Hands-On Convolutional Neural Networks With TensorFlow by Zafar, Iffat -- Read -- Imperial Library of Trantor

Index

Title Page Copyright and Credits

Hands-On Convolutional Neural Networks with TensorFlow

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Contributors

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Preface

Who this book is for What this book covers To get the most out of this book

Download the example code files Conventions used

Get in touch

Reviews

Setup and Introduction to TensorFlow

The TensorFlow way of thinking Setting up and installing TensorFlow

Conda environments Checking whether your installation works

TensorFlow API levels

Eager execution

Building your first TensorFlow model

One-hot vectors Splitting into training and test sets

Creating TensorFlow graphs

Variables Operations

Feeding data with placeholders Initializing variables Training our model

Loss functions Optimization

Evaluating a trained model The session Summary

Deep Learning and Convolutional Neural Networks

AI and ML

Types of ML Old versus new ML

Artificial neural networks

Activation functions The XOR problem Training neural networks Backpropagation and the chain rule Batches Loss functions The optimizer and its hyperparameters

Underfitting versus overfitting

Feature scaling Fully connected layers A TensorFlow example for the XOR problem

Convolutional neural networks

Convolution Input padding Calculating the number of parameters (weights) Calculating the number of operations Converting convolution layers into fully connected layers The pooling layer 1x1 Convolution Calculating the receptive field

Building a CNN model in TensorFlow

TensorBoard Other types of convolutions

Summary

Image Classification in TensorFlow

CNN model architecture

Cross-entropy loss (log loss) Multi-class cross entropy loss The train/test dataset split

Datasets

ImageNet CIFAR Loading CIFAR

Image classification with TensorFlow

Building the CNN graph Learning rate scheduling Introduction to the tf.data API The main training loop

Model Initialization

Do not initialize all weights with zeros Initializing with a mean zero distribution

Xavier-Bengio and the Initializer

Improving generalization by regularizing

L2 and L1 regularization Dropout The batch norm layer

Summary

Object Detection and Segmentation

Image classification with localization

Localization as regression TensorFlow implementation Other applications of localization

Object detection as classification – Sliding window

Using heuristics to guide us (R-CNN)

Problems

Fast R-CNN Faster R-CNN

Region Proposal Network RoI Pooling layer

Conversion from traditional CNN to Fully Convnets

Single Shot Detectors – You Only Look Once

Creating training set for Yolo object detection Evaluating detection (Intersection Over Union) Filtering output Anchor Box Testing/Predicting in Yolo Detector Loss function (YOLO loss)

Loss Part 1 Loss Part 2 Loss Part 3

Semantic segmentation

Max Unpooling Deconvolution layer (Transposed convolution) The loss function Labels Improving results

Instance segmentation

Mask R-CNN

Summary

VGG, Inception Modules, Residuals, and MobileNets

Substituting big convolutions

Substituting the 3x3 convolution

VGGNet

Architecture Parameters and memory calculation Code More about VGG

GoogLeNet

Inception module More about GoogLeNet

Residual Networks MobileNets

Depthwise separable convolution Control parameters More about MobileNets

Summary

Autoencoders, Variational Autoencoders, and Generative Adversarial Networks

Why generative models Autoencoders

Convolutional autoencoder example Uses and limitations of autoencoders

Variational autoencoders

Parameters to define a normal distribution VAE loss function

Kullback-Leibler divergence

Training the VAE The reparameterization trick Convolutional Variational Autoencoder code Generating new data

Generative adversarial networks

The discriminator The generator GAN loss function

Generator loss Discriminator loss Putting the losses together

Training the GAN Deep convolutional GAN WGAN BEGAN Conditional GANs Problems with GANs

Loss interpretability Mode collapse

Techniques to improve GANs' trainability

Minibatch discriminator

Summary

Transfer Learning

When? How? An overview How? Code example

TensorFlow useful elements

An autoencoder without the decoder Selecting layers Training only some layers

Complete source

Summary

Machine Learning Best Practices and Troubleshooting

Building Machine Learning Systems Data Preparation

Split of Train/Development/Test set Mismatch of the Dev and Test set When to Change Dev/Test Set

Bias and Variance Data Imbalance

Collecting more data Look at your performance metric Data synthesis/Augmentation Resample Data Loss function Weighting

Evaluation Metrics Code Structure best Practice

Singleton Pattern

Recipe for CNN creation Summary

Training at Scale

Storing data in TFRecords

Making a TFRecord

Storing encoded images Sharding

Making efficient pipelines

Parallel calls for map transformations

Getting a batch Prefetching Tracing your graph

Distributed computing in TensorFlow

Model/data parallelism

Synchronous/asynchronous SGD

When data does not fit on one computer

The advantages of NoSQL systems

Installing Cassandra (Ubuntu 16.04) The CQLSH tool Creating databases, tables, and indexes Doing queries in Python Populating tables in Python Doing backups

Scaling computation in the cloud

EC2

AMI

Storage (S3)

SageMaker

Summary

References

Chapter 1 Chapter 2 Chapter 3 Chapter 4 Chapter 5 Chapter 7 Chapter 9

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