更新时间：2021-08-13 15:34:04
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
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Download the example code files
Conventions used
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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
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
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
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
