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Home > Data > Machine Learning > TensorFlow Machine Learning Cookbook
TensorFlow Machine Learning Cookbook
TensorFlow Machine Learning Cookbook

TensorFlow Machine Learning Cookbook: Over 60 practical recipes to help you master Google's TensorFlow machine learning library

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TensorFlow Machine Learning Cookbook

Chapter 2. The TensorFlow Way

In this chapter, we will introduce the key components of how TensorFlow operates. Then we will tie it together to create a simple classifier and evaluate the outcomes. By the end of the chapter you should have learned about the following:

  • Operations in a Computational Graph
  • Layering Nested Operations
  • Working with Multiple Layers
  • Implementing Loss Functions
  • Implementing Back Propagation
  • Working with Batch and Stochastic Training
  • Combining Everything Together
  • Evaluating Models

Introduction

Now that we have introduced how TensorFlow creates tensors, uses variables and placeholders, we will introduce how to act on these objects in a computational graph. From this, we can set up a simple classifier and see how well it performs.

Note

Also, remember that all the code from this book is available online on GitHub at https://github.com/nfmcclure/tensorflow_cookbook.

Operations in a Computational Graph

Now that we can put objects into our computational graph, we will introduce operations that act on such objects.

Getting ready

To start a graph, we load TensorFlow and create a session, as follows:

import tensorflow as tf
sess = tf.Session()

How to do it…

In this example, we will combine what we have learned and feed in each number in a list to an operation in a graph and print the output:

  1. First we declare our tensors and placeholders. Here we will create a numpy array to feed into our operation:
    import numpy as np
x_vals = np.array([1., 3., 5., 7., 9.])
x_data = tf.placeholder(tf.float32)
m_const = tf.constant(3.)
my_product = tf.mul(x_data, m_const)
for x_val in x_vals:
    print(sess.run(my_product, feed_dict={x_data: x_val}))
3.0
9.0
15.0
21.0
27.0

How it works…

Steps 1 and 2 create the data and operations on the computational graph. Then, in step 3, we feed the data through the graph and print the output. Here is what the computational graph...

Layering Nested Operations

In this recipe, we will learn how to put multiple operations on the same computational graph.

Getting ready

It's important to know how to chain operations together. This will set up layered operations in the computational graph. For a demonstration we will multiply a placeholder by two matrices and then perform addition. We will feed in two matrices in the form of a three-dimensional numpy array:

import tensorflow as tf
sess = tf.Session()

How to do it…

It is also important to note how the data will change shape as it passes through. We will feed in two numpy arrays of size 3x5. We will multiply each matrix by a constant of size 5x1, which will result in a matrix of size 3x1. We will then multiply this by 1x1 matrix resulting in a 3x1 matrix again. Finally, we add a 3x1 matrix at the end, as follows:

  1. First we create the data to feed in and the corresponding placeholder:
    my_array = np.array([[1., 3., 5., 7., 9.],
                   [-2., 0., 2., 4., 6.],
...

Working with Multiple Layers

Now that we have covered multiple operations, we will cover how to connect various layers that have data propagating through them.

Getting ready

In this recipe, we will introduce how to best connect various layers, including custom layers. The data we will generate and use will be representative of small random images. It is best to understand these types of operation on a simple example and how we can use some built-in layers to perform calculations. We will perform a small moving window average across a 2D image and then flow the resulting output through a custom operation layer.

In this section, we will see that the computational graph can get large and hard to look at. To address this, we will also introduce ways to name operations and create scopes for layers. To start, load numpy and tensorflow and create a graph, using the following:

import tensorflow as tf
import numpy as np
sess = tf.Session()

How to do it…

  1. First we create our sample 2D image with...

Implementing Loss Functions

Loss functions are very important to machine learning algorithms. They measure the distance between the model outputs and the target (truth) values. In this recipe, we show various loss function implementations in TensorFlow.

Getting ready

In order to optimize our machine learning algorithms, we will need to evaluate the outcomes. Evaluating outcomes in TensorFlow depends on specifying a loss function. A loss function tells TensorFlow how good or bad the predictions are compared to the desired result. In most cases, we will have a set of data and a target on which to train our algorithm. The loss function compares the target to the prediction and gives a numerical distance between the two.

