driven problems with Python

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TensorFlow 1.x Deep Learning Cookbook

Regression

This chapter shows how regression can be done using TensorFlow. In this chapter, we will cover the following topics:

  • Choosing loss functions
  • Optimizers in TensorFlow
  • Reading from CSV files and preprocessing data
  • House price estimation-simple linear regression
  • House price estimation-multiple linear regression
  • Logistic regression on the MNIST dataset

Introduction

Regression is one of the oldest and yet quite powerful tools for mathematical modelling, classification, and prediction. Regression finds application in varied fields ranging from engineering, physical science, biology, and the financial market to social sciences. It is the basic tool in the hand of a data scientist.

Regression is normally the first algorithm that people in machine learning work with. It allows us to make predictions from data by learning the relationship between the dependent and independent variables. For example, in the case of house price estimation, we determine the relationship between the area of the house (independent variable) and its price (dependent variable); this relationship can be then used to predict the price of any house given its area. We can have multiple independent variables impacting the dependent variable. Thus, there are two...

Choosing loss functions

As discussed earlier, in regression we define the loss function or objective function and the aim is to find the coefficients such that the loss is minimized. In this recipe, you will learn how to define loss functions in TensorFlow and choose a proper loss function depending on the problem at hand.

Getting ready

Declaring a loss function requires defining the coefficients as variables and the dataset as placeholders. One can have a constant learning rate or changing learning rate and regularization constant. In the following code, let m be the number of samples, n the number of features, and P the number of classes. We should define these global parameters before the code:

m = 1000
n = 15
P = 2
...

Optimizers in TensorFlow

From your high school math, you must know that a function's first derivative is zero at its maxima and minima. The gradient descent algorithm is based on the same principle--the coefficients (weights and biases) are adjusted such that the gradient of the loss function decreases. In regression, we use gradient descent to optimize the loss function and obtain coefficients. In this recipe, you will learn how to use the gradient descent optimizer of TensorFlow and some of its variants.

Getting ready

The update of coefficients (W and b) is done proportionally to the negative of the gradient of the loss function. There are three variations of gradient descent depending on the size of the training sample...

Reading from CSV files and preprocessing data

Most of you will already be familiar with Pandas and its usefulness in handling large dataset files. TensorFlow also offers methods to read from the files. In the first chapter, we went through the recipe for reading from files in TensorFlow; in this recipe, we will focus on how to read from CSV files and preprocess the data before training.

Getting ready

We will consider the Boston housing price dataset (http://lib.stat.cmu.edu/datasets/boston) collected by Harrison and Rubinfield in 1978. The dataset contains 506 sample cases. Each house is assigned 14 attributes:

  • CRIM: per capita crime rate by town
  • ZN: Proportion of residential land zoned for lots over 25,000 sq.ft
  • INDUS: Proportion...

House price estimation-simple linear regression

In this recipe, we will perform simple linear regression based on the number of rooms (RM) on the Boston house price dataset.

Getting ready

Our goal is to predict the house price given in the last column (MEDV). In this recipe, we load the dataset from the TensorFlow Contrib dataset directly. We optimize the coefficients for an individual training sample using the stochastic gradient descent optimizer.

How to do it...

We proceed with the recipe as follows:

  1. The first step is to import all the packages that we will need:
...

House price estimation-multiple linear regression

We can do multiple linear regression on the same data by making a slight modification in the declaration of weights and placeholders. In the case of multiple linear regression, as each feature has different value ranges, normalization becomes essential. Here is the code for multiple linear regression on the Boston house price dataset, using all the 13 input features.

How to do it...

Here is how we proceed with the recipe:

  1. The first step is to import all the packages that we will need:
import tensorflow as tf
 import numpy as np
 import matplotlib.pyplot as plt
  1. We need to normalize the feature data since the data ranges of all the features are varied. We define a normalize...

