Practical Convolutional Neural Networks by Karim, Md. Rezaul -- Read -- Imperial Library of Trantor

Index

Title Page Copyright and Credits

Practical Convolutional Neural Networks

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Contributors

About the authors About the reviewer Packt is searching for authors like you

Preface

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

Download the example code files Download the color images Conventions used

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Deep Neural Networks – Overview

Building blocks of a neural network Introduction to TensorFlow

Installing TensorFlow

For macOS X/Linux variants TensorFlow basics Basic math with TensorFlow Softmax in TensorFlow

Introduction to the MNIST dataset

The simplest artificial neural network

Building a single-layer neural network with TensorFlow

Keras deep learning library overview

Layers in the Keras model

Handwritten number recognition with Keras and MNIST

Retrieving training and test data

Flattened data

Visualizing the training data Building the network Training the network Testing

Understanding backpropagation Summary

Introduction to Convolutional Neural Networks

History of CNNs Convolutional neural networks

How do computers interpret images? Code for visualizing an image Dropout Input layer Convolutional layer

Convolutional layers in Keras

Pooling layer

Practical example – image classification

Image augmentation

Summary

Build Your First CNN and Performance Optimization

CNN architectures and drawbacks of DNNs

Convolutional operations Pooling, stride, and padding operations

Fully connected layer

Convolution and pooling operations in TensorFlow

Applying pooling operations in TensorFlow Convolution operations in TensorFlow

Training a CNN

Weight and bias initialization Regularization Activation functions

Using sigmoid Using tanh Using ReLU

Building, training, and evaluating our first CNN

Dataset description

Step 1 – Loading the required packages Step 2 – Loading the training/test images to generate train/test set Step 3- Defining CNN hyperparameters Step 4 – Constructing the CNN layers Step 5 – Preparing the TensorFlow graph Step 6 – Creating a CNN model Step 7 – Running the TensorFlow graph to train the CNN model Step 8 – Model evaluation

Model performance optimization

Number of hidden layers Number of neurons per hidden layer Batch normalization Advanced regularization and avoiding overfitting

Applying dropout operations with TensorFlow

Which optimizer to use? Memory tuning Appropriate layer placement Building the second CNN by putting everything together

Dataset description and preprocessing Creating the CNN model Training and evaluating the network

Summary

Popular CNN Model Architectures

Introduction to ImageNet LeNet AlexNet architecture

Traffic sign classifiers using AlexNet

VGGNet architecture

VGG16 image classification code example

GoogLeNet architecture

Architecture insights Inception module

ResNet architecture Summary

Transfer Learning

Feature extraction approach

Target dataset is small and is similar to the original training dataset Target dataset is small but different from the original training dataset Target dataset is large and similar to the original training dataset Target dataset is large and different from the original training dataset

Transfer learning example Multi-task learning Summary

Autoencoders for CNN

Introducing to autoencoders Convolutional autoencoder Applications

An example of compression

Summary

Object Detection and Instance Segmentation with CNN

The differences between object detection and image classification

Why is object detection much more challenging than image classification?

Traditional, nonCNN approaches to object detection

Haar features, cascading classifiers, and the Viola-Jones algorithm

Haar Features Cascading classifiers The Viola-Jones algorithm

R-CNN – Regions with CNN features Fast R-CNN – fast region-based CNN Faster R-CNN – faster region proposal network-based CNN Mask R-CNN – Instance segmentation with CNN Instance segmentation in code

Creating the environment

Installing Python dependencies (Python2 environment) Downloading and installing the COCO API and detectron library (OS shell commands)

Preparing the COCO dataset folder structure Running the pre-trained model on the COCO dataset

References Summary

GAN: Generating New Images with CNN

Pix2pix - Image-to-Image translation GAN

CycleGAN Training a GAN model

GAN – code example

Calculating loss

Adding the optimizer

Semi-supervised learning and GAN

Feature matching

Semi-supervised classification using a GAN example Deep convolutional GAN

Batch normalization

Summary

Attention Mechanism for CNN and Visual Models

Attention mechanism for image captioning Types of Attention

Hard Attention Soft Attention

Using attention to improve visual models

Reasons for sub-optimal performance of visual CNN models Recurrent models of visual attention

Applying the RAM on a noisy MNIST sample

Glimpse Sensor in code

References Summary

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