For this recipe, we will cover the main loss functions that we can implement in TensorFlow.

To see how the different loss functions operate, we will plot them in this recipe. We will first start a computational graph and load matplotlib, a python plotting library, as follows:

import...

Implementing Back Propagation

One of the benefits of using TensorFlow, is that it can keep track of operations and automatically update model variables based on back propagation. In this recipe, we will introduce how to use this aspect to our advantage when training machine learning models.

Getting ready

Now we will introduce how to change our variables in the model in such a way that a loss function is minimized. We have learned about how to use objects and operations, and create loss functions that will measure the distance between our predictions and targets. Now we just have to tell TensorFlow how to back propagate errors through our computational graph to update the variables and minimize the loss function. This is done via declaring an optimization function. Once we have an optimization function declared, TensorFlow will go through and figure out the back propagation terms for all of our computations in the graph. When we feed data in and minimize the loss function, TensorFlow will...

Introduction

Now that we have introduced how TensorFlow creates tensors, uses variables and placeholders, we will introduce how to act on these objects in a computational graph. From this, we can set up a simple classifier and see how well it performs.

Note

Also, remember that all the code from this book is available online on GitHub at https://github.com/nfmcclure/tensorflow_cookbook.

Operations in a Computational Graph

Now that we can put objects into our computational graph, we will introduce operations that act on such objects.

Getting ready

To start a graph, we load TensorFlow and create a session, as follows:

import tensorflow as tf
sess = tf.Session()

How to do it…

In this example, we will combine what we have learned and feed in each number in a list to an operation in a graph and print the output:

  1. First we declare our tensors and placeholders. Here we will create a numpy array to feed into our operation:

    import numpy as np
x_vals = np.array([1., 3., 5., 7., 9.])
x_data = tf.placeholder(tf.float32)
m_const = tf.constant(3.)
my_product = tf.mul(x_data, m_const)
for x_val in x_vals:
    print(sess.run(my_product, feed_dict={x_data: x_val}))
3.0
9.0
15.0
21.0
27.0

How it works…

Steps 1 and 2 create the data and operations on the computational graph. Then, in step 3, we feed the data through the graph and print the output. Here is what the computational graph looks like:

Figure...

Layering Nested Operations

In this recipe, we will learn how to put multiple operations on the same computational graph.

Getting ready

It's important to know how to chain operations together. This will set up layered operations in the computational graph. For a demonstration we will multiply a placeholder by two matrices and then perform addition. We will feed in two matrices in the form of a three-dimensional numpy array:

import tensorflow as tf
sess = tf.Session()

How to do it…

It is also important to note how the data will change shape as it passes through. We will feed in two numpy arrays of size 3x5. We will multiply each matrix by a constant of size 5x1, which will result in a matrix of size 3x1. We will then multiply this by 1x1 matrix resulting in a 3x1 matrix again. Finally, we add a 3x1 matrix at the end, as follows:

  1. First we create the data to feed in and the corresponding placeholder:

    my_array = np.array([[1., 3., 5., 7., 9.],
                   [-2., 0., 2., 4., 6.],
             ...
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Key benefits

  • Your quick guide to implementing TensorFlow in your day-to-day machine learning activities
  • Learn advanced techniques that bring more accuracy and speed to machine learning
  • Upgrade your knowledge to the second generation of machine learning with this guide on TensorFlow

Description

TensorFlow is an open source software library for Machine Intelligence. The independent recipes in this book will teach you how to use TensorFlow for complex data computations and will let you dig deeper and gain more insights into your data than ever before. You’ll work through recipes on training models, model evaluation, sentiment analysis, regression analysis, clustering analysis, artificial neural networks, and deep learning – each using Google’s machine learning library TensorFlow. This guide starts with the fundamentals of the TensorFlow library which includes variables, matrices, and various data sources. Moving ahead, you will get hands-on experience with Linear Regression techniques with TensorFlow. The next chapters cover important high-level concepts such as neural networks, CNN, RNN, and NLP. Once you are familiar and comfortable with the TensorFlow ecosystem, the last chapter will show you how to take it to production.