Logistic regression on the MNIST dataset

This recipe is based on the logistic regressor for MNIST provided at https://www.tensorflow.org/get_started/mnist/beginners, but we will add some TensorBoard summaries to understand it better. Most of you must already be familiar with the MNIST dataset--it is like the ABC of machine learning. It contains images of handwritten digits and a label for each image, saying which digit it is.

For logistic regression, we use one-hot encoding for the output Y. Thus, we have 10 bits representing the output; each bit can have a value either 0 or 1, and being one-hot means that for each image in label Y, only one bit out of the 10 will have value 1, the rest will be zeros. Here, you can see the image of the handwritten numeral 8, along with its hot encoded value [0 0 0 0 0 0 0 0 1 0]:

...
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Key benefits

  • Skill up and implement tricky neural networks using Google's TensorFlow 1.x
  • An easy-to-follow guide that lets you explore reinforcement learning, GANs, autoencoders, multilayer perceptrons and more.
  • Hands-on recipes to work with Tensorflow on desktop, mobile, and cloud environment

Description

Deep neural networks (DNNs) have achieved a lot of success in the field of computer vision, speech recognition, and natural language processing. This exciting recipe-based guide will take you from the realm of DNN theory to implementing them practically to solve real-life problems in the artificial intelligence domain. In this book, you will learn how to efficiently use TensorFlow, Google’s open source framework for deep learning. You will implement different deep learning networks, such as Convolutional Neural Networks (CNNs), Recurrent Neural Networks (RNNs), Deep Q-learning Networks (DQNs), and Generative Adversarial Networks (GANs), with easy-to-follow standalone recipes. You will learn how to use TensorFlow with Keras as the backend. You will learn how different DNNs perform on some popularly used datasets, such as MNIST, CIFAR-10, and Youtube8m. You will not only learn about the different mobile and embedded platforms supported by TensorFlow, but also how to set up cloud platforms for deep learning applications. You will also get a sneak peek at TPU architecture and how it will affect the future of DNNs. By using crisp, no-nonsense recipes, you will become an expert in implementing deep learning techniques in growing real-world applications and research areas such as reinforcement learning, GANs, and autoencoders.

Who is this book for?

This book is intended for data analysts, data scientists, machine learning practitioners and deep learning enthusiasts who want to perform deep learning tasks on a regular basis and are looking for a handy guide they can refer to. People who are slightly familiar with neural networks, and now want to gain expertise in working with different types of neural networks and datasets, will find this book quite useful.

What you will learn

  • • Leverage different data sets such as MNIST, CIFAR-10, and Youtube8m with TensorFlow and learn how to access and use them in your code
  • • Use TensorBoard to understand neural network architectures, optimize the learning process, and peek inside the neural network black box
  • • Use different regression techniques for prediction and classifi cation problems
  • • Build single and multilayer perceptrons in TensorFlow
  • • Implement a CNN and a RNN in TensorFlow, and use them to solve real-world problems
  • • Learn how Restricted Boltzmann Machines can be used to recommend movies
  • • Understand the implementation of autoencoders and deep belief networks, and use them for emotion detection
  • • Master the different reinforcement learning methods in order to implement game playing agents
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Publication date : Dec 12, 2017
Length: 536 pages
Edition : 1st
Language : English
ISBN-13 : 9781788293594
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Category :
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Tools :
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Publication date : Dec 12, 2017
Length: 536 pages
Edition : 1st
Language : English
ISBN-13 : 9781788293594
Vendor :
Google
Category :
Data
Languages :
Python
Concepts :
Deep Learning
Tools :
TensorFlow

