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