Book Image

Mastering TensorFlow 1.x

Book Image

Mastering TensorFlow 1.x

Overview of this book

TensorFlow is the most popular numerical computation library built from the ground up for distributed, cloud, and mobile environments. TensorFlow represents the data as tensors and the computation as graphs. This book is a comprehensive guide that lets you explore the advanced features of TensorFlow 1.x. Gain insight into TensorFlow Core, Keras, TF Estimators, TFLearn, TF Slim, Pretty Tensor, and Sonnet. Leverage the power of TensorFlow and Keras to build deep learning models, using concepts such as transfer learning, generative adversarial networks, and deep reinforcement learning. Throughout the book, you will obtain hands-on experience with varied datasets, such as MNIST, CIFAR-10, PTB, text8, and COCO-Images. You will learn the advanced features of TensorFlow1.x, such as distributed TensorFlow with TF Clusters, deploy production models with TensorFlow Serving, and build and deploy TensorFlow models for mobile and embedded devices on Android and iOS platforms. You will see how to call TensorFlow and Keras API within the R statistical software, and learn the required techniques for debugging when the TensorFlow API-based code does not work as expected. The book helps you obtain in-depth knowledge of TensorFlow, making you the go-to person for solving artificial intelligence problems. By the end of this guide, you will have mastered the offerings of TensorFlow and Keras, and gained the skills you need to build smarter, faster, and efficient machine learning and deep learning systems.

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Table of Contents (21 chapters)
Preface
Preface
Who this book is for
What this book covers
To get the most out of this book
Get in touch
Free Chapter
1
TensorFlow 101
TensorFlow 101
What is TensorFlow?
TensorFlow core
Data flow graph or computation graph
TensorBoard
Summary
2
High-Level Libraries for TensorFlow
High-Level Libraries for TensorFlow
TF Estimator - previously TF Learn
TF Slim
TFLearn
PrettyTensor
Sonnet
Summary
3
Keras 101
Keras 101
Installing Keras
Neural Network Models in Keras
Creating the Keras model
Keras Layers
Adding 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
4
Classical Machine Learning with TensorFlow
Classical Machine Learning with TensorFlow
Simple linear regression
Multi-regression
Regularized regression
Classification using logistic regression
Binary classification
Multiclass classification
Summary
5
Neural Networks and MLP with TensorFlow and Keras
Neural Networks and MLP with TensorFlow and Keras
The perceptron
MultiLayer Perceptron
MLP for image classification
MLP for time series regression
Summary
6
RNN with TensorFlow and Keras
RNN with TensorFlow and Keras
Simple Recurrent Neural Network
RNN variants
LSTM network
GRU network
TensorFlow for RNN
Keras for RNN
Application areas of RNNs
RNN in Keras for MNIST data
Summary
7
RNN for Time Series Data with TensorFlow and Keras
RNN for Time Series Data with TensorFlow and Keras
Airline Passengers 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
8
RNN for Text Data with TensorFlow and Keras
RNN for Text Data with TensorFlow and Keras
Word vector representations
Preparing the data for word2vec models
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
Summary
9
CNN with TensorFlow and Keras
CNN with TensorFlow and Keras
Understanding convolution
Understanding pooling
CNN architecture pattern - LeNet
LeNet for MNIST data
LeNet for CIFAR10 Data
Summary
10
Autoencoder with TensorFlow and Keras
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
11
TensorFlow Models in Production with TF Serving
TensorFlow Models in Production with TF Serving
Saving and Restoring models in TensorFlow
Saving and restoring Keras models
TensorFlow Serving
TF Serving in the Docker containers
TensorFlow Serving on Kubernetes
Summary
12
Transfer Learning and Pre-Trained Models
Transfer Learning and Pre-Trained Models
ImageNet dataset
Retraining or fine-tuning models
COCO animals dataset and pre-processing images
VGG16 in TensorFlow
Image preprocessing in TensorFlow for pre-trained VGG16
VGG16 in Keras
Inception v3 in TensorFlow
Summary
13
Deep Reinforcement Learning
Deep Reinforcement Learning
OpenAI Gym 101
Applying simple policies to a cartpole game
Reinforcement learning 101
Naive Neural Network policy for Reinforcement Learning
Implementing Q-Learning
Summary
14
Generative Adversarial Networks
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
15
Distributed Models with TensorFlow Clusters
Distributed Models with TensorFlow Clusters
Strategies for distributed execution
TensorFlow clusters
Summary
16
TensorFlow Models on Mobile and Embedded Platforms
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
17
TensorFlow and Keras in R
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
18
Debugging TensorFlow Models
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
19
Tensor Processing Units
Tensor Processing Units
20
Other Books You May Enjoy
Other Books You May Enjoy
Leave a review - let other readers know what you think

LeNet for MNIST data

You can follow along with the code in the Jupyter notebook ch-09a_CNN_MNIST_TF_and_Keras.

Prepare the MNIST data into test and train sets:

from tensorflow.examples.tutorials.mnist import input_data
mnist = input_data.read_data_sets(os.path.join('.','mnist'), one_hot=True)
X_train = mnist.train.images
X_test = mnist.test.images
Y_train = mnist.train.labels
Y_test = mnist.test.labels

LeNet CNN for MNIST with TensorFlow

In TensorFlow, apply the following steps to build the LeNet based CNN models for MNIST data:

  1. Define the hyper-parameters, and the placeholders for x and y (input images and output labels):
n_classes = 10 # 0-9 digits
n_width = 28
n_height = 28
n_depth = 1
n_inputs = n_height ...
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