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Table of Contents

14 Chapters
TensorFlow - An Introduction Chevron down icon Chevron up icon
TensorFlow - An Introduction
Introduction
Installing TensorFlow
Hello world in TensorFlow
Understanding the TensorFlow program structure
Working with constants, variables, and placeholders
Performing matrix manipulations using TensorFlow
Using a data flow graph
Migrating from 0.x to 1.x
Using XLA to enhance computational performance
Invoking CPU/GPU devices
TensorFlow for Deep Learning
Different Python packages required for DNN-based problems
Regression Chevron down icon Chevron up icon
Regression
Introduction
Choosing loss functions
Optimizers in TensorFlow
Reading from CSV files and preprocessing data
House price estimation-simple linear regression
House price estimation-multiple linear regression
Logistic regression on the MNIST dataset
Neural Networks - Perceptron Chevron down icon Chevron up icon
Neural Networks - Perceptron
Introduction
Activation functions
Single layer perceptron
Calculating gradients of backpropagation algorithm
MNIST classifier using MLP
Function approximation using MLP-predicting Boston house prices
Tuning hyperparameters
Higher-level APIs-Keras
See also
Convolutional Neural Networks Chevron down icon Chevron up icon
Convolutional Neural Networks
Introduction
Creating a ConvNet to classify handwritten MNIST numbers
Creating a ConvNet to classify CIFAR-10
Transferring style with VGG19 for image repainting
Using a pretrained VGG16 net for transfer learning
Creating a DeepDream network
Advanced Convolutional Neural Networks Chevron down icon Chevron up icon
Advanced Convolutional Neural Networks
Introduction
Creating a ConvNet for Sentiment Analysis
Inspecting what filters a VGG pre-built network has learned
Classifying images with VGGNet, ResNet, Inception, and Xception
Recycling pre-built Deep Learning models for extracting features
Very deep InceptionV3 Net used for Transfer Learning
Generating music with dilated ConvNets, WaveNet, and NSynth
Answering questions about images (Visual Q&A)
Classifying videos with pre-trained nets in six different ways
Recurrent Neural Networks Chevron down icon Chevron up icon
Recurrent Neural Networks
Introduction
Neural machine translation - training a seq2seq RNN
Neural machine translation - inference on a seq2seq RNN
All you need is attention - another example of a seq2seq RNN
Learning to write as Shakespeare with RNNs
Learning to predict future Bitcoin value with RNNs
Many-to-one and many-to-many RNN examples
Unsupervised Learning Chevron down icon Chevron up icon
Unsupervised Learning
Introduction
Principal component analysis
k-means clustering
Self-organizing maps
Restricted Boltzmann Machine
Recommender system using RBM
DBN for Emotion Detection
Autoencoders Chevron down icon Chevron up icon
Autoencoders
Introduction
Vanilla autoencoders
Sparse autoencoder
Denoising autoencoder
Convolutional autoencoders
Stacked autoencoder
Reinforcement Learning Chevron down icon Chevron up icon
Reinforcement Learning
Introduction
Learning OpenAI Gym
Implementing neural network agent to play Pac-Man
Q learning to balance Cart-Pole
Game of Atari using Deep Q Networks
Policy gradients to play the game of Pong
Mobile Computation Chevron down icon Chevron up icon
Mobile Computation
Introduction
Installing TensorFlow mobile for macOS and Android
Playing with TensorFlow and Android examples
Installing TensorFlow mobile for macOS and iPhone
Optimizing a TensorFlow graph for mobile devices
Profiling a TensorFlow graph for mobile devices
Transforming a TensorFlow graph for mobile devices
Generative Models and CapsNet Chevron down icon Chevron up icon
Generative Models and CapsNet
Introduction
Learning to forge MNIST images with simple GANs
Learning to forge MNIST images with DCGANs
Learning to forge Celebrity Faces and other datasets with DCGAN
Implementing Variational Autoencoders
Learning to beat the previous MNIST state-of-the-art results with Capsule Networks
Distributed TensorFlow and Cloud Deep Learning Chevron down icon Chevron up icon
Distributed TensorFlow and Cloud Deep Learning
Introduction
Working with TensorFlow and GPUs
Playing with Distributed TensorFlow: multiple GPUs and one CPU
Playing with Distributed TensorFlow: multiple servers
Training a Distributed TensorFlow MNIST classifier
Working with TensorFlow Serving and Docker
Running Distributed TensorFlow on Google Cloud (GCP) with Compute Engine
Running Distributed TensorFlow on Google CloudML
Running Distributed TensorFlow on Microsoft Azure
Running Distributed TensorFlow on Amazon AWS
Learning to Learn with AutoML (Meta-Learning) Chevron down icon Chevron up icon
Learning to Learn with AutoML (Meta-Learning)
Meta-learning with recurrent networks and with reinforcement learning
Meta-learning blocks
Meta-learning novel tasks
Siamese Network
TensorFlow Processing Units Chevron down icon Chevron up icon
TensorFlow Processing Units
Components of TPUs

