Mastering TensorFlow 1.x电子书

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Mastering TensorFlow 1.x

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Foreword

Contributors

About the author

About the reviewer

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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

TensorFlow 101

What is TensorFlow?

TensorFlow core

Code warm-up - Hello TensorFlow

Tensors

Constants

Operations

Placeholders

Creating tensors from Python objects

Variables

Tensors generated from library functions

Populating tensor elements with the same values

Populating tensor elements with sequences

Populating tensor elements with a random distribution

Getting Variables with tf.get_variable()

Data flow graph or computation graph

Order of execution and lazy loading

Executing graphs across compute devices - CPU and GPGPU

Placing graph nodes on specific compute devices

Simple placement

Dynamic placement

Soft placement

GPU memory handling

Multiple graphs

TensorBoard

A TensorBoard minimal example

TensorBoard details

Summary

High-Level Libraries for TensorFlow

TF Estimator - previously TF Learn

TF Slim

TFLearn

Creating the TFLearn Layers

TFLearn core layers

TFLearn convolutional layers

TFLearn recurrent layers

TFLearn normalization layers

TFLearn embedding layers

TFLearn merge layers

TFLearn estimator layers

Creating the TFLearn Model

Types of TFLearn models

Training the TFLearn Model

Using the TFLearn Model

PrettyTensor

Sonnet

Summary

Keras 101

Installing Keras

Neural Network Models in Keras

Workflow for building models in Keras

Creating the Keras model

Sequential API for creating the Keras model

Functional API for creating the Keras model

Keras Layers

Keras core layers

Keras convolutional layers

Keras pooling layers

Keras locally-connected layers

Keras recurrent layers

Keras embedding layers

Keras merge layers

Keras advanced activation layers

Keras normalization layers

Keras noise layers

Adding Layers to the Keras Model

Sequential API to add layers to the Keras model

Functional API to add layers to the Keras Model

Compiling the Keras model

Training the Keras model

Predicting with the Keras model

Additional modules in Keras

Keras sequential model example for MNIST dataset

Summary

Classical Machine Learning with TensorFlow

Simple linear regression

Data preparation

Building a simple regression model

Defining the inputs, parameters, and other variables

Defining the model

Defining the loss function

Defining the optimizer function

Training the model

Using the trained model to predict

Multi-regression

Regularized regression

Lasso regularization

Ridge regularization

ElasticNet regularization

Classification using logistic regression

Logistic regression for binary classification

Logistic regression for multiclass classification

Binary classification

Multiclass classification

Summary

Neural Networks and MLP with TensorFlow and Keras

The perceptron

MultiLayer Perceptron

MLP for image classification

TensorFlow-based MLP for MNIST classification

Keras-based MLP for MNIST classification

TFLearn-based MLP for MNIST classification

Summary of MLP with TensorFlow, Keras, and TFLearn

MLP for time series regression

Summary

RNN with TensorFlow and Keras

Simple Recurrent Neural Network

RNN variants

LSTM network

GRU network

TensorFlow for RNN

TensorFlow RNN Cell Classes

TensorFlow RNN Model Construction Classes

TensorFlow RNN Cell Wrapper Classes

Keras for RNN

Application areas of RNNs

RNN in Keras for MNIST data

Summary

RNN for Time Series Data with TensorFlow and Keras

Airline Passengers dataset

Loading the airpass dataset

Visualizing the airpass dataset

Preprocessing the dataset for RNN models with TensorFlow

Simple RNN in TensorFlow

LSTM in TensorFlow

GRU in TensorFlow

Preprocessing the dataset for RNN models with Keras

Simple RNN with Keras

LSTM with Keras

GRU with Keras

Summary

RNN for Text Data with TensorFlow and Keras

Word vector representations