Who is this book for?

This book is ideal for data scientists who are familiar with C++ or Python and perform machine learning activities on a day-to-day basis. Intermediate and advanced machine learning implementers who need a quick guide they can easily navigate will find it useful.

What you will learn

  • Become familiar with the basics of the TensorFlow machine learning library
  • Get to know Linear Regression techniques with TensorFlow
  • Learn SVMs with hands-on recipes
  • Implement neural networks and improve predictions
  • Apply NLP and sentiment analysis to your data
  • Master CNN and RNN through practical recipes
  • Take TensorFlow into production

Product Details

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Publication date : Feb 14, 2017
Length: 370 pages
Edition : 1st
Language : English
ISBN-13 : 9781786466303
Vendor :
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Category :
Data
Languages :
Python
Concepts :
Machine Learning
Tools :
TensorFlow

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Product Details

Publication date : Feb 14, 2017
Length: 370 pages
Edition : 1st
Language : English
ISBN-13 : 9781786466303
Vendor :
Google
Category :
Data
Languages :
Python
Concepts :
Machine Learning
Tools :
TensorFlow

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Table of Contents

12 Chapters
1. Getting Started with TensorFlow Chevron down icon Chevron up icon
1. Getting Started with TensorFlow
Introduction
How TensorFlow Works
Declaring Tensors
Using Placeholders and Variables
Working with Matrices
Declaring Operations
Implementing Activation Functions
Working with Data Sources
Additional Resources
2. The TensorFlow Way Chevron down icon Chevron up icon
2. The TensorFlow Way
Introduction
Operations in a Computational Graph
Layering Nested Operations
Working with Multiple Layers
Implementing Loss Functions
Implementing Back Propagation
Working with Batch and Stochastic Training
Combining Everything Together
Evaluating Models
3. Linear Regression Chevron down icon Chevron up icon
3. Linear Regression
Introduction
Using the Matrix Inverse Method
Implementing a Decomposition Method
Learning The TensorFlow Way of Linear Regression
Understanding Loss Functions in Linear Regression
Implementing Deming regression
Implementing Lasso and Ridge Regression
Implementing Elastic Net Regression
Implementing Logistic Regression
4. Support Vector Machines Chevron down icon Chevron up icon
4. Support Vector Machines
Introduction
Working with a Linear SVM
Reduction to Linear Regression
Working with Kernels in TensorFlow
Implementing a Non-Linear SVM
Implementing a Multi-Class SVM
5. Nearest Neighbor Methods Chevron down icon Chevron up icon
5. Nearest Neighbor Methods
Introduction
Working with Nearest Neighbors
Working with Text-Based Distances
Computing with Mixed Distance Functions
Using an Address Matching Example
Using Nearest Neighbors for Image Recognition
6. Neural Networks Chevron down icon Chevron up icon
6. Neural Networks
Introduction
Implementing Operational Gates
Working with Gates and Activation Functions
Implementing a One-Layer Neural Network
Implementing Different Layers
Using a Multilayer Neural Network
Improving the Predictions of Linear Models
Learning to Play Tic Tac Toe
7. Natural Language Processing Chevron down icon Chevron up icon
7. Natural Language Processing
Introduction
Working with bag of words
Implementing TF-IDF
Working with Skip-gram Embeddings
Working with CBOW Embeddings
Making Predictions with Word2vec
Using Doc2vec for Sentiment Analysis
8. Convolutional Neural Networks Chevron down icon Chevron up icon
8. Convolutional Neural Networks
Introduction
Implementing a Simpler CNN
Implementing an Advanced CNN
Retraining Existing CNNs models
Applying Stylenet/Neural-Style
Implementing DeepDream
9. Recurrent Neural Networks Chevron down icon Chevron up icon
9. Recurrent Neural Networks
Introduction
Implementing RNN for Spam Prediction
Implementing an LSTM Model
Stacking multiple LSTM Layers
Creating Sequence-to-Sequence Models
Training a Siamese Similarity Measure
10. Taking TensorFlow to Production Chevron down icon Chevron up icon
10. Taking TensorFlow to Production
Introduction
Implementing unit tests
Using Multiple Executors
Parallelizing TensorFlow
Taking TensorFlow to Production
Productionalizing TensorFlow – An Example
11. More with TensorFlow Chevron down icon Chevron up icon
11. More with TensorFlow
Introduction
Visualizing graphs in Tensorboard
There's more…
Working with a Genetic Algorithm
Clustering Using K-Means
Solving a System of ODEs
Index Chevron down icon Chevron up icon
Index