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U_R_K Aug 09, 2020
Full star icon Empty star icon Empty star icon Empty star icon Empty star icon 1
This is highly recommended book. But Amazon shipped me India edition while charged for international edition. Instead of published 35% discount I ended up paying 35% more on printed price. Since I am in urgent need of the book, I am not going to return it.
Amazon Verified review Amazon
Olivier Croissant Mar 21, 2019
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Il est inadmissible de vendre presque au prix neuf un kindle illisible !
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Gayan Aug 18, 2018
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Good product good read
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Suren Jul 15, 2018
Full star icon Full star icon Full star icon Empty star icon Empty star icon 3
I bought this book to expand my knowledge of tensorflow. In general this is good collage of articles on neural networks. However, all the articles separately available openly in the internet. For example chapter on lstms there is google neural translation machine, I expected to get more insight than it is in original google article, but the paragraph in the book is just copy/paste.There are plenty of typos in text as well as in code.Overall, if you are just starting with tensorflow, it’s a good reference point, however It is overpriced for the content that is freely available.
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H. Rast May 08, 2018
Full star icon Empty star icon Empty star icon Empty star icon Empty star icon 1
I bought this from Packt on a discount (knowing full well the lack of quality of their publications), but I needed to do something specific with tensorflow really badly.Of course I get the book and go straight for that chapter, and wait for it... he is using Keras to do it.The title makes it seem like its a book on tensorflow but the one chapter that i read (chapter 4 section on using pretrained models) is using keras!false advertising.H
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List of EU27 countries: www.gov.uk/eu-eea:

A custom duty or localized taxes may be applicable on the shipment and would be charged by the recipient country outside of the EU27 which should be paid by the customer and these duties are not included in the shipping charges been charged on the order.

How do I know my custom duty charges? Chevron down icon Chevron up icon

The amount of duty payable varies greatly depending on the imported goods, the country of origin and several other factors like the total invoice amount or dimensions like weight, and other such criteria applicable in your country.

For example:

  • If you live in Mexico, and the declared value of your ordered items is over $ 50, for you to receive a package, you will have to pay additional import tax of 19% which will be $ 9.50 to the courier service.
  • Whereas if you live in Turkey, and the declared value of your ordered items is over € 22, for you to receive a package, you will have to pay additional import tax of 18% which will be € 3.96 to the courier service.
How can I cancel my order? Chevron down icon Chevron up icon

Cancellation Policy for Published Printed Books:

You can cancel any order within 1 hour of placing the order. Simply contact customercare@packt.com with your order details or payment transaction id. If your order has already started the shipment process, we will do our best to stop it. However, if it is already on the way to you then when you receive it, you can contact us at customercare@packt.com using the returns and refund process.

Please understand that Packt Publishing cannot provide refunds or cancel any order except for the cases described in our Return Policy (i.e. Packt Publishing agrees to replace your printed book because it arrives damaged or material defect in book), Packt Publishing will not accept returns.

