Practical Convolutional Neural Networks电子书

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Practical Convolutional Neural Networks

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Contributors

About the authors

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Preface

Who this book is for

What this book covers

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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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