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Mastering TensorFlow 1.x
Mastering TensorFlow 1.x

Mastering TensorFlow 1.x: Advanced machine learning and deep learning concepts using TensorFlow 1.x and Keras

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Mastering TensorFlow 1.x

High-Level Libraries for TensorFlow

There are several high-level libraries and interfaces (API) for TensorFlow that allow us to build and train models easily and with less amount of code such as TF Learn, TF Slim, Sonnet, PrettyTensor, Keras and recently released TensorFlow Estimators.

We will cover the following high-level libraries in this chapter while dedicating the next chapter to Keras:

  • TF Estimator - previously TF Learn
  • TF Slim
  • TFLearn
  • PrettyTensor
  • Sonnet

We shall provide examples of building the models for MNIST dataset using all of the five libraries. Do not worry about understanding the details of the models yet as we cover the details of models from chapter 4 onwards.

You can follow the code examples in this chapter with the Jupyter Notebook ch-02_TF_High_Level_Libraries included in the code bundle. Try modifying the examples in the notebook to experiment and play...

TF Estimator - previously TF Learn

TF Estimator is a high-level API that makes it simple to create and train models by encapsulating the functionalities for training, evaluating, predicting and exporting. TensorFlow recently re-branded and released the TF Learn package within TensorFlow under the new name TF Estimator, probably to avoid confusion with TFLearn package from tflearn.org. TF Estimator API has made significant enhancements to the original TF Learn package, that are described in the research paper presented in KDD 17 Conference, and can be found at the following link: https://doi.org/10.1145/3097983.3098171.

TF Estimator interface design is inspired from the popular machine learning library SciKit Learn, allowing to create the estimator object from different kinds of available models, and then providing four main functions on any kind of estimator:

  • estimator.fit()
  • ...

TF Slim

TF Slim is a lightweight library built on top of TensorFlow core for defining and training models. TF Slim can be used in conjunction with other TensorFlow low level and high-level libraries such as TF Learn. The TF Slim comes as part of the TensorFlow installation in the package: tf.contrib.slim. Run the following command to check if your TF Slim installation is working:

python3 -c 'import tensorflow.contrib.slim as slim; eval = slim.evaluation.evaluate_once'

TF Slim provides several modules that can be picked and applied independently and mixed with other TensorFlow packages. For example, at the time of writing of this book TF Slim had following major modules:

TF Slim module Module description
arg_scope Provides a mechanism to apply elements to all graph nodes defined under a scope.
layers Provides several different kinds of layers such as fully_connected...

TFLearn

TFLearn is a modular library in Python that is built on top of core TensorFlow.

TFLearn is different from the TensorFlow Learn package which is also known as TF Learn (with one space in between TF and Learn). TFLearn is available at the following link: http://tflearn.org, and the source code is available on GitHub at the following link: https://github.com/tflearn/tflearn.

TFLearn can be installed in Python 3 with the following command:

pip3 install tflearn
To install TFLearn in other environments or from source, please refer to the following link: http://tflearn.org/installation/.

The simple workflow in TFLearn is as follows:

  1. Create an input layer first.
  2. Pass the input object to create further layers.
  3. Add the output layer.
  4. Create the net using an estimator layer such as regression.
  5. Create a model from the net created in the previous step.
  6. Train the model with the model...

PrettyTensor

PrettyTensor provides a thin wrapper on top of TensorFlow. The objects provided by PrettyTensor support a chainable syntax to define neural networks. For example, a model could be created by chaining the layers as shown in the following code:

model = (X.
flatten().
fully_connected(10).
softmax_classifier(n_classes, labels=Y))

PrettyTensor can be installed in Python 3 with the following command:

pip3 install prettytensor

PrettyTensor offers a very lightweight and extensible interface in the form of a method named apply(). Any additional function can be chained to PrettyTensor objects using the .apply(function, arguments) method. PrettyTensor will call the function and supply the current tensor as the first argument to the function.

User-created functions can be added using the @prettytensor.register decorator. Details can be found at https:...

Sonnet

Sonnet is an object-oriented library written in Python. It was released by DeepMind in 2017. Sonnet intends to cleanly separate the following two aspects of building computation graphs from objects:

  • The configuration of objects called modules
  • The connection of objects to computation graphs

Sonnet can be installed in Python 3 with the following command:

pip3 install dm-sonnet
Sonnet can be installed from the source by following directions from the following link: https://github.com/deepmind/sonnet/blob/master/docs/INSTALL.md.

The modules are defined as sub-classes of the abstract class sonnet.AbstractModule. At the time of writing this book, the following modules are available in Sonnet:

Basic modules AddBias, BatchApply, BatchFlatten, BatchReshape, FlattenTrailingDimensions, Linear, MergeDims, SelectInput, SliceByDim, TileByDim, and TrainableVariable
Recurrent modules...

Summary

In this chapter, we did a tour of some of the high-level libraries that are built on top of TensorFlow. We learned about TF Estimator, TF Slim, TFLearn, PrettyTensor, and Sonnet. We implemented the MNIST classification example for all five of them. If you could not understand the details of the models, do not worry, because the models built for MNIST example will be presented again in the following chapters.

