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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...

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

What do you get with Print?

Instant access to your digital eBook copy whilst your Print order is Shipped

Paperback book shipped to your preferred address

Download this book in EPUB and PDF formats

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Neural Network Programming with TensorFlow

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Total € 99.97

william a rivera Mar 19, 2018

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

sp Nov 23, 2019

Good

Amazonic customer Dec 16, 2019

Great

RT Aug 26, 2018

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).

Alexander Apr 14, 2018

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.

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...`

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...

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

What do you get with Print?

Mastering TensorFlow 1.x

High-Level Libraries for TensorFlow

TF Estimator - previously TF Learn

TF Slim

TFLearn

PrettyTensor

Sonnet

Summary

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

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

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