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by Itay Lieder, Yehezkel S. Resheff, Tom Hope
Learning TensorFlow
Preface
1. Introduction
Going Deep
Using TensorFlow for AI Systems
TensorFlow: What’s in a Name?
A High-Level Overview
Summary
2. Go with the Flow: Up and Running with TensorFlow
Installing TensorFlow
Hello World
MNIST
Softmax Regression
Summary
3. Understanding TensorFlow Basics
Computation Graphs
What Is a Computation Graph?
The Benefits of Graph Computations
Graphs, Sessions, and Fetches
Creating a Graph
Creating a Session and Running It
Constructing and Managing Our Graph
Fetches
Flowing Tensors
Nodes Are Operations, Edges Are Tensor Objects
Data Types
Tensor Arrays and Shapes
Names
Variables, Placeholders, and Simple Optimization
Variables
Placeholders
Optimization
Summary
4. Convolutional Neural Networks
Introduction to CNNs
MNIST: Take II
Convolution
Pooling
Dropout
The Model
CIFAR10
Loading the CIFAR10 Dataset
Simple CIFAR10 Models
Summary
5. Text I: Working with Text and Sequences, and TensorBoard Visualization
The Importance of Sequence Data
Introduction to Recurrent Neural Networks
Vanilla RNN Implementation
TensorFlow Built-in RNN Functions
RNN for Text Sequences
Text Sequences
Supervised Word Embeddings
LSTM and Using Sequence Length
Training Embeddings and the LSTM Classifier
Summary
6. Text II: Word Vectors, Advanced RNN, and Embedding Visualization
Introduction to Word Embeddings
Word2vec
Skip-Grams
Embeddings in TensorFlow
The Noise-Contrastive Estimation (NCE) Loss Function
Learning Rate Decay
Training and Visualizing with TensorBoard
Checking Out Our Embeddings
Pretrained Embeddings, Advanced RNN
Pretrained Word Embeddings
Bidirectional RNN and GRU Cells
Summary
7. TensorFlow Abstractions and Simplifications
Chapter Overview
High-Level Survey
contrib.learn
Linear Regression
DNN Classifier
FeatureColumn
Homemade CNN with contrib.learn
TFLearn
Installation
CNN
RNN
Keras
Pretrained models with TF-Slim
Summary
8. Queues, Threads, and Reading Data
The Input Pipeline
TFRecords
Writing with TFRecordWriter
Queues
Enqueuing and Dequeuing
Multithreading
Coordinator and QueueRunner
A Full Multithreaded Input Pipeline
tf.train.string_input_producer() and tf.TFRecordReader()
tf.train.shuffle_batch()
tf.train.start_queue_runners() and Wrapping Up
Summary
9. Distributed TensorFlow
Distributed Computing
Where Does the Parallelization Take Place?
What Is the Goal of Parallelization?
TensorFlow Elements
tf.app.flags
Clusters and Servers
Replicating a Computational Graph Across Devices
Managed Sessions
Device Placement
Distributed Example
Summary
10. Exporting and Serving Models with TensorFlow
Saving and Exporting Our Model
Assigning Loaded Weights
The Saver Class
Introduction to TensorFlow Serving
Overview
Installation
Building and Exporting
Summary
A. Tips on Model Construction and Using TensorFlow Serving
Model Structuring and Customization
Model Structuring
Customization
Required and Recommended Components for TensorFlow Serving
What Is a Docker Container and Why Do We Use It?
Some Basic Docker Commands
Index
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