We summarize the libraries and frameworks presented in this chapter in the following table:

High-Level Library Documentation Link Source Code Link pip3 install package
TF Estimator https://www.tensorflow.org/get_started/estimator https://github.com/tensorflow/tensorflow/tree/master/tensorflow/python/estimator preinstalled with TensorFlow
TF Slim https://github.com/tensorflow/tensorflow/tree/r1.4/tensorflow/contrib/slim https://github.com/tensorflow/tensorflow...
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Key benefits

  • Delve into advanced machine learning and deep learning use cases using Tensorflow and Keras
  • Build, deploy, and scale end-to-end deep neural network models in a production environment
  • Learn to deploy TensorFlow on mobile, and distributed TensorFlow on GPU, Clusters, and Kubernetes

Description

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.

Who is this book for?

This book is for data scientists, machine learning engineers, artificial intelligence engineers, and for all TensorFlow users who wish to upgrade their TensorFlow knowledge and work on various machine learning and deep learning problems. If you are looking for an easy-to-follow guide that underlines the intricacies and complex use cases of machine learning, you will find this book extremely useful. Some basic understanding of TensorFlow is required to get the most out of the book.

What you will learn

  • Master advanced concepts of deep learning such as transfer learning, reinforcement learning, generative models and more, using TensorFlow and Keras
  • Perform supervised (classification and regression) and unsupervised (clustering) learning to solve machine learning tasks
  • Build end-to-end deep learning (CNN, RNN, and Autoencoders) models with TensorFlow
  • Scale and deploy production models with distributed and high-performance computing on GPU and clusters
  • Build TensorFlow models to work with multilayer perceptrons using Keras, TFLearn, and R
  • Learn the functionalities of smart apps by building and deploying TensorFlow models on iOS and Android devices
  • Supercharge TensorFlow with distributed training and deployment on Kubernetes and TensorFlow Clusters

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Length: 474 pages
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Language : English
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Product Details

Publication date : Jan 22, 2018
Length: 474 pages
Edition : 1st
Language : English
ISBN-13 : 9781788292061
Vendor :
Google
Category :
Languages :
Concepts :
Tools :

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Table of Contents

20 Chapters
TensorFlow 101 Chevron down icon Chevron up icon
High-Level Libraries for TensorFlow Chevron down icon Chevron up icon
Keras 101 Chevron down icon Chevron up icon
Classical Machine Learning with TensorFlow Chevron down icon Chevron up icon
Neural Networks and MLP with TensorFlow and Keras Chevron down icon Chevron up icon
RNN with TensorFlow and Keras Chevron down icon Chevron up icon
RNN for Time Series Data with TensorFlow and Keras Chevron down icon Chevron up icon
RNN for Text Data with TensorFlow and Keras Chevron down icon Chevron up icon
CNN with TensorFlow and Keras Chevron down icon Chevron up icon
Autoencoder with TensorFlow and Keras Chevron down icon Chevron up icon
TensorFlow Models in Production with TF Serving Chevron down icon Chevron up icon
Transfer Learning and Pre-Trained Models Chevron down icon Chevron up icon
Deep Reinforcement Learning Chevron down icon Chevron up icon
Generative Adversarial Networks Chevron down icon Chevron up icon
Distributed Models with TensorFlow Clusters Chevron down icon Chevron up icon
TensorFlow Models on Mobile and Embedded Platforms Chevron down icon Chevron up icon
TensorFlow and Keras in R Chevron down icon Chevron up icon
Debugging TensorFlow Models Chevron down icon Chevron up icon
Tensor Processing Units Chevron down icon Chevron up icon
Other Books You May Enjoy Chevron down icon Chevron up icon

Customer reviews

Rating distribution
Full star icon Full star icon Full star icon Half star icon Empty star icon 3.4
(5 Ratings)
5 star 60%
4 star 0%
3 star 0%
2 star 0%
1 star 40%
william a rivera Mar 19, 2018
Full star icon Full star icon Full star icon Full star icon Full star icon 5
This book provides a nice overview of the topic with practical examples and code for you to follow along. The descriptions are easy to understand. If you are new to the topic you will definitely be exposed to some great material.
Amazon Verified review Amazon
sp Nov 23, 2019
Full star icon Full star icon Full star icon Full star icon Full star icon 5
Good
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Amazonic customer Dec 16, 2019
Full star icon Full star icon Full star icon Full star icon Full star icon 5
Great
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RT Aug 26, 2018
Full star icon Empty star icon Empty star icon Empty star icon Empty star icon 1
Das Layout is grauenvoll - mathematische Formeln sind nicht bündig mite der Zeile (ragen darüber hinaus - mit Latex bekommt das auch ein Laie beser hin).Eigentlich suchte ich ein Buch, daß die API der TensorFlow Core Lib erklärt. Das Buch bleibt aber sehr an der Oberfläche. Es werden auch high-Level APIs wie Keras und Tensor behandelt (zu Lasten der Core Lib). Durch das ungüstige Layuout wird die Seitenzahl unnötig aufgebläht (Buch ist recht dick).
Amazon Verified review Amazon
Alexander Apr 14, 2018
Full star icon Empty star icon Empty star icon Empty star icon Empty star icon 1
Leider nicht sehr brauchbar das Buch. Es wird kaum etwas erklärt, der Inhalt ist eher nur online zusammen kopiert. DIe Codebeispiele sind nicht sehr hilfreich.
Amazon Verified review Amazon
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