Preparing the data for word2vec models

Loading and preparing the PTB dataset

Loading and preparing the text8 dataset

Preparing the small validation set

skip-gram model with TensorFlow

Visualize the word embeddings using t-SNE

skip-gram model with Keras

Text generation with RNN models in TensorFlow and Keras

Text generation LSTM in TensorFlow

Text generation LSTM in Keras

Summary

CNN with TensorFlow and Keras

Understanding convolution

Understanding pooling

CNN architecture pattern - LeNet

LeNet for MNIST data

LeNet CNN for MNIST with TensorFlow

LeNet CNN for MNIST with Keras

LeNet for CIFAR10 Data

ConvNets for CIFAR10 with TensorFlow

ConvNets for CIFAR10 with Keras

Summary

Autoencoder with TensorFlow and Keras

Autoencoder types

Stacked autoencoder in TensorFlow

Stacked autoencoder in Keras

Denoising autoencoder in TensorFlow

Denoising autoencoder in Keras

Variational autoencoder in TensorFlow

Variational autoencoder in Keras

Summary

TensorFlow Models in Production with TF Serving

Saving and Restoring models in TensorFlow

Saving and restoring all graph variables with the saver class

Saving and restoring selected variables with the saver class

Saving and restoring Keras models

TensorFlow Serving

Installing TF Serving

Saving models for TF Serving

Serving models with TF Serving

TF Serving in the Docker containers

Installing Docker

Building a Docker image for TF serving

Serving the model in the Docker container

TensorFlow Serving on Kubernetes

Installing Kubernetes

Uploading the Docker image to the dockerhub

Deploying in Kubernetes

Summary

Transfer Learning and Pre-Trained Models

ImageNet dataset

Retraining or fine-tuning models

COCO animals dataset and pre-processing images

VGG16 in TensorFlow

Image classification using pre-trained VGG16 in TensorFlow

Image preprocessing in TensorFlow for pre-trained VGG16

Image classification using retrained VGG16 in TensorFlow

VGG16 in Keras

Image classification using pre-trained VGG16 in Keras

Image classification using retrained VGG16 in Keras

Inception v3 in TensorFlow

Image classification using Inception v3 in TensorFlow

Image classification using retrained Inception v3 in TensorFlow

Summary

Deep Reinforcement Learning

OpenAI Gym 101

Applying simple policies to a cartpole game

Reinforcement learning 101

Q function (learning to optimize when the model is not available)

Exploration and exploitation in the RL algorithms

V function (learning to optimize when the model is available)

Reinforcement learning techniques

Naive Neural Network policy for Reinforcement Learning

Implementing Q-Learning

Initializing and discretizing for Q-Learning

Q-Learning with Q-Table

Q-Learning with Q-Network or Deep Q Network (DQN)

Summary

Generative Adversarial Networks

Generative Adversarial Networks 101

Best practices for building and training GANs

Simple GAN with TensorFlow

Simple GAN with Keras

Deep Convolutional GAN with TensorFlow and Keras

Summary

Distributed Models with TensorFlow Clusters

Strategies for distributed execution

TensorFlow clusters

Defining cluster specification

Create the server instances

Define the parameter and operations across servers and devices

Define and train the graph for asynchronous updates

Define and train the graph for synchronous updates

Summary

TensorFlow Models on Mobile and Embedded Platforms

TensorFlow on mobile platforms

TF Mobile in Android apps

TF Mobile demo on Android

TF Mobile in iOS apps

TF Mobile demo on iOS

TensorFlow Lite

TF Lite Demo on Android

TF Lite demo on iOS

Summary

TensorFlow and Keras in R

Installing TensorFlow and Keras packages in R

TF core API in R

TF estimator API in R

Keras API in R

TensorBoard in R

The tfruns package in R

Summary

Debugging TensorFlow Models

Fetching tensor values with tf.Session.run()

Printing tensor values with tf.Print()

Asserting on conditions with tf.Assert()

Debugging with the TensorFlow debugger (tfdbg)

Summary

Tensor Processing Units

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