Practical Convolutional Neural Networks

Other Books You May Enjoy

Summary

References

Glimpse Sensor in code

Applying the RAM on a noisy MNIST sample

Recurrent models of visual attention

Reasons for sub-optimal performance of visual CNN models

Using attention to improve visual models

Soft Attention

Hard Attention

Types of Attention

Attention mechanism for image captioning

Attention Mechanism for CNN and Visual Models

Summary

Batch normalization

Deep convolutional GAN

Semi-supervised classification using a GAN example

Feature matching

Semi-supervised learning and GAN

Adding the optimizer

Calculating loss

GAN – code example

Training a GAN model

CycleGAN

Pix2pix - Image-to-Image translation GAN

GAN: Generating New Images with CNN

Summary

References

Running the pre-trained model on the COCO dataset

Preparing the COCO dataset folder structure

Downloading and installing the COCO API and detectron library (OS shell commands)

Installing Python dependencies (Python2 environment)

Creating the environment

Instance segmentation in code

Mask R-CNN – Instance segmentation with CNN

Faster R-CNN – faster region proposal network-based CNN

Fast R-CNN – fast region-based CNN

R-CNN – Regions with CNN features

The Viola-Jones algorithm

Cascading classifiers

Haar Features

Haar features cascading classifiers and the Viola-Jones algorithm

Traditional nonCNN approaches to object detection

Why is object detection much more challenging than image classification?

The differences between object detection and image classification

Object Detection and Instance Segmentation with CNN

Summary

An example of compression

Applications

Convolutional autoencoder

Introducing to autoencoders

Autoencoders for CNN

Summary

Multi-task learning

Transfer learning example

Target dataset is large and different from the original training dataset

Target dataset is large and similar to the original training dataset

Target dataset is small but different from the original training dataset

Target dataset is small and is similar to the original training dataset

Feature extraction approach

Transfer Learning

Summary

ResNet architecture

Inception module

Architecture insights

GoogLeNet architecture

VGG16 image classification code example

VGGNet architecture

Traffic sign classifiers using AlexNet

AlexNet architecture

LeNet

Introduction to ImageNet

Popular CNN Model Architectures

Summary

Training and evaluating the network

Creating the CNN model

Dataset description and preprocessing

Building the second CNN by putting everything together

Appropriate layer placement

Memory tuning

Which optimizer to use?

Applying dropout operations with TensorFlow

Advanced regularization and avoiding overfitting

Batch normalization

Number of neurons per hidden layer

Number of hidden layers

Model performance optimization

Step 8 – Model evaluation

Step 7 – Running the TensorFlow graph to train the CNN model

Step 6 – Creating a CNN model

Step 5 – Preparing the TensorFlow graph

Step 4 – Constructing the CNN layers

Step 3- Defining CNN hyperparameters

Step 2 – Loading the training/test images to generate train/test set

Step 1 – Loading the required packages

Dataset description

Building training and evaluating our first CNN

Using ReLU

Using tanh

Using sigmoid

Activation functions

Regularization

Weight and bias initialization

Training a CNN

Convolution operations in TensorFlow

Applying pooling operations in TensorFlow

Convolution and pooling operations in TensorFlow

Fully connected layer

Pooling stride and padding operations

Convolutional operations

CNN architectures and drawbacks of DNNs

Build Your First CNN and Performance Optimization

Summary

Image augmentation

Practical example – image classification

Pooling layer

Convolutional layers in Keras

Convolutional layer

Input layer

Dropout

Code for visualizing an image

How do computers interpret images?

Convolutional neural networks

History of CNNs

Introduction to Convolutional Neural Networks

Summary

Understanding backpropagation

Testing

Training the network

Building the network

Visualizing the training data

Flattened data

Retrieving training and test data

Handwritten number recognition with Keras and MNIST

Layers in the Keras model

Keras deep learning library overview

Building a single-layer neural network with TensorFlow

The simplest artificial neural network

Introduction to the MNIST dataset

Softmax in TensorFlow

Basic math with TensorFlow

TensorFlow basics

For macOS X/Linux variants

Installing TensorFlow

Introduction to TensorFlow

Building blocks of a neural network

Deep Neural Networks – Overview

Reviews

Get in touch

Conventions used

Download the color images

Download the example code files

To get the most out of this book

What this book covers

Who this book is for

Preface

Packt is searching for authors like you

About the reviewer

About the authors

Contributors

PacktPub.com

Why subscribe?

Packt Upsell

Title Page

coverpage

coverpage

Title Page

Packt Upsell

Why subscribe?

PacktPub.com

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

Get in touch

Reviews

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

Other Books You May Enjoy