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Rating distribution
Full star icon Full star icon Full star icon Half star icon Empty star icon 3.7
(18 Ratings)
5 star 38.9%
4 star 22.2%
3 star 16.7%
2 star 11.1%
1 star 11.1%
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Chax Oct 01, 2017
Full star icon Full star icon Full star icon Full star icon Full star icon 5
good
Amazon Verified review Amazon
Mithun Patel Dec 07, 2017
Full star icon Full star icon Full star icon Full star icon Full star icon 5
Great book
Amazon Verified review Amazon
Antonio Gulli May 21, 2017
Full star icon Full star icon Full star icon Full star icon Full star icon 5
I read many books about Deep Learning, and was looking for an handson book on Tensorflow. This Cookbook is very practical and it explains how to use TF step-by-step. It starts with simple graph computations, then it extends into matrix computations, linear regression, and many additional neural network algorithms including neural networks. Definitively recommended if you want to have a swiss knife reference book
Amazon Verified review Amazon
Amazon Customer Feb 27, 2017
Full star icon Full star icon Full star icon Full star icon Full star icon 5
I am the reviewer of this book,Many examples of this book are very helpful for the beginners of TensorFlow users.
Amazon Verified review Amazon
Satya Kondapalli Mar 09, 2017
Full star icon Full star icon Full star icon Full star icon Full star icon 5
I have ordered and returned some books related TF and Neural Networks. Many books start very high level and lost interest. Finally I found book I am interested in. You can start coding in TF using this book. Once you built your code skills then you can buy more theoretical books and lost somewhere. Note: When framework doing all the work, why do I need to learn every math function. This is a no nonsense book for me. I love it. I have been teaching big data past 6 years. Lately I have been doing some work in spark ML and moving on to TensorFlow. Spark also supporting TensorFrames. Deep learning will soon eat machine learning.I suggest this book for any one start learning TF.Satya , ambariCloud
Amazon Verified review Amazon

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About the author

Profile icon Nick McClure
Nick McClure
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Nick McClure is currently a senior data scientist at PayScale, Inc. in Seattle, WA. Prior to this, he has worked at Zillow Group and Caesar's Entertainment Corporation. He got his degrees in Applied Mathematics from The University of Montana and the College of Saint Benedict and Saint John's University. He has a passion for learning and advocating for analytics, machine learning, and artificial intelligence. Nick occasionally puts his thoughts and musings on his blog or through his Twitter account, @nfmcclure.
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  • To contact us directly if a problem is not resolved, use www.packtpub.com/contact-us
What eBook formats do Packt support? Chevron down icon Chevron up icon

Our eBooks are currently available in a variety of formats such as PDF and ePubs. In the future, this may well change with trends and development in technology, but please note that our PDFs are not Adobe eBook Reader format, which has greater restrictions on security.

You will need to use Adobe Reader v9 or later in order to read Packt's PDF eBooks.

What are the benefits of eBooks? Chevron down icon Chevron up icon
  • You can get the information you need immediately
  • You can easily take them with you on a laptop
  • You can download them an unlimited number of times
  • You can print them out
  • They are copy-paste enabled
  • They are searchable
  • There is no password protection
  • They are lower price than print
  • They save resources and space
What is an eBook? Chevron down icon Chevron up icon

Packt eBooks are a complete electronic version of the print edition, available in PDF and ePub formats. Every piece of content down to the page numbering is the same. Because we save the costs of printing and shipping the book to you, we are able to offer eBooks at a lower cost than print editions.

When you have purchased an eBook, simply login to your account and click on the link in Your Download Area. We recommend you saving the file to your hard drive before opening it.

For optimal viewing of our eBooks, we recommend you download and install the free Adobe Reader version 9.