TensorFlow 1.x Deep Learning Cookbook

Overview of this book

Deep neural networks (DNNs) have achieved a lot of success in the field of computer vision, speech recognition, and natural language processing. This exciting recipe-based guide will take you from the realm of DNN theory to implementing them practically to solve real-life problems in the artificial intelligence domain. In this book, you will learn how to efficiently use TensorFlow, Google’s open source framework for deep learning. You will implement different deep learning networks, such as Convolutional Neural Networks (CNNs), Recurrent Neural Networks (RNNs), Deep Q-learning Networks (DQNs), and Generative Adversarial Networks (GANs), with easy-to-follow standalone recipes. You will learn how to use TensorFlow with Keras as the backend. You will learn how different DNNs perform on some popularly used datasets, such as MNIST, CIFAR-10, and Youtube8m. You will not only learn about the different mobile and embedded platforms supported by TensorFlow, but also how to set up cloud platforms for deep learning applications. You will also get a sneak peek at TPU architecture and how it will affect the future of DNNs. By using crisp, no-nonsense recipes, you will become an expert in implementing deep learning techniques in growing real-world applications and research areas such as reinforcement learning, GANs, and autoencoders.

Preface

What this book covers

What you need for this book

Free Chapter

TensorFlow - An Introduction

Introduction

Installing TensorFlow

Hello world in TensorFlow

Understanding the TensorFlow program structure

Working with constants, variables, and placeholders

Performing matrix manipulations using TensorFlow

Using a data flow graph

Migrating from 0.x to 1.x

Using XLA to enhance computational performance

Invoking CPU/GPU devices

TensorFlow for Deep Learning

Different Python packages required for DNN-based problems

Regression

Introduction

Choosing loss functions

Optimizers in TensorFlow

Reading from CSV files and preprocessing data

House price estimation-simple linear regression

House price estimation-multiple linear regression

Logistic regression on the MNIST dataset

Neural Networks - Perceptron

Introduction

Activation functions

Single layer perceptron

Calculating gradients of backpropagation algorithm

MNIST classifier using MLP

Function approximation using MLP-predicting Boston house prices

Tuning hyperparameters

Higher-level APIs-Keras

See also

Convolutional Neural Networks

Introduction

Creating a ConvNet to classify handwritten MNIST numbers

Creating a ConvNet to classify CIFAR-10

Transferring style with VGG19 for image repainting

Using a pretrained VGG16 net for transfer learning

Creating a DeepDream network

Advanced Convolutional Neural Networks

Introduction

Creating a ConvNet for Sentiment Analysis

Inspecting what filters a VGG pre-built network has learned

Classifying images with VGGNet, ResNet, Inception, and Xception

Recycling pre-built Deep Learning models for extracting features

Very deep InceptionV3 Net used for Transfer Learning

Generating music with dilated ConvNets, WaveNet, and NSynth

Answering questions about images (Visual Q&A)

Classifying videos with pre-trained nets in six different ways

Recurrent Neural Networks

Introduction

Neural machine translation - training a seq2seq RNN

Neural machine translation - inference on a seq2seq RNN

All you need is attention - another example of a seq2seq RNN

Learning to write as Shakespeare with RNNs

Learning to predict future Bitcoin value with RNNs

Many-to-one and many-to-many RNN examples

Unsupervised Learning

Introduction

Principal component analysis

k-means clustering

Self-organizing maps

Restricted Boltzmann Machine

Recommender system using RBM

DBN for Emotion Detection

Autoencoders

Introduction

Vanilla autoencoders

Sparse autoencoder

Denoising autoencoder

Convolutional autoencoders

Stacked autoencoder

Reinforcement Learning

Introduction

Learning OpenAI Gym

Implementing neural network agent to play Pac-Man

Q learning to balance Cart-Pole

Game of Atari using Deep Q Networks

Policy gradients to play the game of Pong

Mobile Computation

Introduction

Installing TensorFlow mobile for macOS and Android

Playing with TensorFlow and Android examples

Installing TensorFlow mobile for macOS and iPhone

Optimizing a TensorFlow graph for mobile devices

Profiling a TensorFlow graph for mobile devices

Transforming a TensorFlow graph for mobile devices

Generative Models and CapsNet

Introduction

Learning to forge MNIST images with simple GANs

Learning to forge MNIST images with DCGANs

Learning to forge Celebrity Faces and other datasets with DCGAN

Implementing Variational Autoencoders

Learning to beat the previous MNIST state-of-the-art results with Capsule Networks

