TensorFlow 1.x Deep Learning Cookbook

Overview of this book

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.

Preface

What this book covers

What you need for this book

Free Chapter

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

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

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

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

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

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

Introduction

Principal component analysis

k-means clustering

Self-organizing maps

Restricted Boltzmann Machine

Recommender system using RBM

DBN for Emotion Detection

Autoencoders

Introduction

Vanilla autoencoders

Sparse autoencoder

Denoising autoencoder

Convolutional autoencoders

Stacked autoencoder

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

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

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

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)

Meta-learning with recurrent networks and with reinforcement learning

Meta-learning blocks

Meta-learning novel tasks

Siamese Network

TensorFlow Processing Units

Components of TPUs

Customer Reviews

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Different Python packages required for DNN-based problems

TensorFlow takes care of most of the neural network implementation. However, this is not sufficient; for preprocessing tasks, serialization, and even plotting, we need some more Python packages.

How to do it...

Here are listed some of the common Python packages used:

Numpy: This is the fundamental package for scientific computing with Python. It supports n-dimensional arrays and matrices. It also has a large collection of high-level mathematical functions. It is a necessary package required by TensorFlow and therefore, with pip install tensorflow, it is installed if not already present.
Matplolib: This is the Python 2D plotting library. You can use it to create plots, histograms, bar charts, error charts, scatterplots, and power spectra with just a few lines of code. It can be installed using pip:

pip install matplotlib 
# or using Anaconda    
conda install -c conda-forge matplotlib

OS: This is included in the basic Python installation. It provides an easy and portable way of using operating system-dependent functionality like reading, writing, and changing files and directories.
Pandas: This provides various data structures and data analysis tools. Using Pandas, you can read and write data between in-memory data structures and different formats. We can read from .csv and text files. It can be installed using either pip install or conda install.
Seaborn: This is a specialized statistical data visualization tool built on Matplotlib.
H5fs: H5fs is a filesystem for Linux (also other operating systems with FUSE implementation like macOS X) capable of operations on an HDFS (Hierarchical Data Format Filesystem).
PythonMagick: It is the Python binding of the ImageMagick library. It is a library to display, convert, and edit raster image and vector image files. It supports more than 200 image file formats. It can be installed using source build available from ImageMagick. Certain .whl formats are also available for a convenient pip install (http://www.lfd.uci.edu/%7Egohlke/pythonlibs/#pythonmagick).

TFlearn: TFlearn is a modular and transparent deep learning library built on top of TensorFlow. It provides a higher-level API to TensorFlow in order to facilitate and speed up experimentation. It currently supports most of the recent deep learning models, such as Convolutions, LSTM, BatchNorm, BiRNN, PReLU, Residual networks, and Generative networks. It works only for TensorFlow 1.0 or higher. To install, use pip install tflearn.
Keras: Keras too is a high-level API for neural networks, which uses TensorFlow as its backend. It can run on top of Theano and CNTK as well. It is extremely user-friendly, adding layers to it is just a one-line job. It can be installed using pip install keras.

TensorFlow 1.x Deep Learning Cookbook

TensorFlow 1.x Deep Learning Cookbook

Overview of this book

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