TensorFlow 2 Pocket Reference: Building and Deploying Machine Learning Models

Cover
Copyright
Table of Contents
Preface
	Conventions Used in This Book
	Using Code Examples
	O’Reilly Online Learning
	How to Contact Us
	Acknowledgments
Chapter 1. Introduction to TensorFlow 2
	Improvements in TensorFlow 2
		Keras API
		Reusable Models in TensorFlow
	Making Commonly Used Operations Easy
		Open Source Data
		Working with Distributed Datasets
		Data Streaming
		Data Engineering
		Transfer Learning
		Model Styles
		Monitoring the Training Process
		Distributed Training
		Serving Your TensorFlow Model
		Improving the Training Experience
	Wrapping Up
Chapter 2. Data Storage and Ingestion
	Streaming Data with Python Generators
	Streaming File Content with a Generator
	JSON Data Structures
	Setting Up a Pattern for Filenames
	Splitting a Single CSV File into Multiple CSV Files
	Creating a File Pattern Object Using tf.io
	Creating a Streaming Dataset Object
	Streaming a CSV Dataset
	Organizing Image Data
	Using TensorFlow Image Generator
	Streaming Cross-Validation Images
	Inspecting Resized Images
	Wrapping Up
Chapter 3. Data Preprocessing
	Preparing Tabular Data for Training
		Marking Columns
		Encoding Column Interactions as Possible Features
		Creating a Cross-Validation Dataset
		Starting the Model Training Process
		Summary
	Preparing Image Data for Processing
		Transforming Images to a Fixed Specification
		Training the Model
		Summary
	Preparing Text Data for Processing
		Tokenizing Text
		Creating a Dictionary and Reverse Dictionary
	Wrapping Up
Chapter 4. Reusable Model Elements
	The Basic TensorFlow Hub Workflow
	Image Classification by Transfer Learning
		Model Requirements
		Data Transformation and Input Processing
		Model Implementation with TensorFlow Hub
		Defining the Output
		Mapping Output to Plain-Text Format
		Evaluation: Creating a Confusion Matrix
		Summary
	Using the tf.keras.applications Module for Pretrained Models
		Model Implementation with tf.keras.applications
		Fine-Tuning Models from tf.keras.applications
	Wrapping Up
Chapter 5. Data Pipelines for Streaming Ingestion
	Streaming Text Files with the text_dataset_from_directory Function
		Downloading Text Data and Setting Up Directories
		Creating the Data Pipeline
		Inspecting the Dataset
		Summary
	Streaming Images with a File List Using the flow_from_dataframe Method
		Downloading Images and Setting Up Directories
		Creating the Data Ingestion Pipeline
		Inspecting the Dataset
		Building and Training the tf.keras Model
	Streaming a NumPy Array with the from_tensor_slices Method
		Loading Example Data and Libraries
		Inspecting the NumPy Array
		Building the Input Pipeline for NumPy Data
	Wrapping Up
Chapter 6. Model Creation Styles
	Using the Symbolic API
		Loading the CIFAR-10 Images
		Inspecting Label Distribution
		Inspecting Images
		Building a Data Pipeline
		Batching the Dataset for Training
		Building the Model
	Understanding Inheritance
	Using the Imperative API
		Defining a Model as a Class
	Choosing the API
	Using the Built-In Training Loop
	Creating and Using a Custom Training Loop
		Creating the Elements of the Loop
		Putting the Elements Together in a Custom Training Loop
	Wrapping Up
Chapter 7. Monitoring the Training Process
	Callback Objects
		ModelCheckpoint
		EarlyStopping
		Summary
	TensorBoard
		Invoking TensorBoard by Local Jupyter Notebook
		Invoking TensorBoard by Local Command Terminal
		Invoking TensorBoard by Colab Notebook
		Visualizing Model Overfitting Using TensorBoard
		Visualizing the Learning Process Using TensorBoard
	Wrapping Up
Chapter 8. Distributed Training
	Data Parallelism
		Asynchronous Parameter Server
		Synchronous Allreduce
	Using the Class tf.distribute.MirroredStrategy
		Setting Up Distributed Training
		Using a GPU Cluster with tf.distribute.MirroredStrategy
		Summary
	The Horovod API
		Code Pattern for Implementing the Horovod API
		Encapsulating the Model Architecture
		Encapsulating the Data Separation and Sharding Processes
		Parameter Synchronization Among Workers
		Model Checkpoint as a Callback
		Distributed Optimizer for Gradient Aggregation
		Distributed Training Using the Horovod API
	Wrapping Up
Chapter 9. Serving TensorFlow Models
	Model Serialization
		Saving a Model to h5 Format
		Saving a Model to pb Format
		Selecting the Model Format
	TensorFlow Serving
		Running TensorFlow Serving with a Docker Image
	Wrapping Up
Chapter 10. Improving the Modeling Experience: Fairness Evaluation and Hyperparameter Tuning
	Model Fairness
		Model Training and Scoring
		Fairness Evaluation
		Rendering Fairness Indicators
	Hyperparameter Tuning
		Integer Lists as Hyperparameters
		Item Choice as Hyperparameters
		Floating-Point Values as Hyperparameters
	End-to-End Hyperparameter Tuning
		Import Libraries and Load Data
	Wrapping Up
Index