Learning TensorFlow. A Guide to Building Deep

Learning TensorFlow. A Guide to Building Deep - Opis

Roughly inspired by the human brain, deep neural networks trained with large amounts of data can solve complex tasks with unprecedented accuracy. This practical book provides an end-to-end guide to TensorFlow, the leading open source software library that helps you build and train neural networks for computer vision, natural language processing (NLP), speech recognition, and general predictive analytics.Authors Tom Hope, Yehezkel Resheff, and Itay Lieder provide a hands-on approach to TensorFlow fundamentals for a broad technical audience-from data scientists and engineers to students and researchers. You...ll begin by working through some basic examples in TensorFlow before diving deeper into topics such as neural network architectures, TensorBoard visualization, TensorFlow abstraction libraries, and multithreaded input pipelines. Once you finish this book, you...ll know how to build and deploy production-ready deep learning systems in TensorFlow.Get up and running with TensorFlow, rapidly and painlesslyLearn how to use TensorFlow to build deep learning models from the ground upTrain popular deep learning models for computer vision and NLPUse extensive abstraction libraries to make development easier and fasterLearn how to scale TensorFlow, and use clusters to distribute model trainingDeploy TensorFlow in a production setting Spis treści:
Preface
1. Introduction
Going Deep
Using TensorFlow for AI Systems
Pre-trained models: state-of-the-art computer vision for all
Generating rich natural language descriptions for images
Text (...) więcej summarization
TensorFlow: Whats 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
Setting attributes with source operations
Data Types
Casting
Tensor Arrays and Shapes
Matrix multiplication
Names
Name scopes
Variables, Placeholders, and Simple Optimization
Variables
Placeholders
Optimization
Training to predict
Defining a loss function
MSE and cross entropy
The gradient descent optimizer
Sampling methods
Gradient descent in TensorFlow
Wrapping it up with examples
Example 1: linear regression
Example 2: logistic regression
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
MNIST images as sequences
The RNN step
Applying the RNN step with tf.scan()
Sequential outputs
RNN classification
Visualizing the model with TensorBoard
TensorFlow Built-in RNN Functions
tf.contrib.rnn.BasicRNNCell and tf.nn.dynamic_rnn()
RNN for Text Sequences
Text Sequences
Supervised Word Embeddings
LSTM and Using Sequence Length
Training Embeddings and the LSTM Classifier
Stacking multiple LSTMs
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
Installation
Sequential model
Functional model
Autoencoders
Pretrained models with TF-Slim
TF-Slim
Creating CNN models with TF-Slim
Downloading and using a pretrained model
Summary
8. Queues, Threads, and Reading Data
The Input Pipeline
TFRecords
Writing with TFRecordWriter
Queues
Enqueuing and Dequeuing
Multithreading
Coordinator and QueueRunner
tf.train.Coordinator
tf.train.QueueRunner and tf.RandomShuffleQueue
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
Installing Serving
Building and Exporting
Exporting our model
Summary
A. Tips on Model Construction and Using TensorFlow Serving
Model Structuring and Customization
Model Structuring
Modular design
Variable sharing
Class encapsulation
Customization
Homemade loss functions
Regularization
Writing your very own op
Required and Recommended Components for TensorFlow Serving
What Is a Docker Container and Why Do We Use It?
Some Basic Docker Commands
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