What is your returns and refunds policy? Chevron down icon Chevron up icon

Return Policy:

We want you to be happy with your purchase from Packtpub.com. We will not hassle you with returning print books to us. If the print book you receive from us is incorrect, damaged, doesn't work or is unacceptably late, please contact Customer Relations Team on customercare@packt.com with the order number and issue details as explained below:

  1. If you ordered (eBook, Video or Print Book) incorrectly or accidentally, please contact Customer Relations Team on customercare@packt.com within one hour of placing the order and we will replace/refund you the item cost.
  2. Sadly, if your eBook or Video file is faulty or a fault occurs during the eBook or Video being made available to you, i.e. during download then you should contact Customer Relations Team within 14 days of purchase on customercare@packt.com who will be able to resolve this issue for you.
  3. You will have a choice of replacement or refund of the problem items.(damaged, defective or incorrect)
  4. Once Customer Care Team confirms that you will be refunded, you should receive the refund within 10 to 12 working days.
  5. If you are only requesting a refund of one book from a multiple order, then we will refund you the appropriate single item.
  6. Where the items were shipped under a free shipping offer, there will be no shipping costs to refund.

On the off chance your printed book arrives damaged, with book material defect, contact our Customer Relation Team on customercare@packt.com within 14 days of receipt of the book with appropriate evidence of damage and we will work with you to secure a replacement copy, if necessary. Please note that each printed book you order from us is individually made by Packt's professional book-printing partner which is on a print-on-demand basis.

What tax is charged? Chevron down icon Chevron up icon

Currently, no tax is charged on the purchase of any print book (subject to change based on the laws and regulations). A localized VAT fee is charged only to our European and UK customers on eBooks, Video and subscriptions that they buy. GST is charged to Indian customers for eBooks and video purchases.

What payment methods can I use? Chevron down icon Chevron up icon

You can pay with the following card types:

  1. Visa Debit
  2. Visa Credit
  3. MasterCard
  4. PayPal
What is the delivery time and cost of print books? Chevron down icon Chevron up icon

Shipping Details

USA:

'

Economy: Delivery to most addresses in the US within 10-15 business days

Premium: Trackable Delivery to most addresses in the US within 3-8 business days

UK:

Economy: Delivery to most addresses in the U.K. within 7-9 business days.
Shipments are not trackable

Premium: Trackable delivery to most addresses in the U.K. within 3-4 business days!
Add one extra business day for deliveries to Northern Ireland and Scottish Highlands and islands

EU:

Premium: Trackable delivery to most EU destinations within 4-9 business days.

Australia:

Economy: Can deliver to P. O. Boxes and private residences.
Trackable service with delivery to addresses in Australia only.
Delivery time ranges from 7-9 business days for VIC and 8-10 business days for Interstate metro
Delivery time is up to 15 business days for remote areas of WA, NT & QLD.

Premium: Delivery to addresses in Australia only
Trackable delivery to most P. O. Boxes and private residences in Australia within 4-5 days based on the distance to a destination following dispatch.

India:

Premium: Delivery to most Indian addresses within 5-6 business days

Rest of the World:

Premium: Countries in the American continent: Trackable delivery to most countries within 4-7 business days

Asia:

Premium: Delivery to most Asian addresses within 5-9 business days

Disclaimer:
All orders received before 5 PM U.K time would start printing from the next business day. So the estimated delivery times start from the next day as well. Orders received after 5 PM U.K time (in our internal systems) on a business day or anytime on the weekend will begin printing the second to next business day. For example, an order placed at 11 AM today will begin printing tomorrow, whereas an order placed at 9 PM tonight will begin printing the day after tomorrow.


Unfortunately, due to several restrictions, we are unable to ship to the following countries:

  1. Afghanistan
  2. American Samoa
  3. Belarus
  4. Brunei Darussalam
  5. Central African Republic
  6. The Democratic Republic of Congo
  7. Eritrea
  8. Guinea-bissau
  9. Iran
  10. Lebanon
  11. Libiya Arab Jamahriya
  12. Somalia
  13. Sudan
  14. Russian Federation
  15. Syrian Arab Republic
  16. Ukraine
  17. Venezuela