Hands-On Convolutional Neural Networks with TensorFlow

封面

Title Page

Copyright and Credits

Hands-On Convolutional Neural Networks with TensorFlow

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Contributors

About the authors

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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

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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