Distributed TensorFlow and Cloud Deep Learning

Introduction

Working with TensorFlow and GPUs

Playing with Distributed TensorFlow: multiple GPUs and one CPU

Playing with Distributed TensorFlow: multiple servers

Training a Distributed TensorFlow MNIST classifier

Working with TensorFlow Serving and Docker

Running Distributed TensorFlow on Google Cloud (GCP) with Compute Engine

Running Distributed TensorFlow on Google CloudML

Running Distributed TensorFlow on Microsoft Azure

Running Distributed TensorFlow on Amazon AWS

Learning to Learn with AutoML (Meta-Learning)

Meta-learning with recurrent networks and with reinforcement learning

Meta-learning blocks

Meta-learning novel tasks

Siamese Network

TensorFlow Processing Units

Components of TPUs

Customer Reviews

5 star

4 star

3 star

2 star

1 star

Migrating from 0.x to 1.x

TensorFlow 1.x does not offer backward compatibility. This means that the codes that worked on TensorFlow 0.x may not work on TensorFlow 1.0. So, if you have codes that worked on TensorFlow 0.x, you need to upgrade them (old GitHub repositories or your own codes). This recipe will point out major differences between TensorFlow 0.x and TensorFlow 1.0 and will show you how to use the script tf_upgrade.py to automatically upgrade the code for TensorFlow 1.0.

How to do it...

Here is how we proceed with the recipe:

First, download tf_upgrade.py from https://github.com/tensorflow/tensorflow/tree/master/tensorflow/tools/compatibility.
If you want to convert one file from TensorFlow 0.x to TensorFlow 1.0, use the following command at the command line:

python tf_upgrade.py --infile old_file.py --outfile upgraded_file.py

For example, if you have a TensorFlow program file named test.py, you will use the preceding command as follows:

python tf_upgrade.py --infile test.py --outfile test_1.0.py

This will result in the creation of a new file named test_1.0.py.
If you want to migrate all the files of a directory, then use the following at the command line:

python tf_upgrade.py --intree InputDIr --outtree OutputDir
 # For example, if you have a directory located at /home/user/my_dir you can migrate all the python files in the directory located at /home/user/my-dir_1p0 using the above command as:
python tf_upgrade.py --intree /home/user/my_dir --outtree /home/user/my_dir_1p0

In most cases, the directory also contains dataset files; you can ensure that non-Python files are copied as well in the new directory (my-dir_1p0 in the preceding example) using the following:

python tf_upgrade.py --intree /home/user/my_dir --outtree /home/user/my_dir_1p0 -copyotherfiles True

In all these cases, a report.txt file is generated. This file contains the details of conversion and any errors in the process.
Read the report.txt file and manually upgrade the part of the code that the script is unable to update.

There's more...

tf_upgrade.py has certain limitations:

It cannot change the arguments of tf.reverse(): you will have to manually fix it
For methods with argument list reordered, like tf.split() and tf.reverse_split(), it will try to introduce keyword arguments, but it cannot actually reorder the arguments
You will have to manually replace constructions like tf.get.variable_scope().reuse_variables() with the following:

with tf.variable_scope(tf.get_variable_scope(), resuse=True):

TensorFlow 1.x Deep Learning Cookbook

TensorFlow 1.x Deep Learning Cookbook

Overview of this book

Related Content you might be interested in

Current Title:

TensorFlow 1.x Deep Learning Cookbook

Deep Learning with TensorFlow 2 and Keras

Deep Learning with Keras

Deep Learning with TensorFlow and Keras