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Hands-On Deep Learning with TensorFlow
Hands-On Deep Learning with TensorFlow

Hands-On Deep Learning with TensorFlow: Uncover what is underneath your data!

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Profile Icon Dan Van Boxel
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Paperback Jul 2017 174 pages 1st Edition
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Hands-On Deep Learning with TensorFlow

Chapter 2. Deep Neural Networks

In the previous chapter, we looked at simple TensorFlow operations and how to use logistic regression on our font classification problem. In this chapter, we will dive into one of the most popular and successful machine learning approaches—neural networks. Using TensorFlow, we'll build both simple and deep neural networks to improve our model of the font classification problem. Here, we will put the basics of neural networks into practice. We will also build and train our first neural network with TensorFlow. We will then move on to a neural network with a hidden layer of neurons and understand it completely. When completed, you will have a better grasp of the following topics:

  • Basic neural networks
  • The single hidden layer model
  • The single hidden layer explained
  • The multiple hidden layer model
  • Results of the multiple hidden layer

In our first section, we'll review the basics of neural networks. You will learn common ways to transform...

Basic neural networks

Our logistic regression model worked well enough, but was fundamentally linear in nature. Doubling the intensity of a pixel doubled its contribution to the score, but we might only really care if a pixel was above a certain threshold or put more weight on changes to small values. Linearity may not capture all the nuances of the problem. One way to handle this issue is to transform our input with a nonlinear function. Let's look at a simple example in TensorFlow.

First, be sure to load the required modules (tensorflow, numpy, and math) and start an interactive session:

import tensorflow as tf
import numpy as np
import math

sess = tf.InteractiveSession()

In the following example, we create three five-long vectors of normal random numbers, truncated to keep them from being too extreme, with different centers:

x1 = tf.Variable(tf.truncated_normal([5],
                 mean=3, stddev=1./math.sqrt(5)))
x2 = tf.Variable(tf.truncated_normal([5],
                 mean=-1...

Single hidden layer model

Here, we'll put the basics of neural network into practice. We'll adapt the logistic regression TenserFlow code into a single hidden layer of neurons. Then, you'll learn the idea behind backpropagation to compute the weights, that is, train the net. Finally, you'll train your first true neural network in TensorFlow.

The TensorFlow code for this section should look familiar. It's just a slightly evolved version of the logistic regression code. Let's look at how to add a hidden layer of neurons that will compute nonlinear combinations of our input pixels.

You should start with a fresh Python session, execute the code to read in, and set up the data as in the logistic model. It's the same code, just copied to the new file:

import tensorflow as tf
import numpy as np
import math
from tqdm import tqdm
%autoindent
try:
    from tqdm import tqdm
except ImportError:
    def tqdm(x, *args, **kwargs):
        return x

You can always go back...

Single hidden layer explained

In this section, we'll carefully look at the model we built. First, we'll verify the overall accuracy of our model, then we'll see where the model goes wrong. Finally, we'll visualize the weights associated with several neurons to see what they're looking for:

plt.figure(figsize=(6, 6))
plt.plot(train_acc,'bo')
plt.plot(test_acc,'rx')

Make sure that you've trained your model as we did in the previous section, if not, you might want to stop here and do that first. Because we evaluated our model accuracy every 10 training epochs and saved the result, it's now easy to explore how our model has evolved.

Using Matplotlib, we can plot both the training accuracy (the blue dots) and testing accuracy (the red dots) on the same figure:

Single hidden layer explained

Again, if you don't have Matplotlib, that's okay. You can just look at the array values themselves. Note that the training accuracy (blue in color) is usually a little better...

The multiple hidden layer model

In this section, we'll show you how to build even more complex models with additional hidden layers. We'll adapt our single hidden layer model into a multilayer model known as a deep neural network. Then, we'll discuss choosing how many neurons and layers to use. Finally, we'll train the model itself, being patient, as this might take a while to compute.

Remember when we added a hidden layer of neurons to our logistic regression model? Well, we can do that again, adding another layer to our single hidden layer model. Once you have more than one layer of neurons, we call this a deep neural network. However, everything you learned before can be applied now. As in the previous sections of this chapter, you should make a fresh Python session and execute the code up to num_hidden1 in this section's code file. Then the fun starts.

The multiple hidden layer model

Exploring the multiple hidden layer model

Let's start by changing the old num_hidden to num_hidden1 to indicate...

Results of the multiple hidden layer

Now, we'll look into what's going on inside a deep neural network. First, we'll verify the model accuracy. Then, we'll visualize and study the pixel weights. Finally, we'll look at the output weights as well.

After you've trained your deep neural network, let's take a look at the model accuracy. We'll do this the same way that we did for the single hidden layer model. The only difference this time, is that we have many more saved samples of the training and testing accuracy, having gone from many more epochs.

As always, don't worry if you don't have Matplotlib; printing parts of the arrays is fine.

Understanding the multiple hidden layers graph

Execute the following code to see the result:

# Plot the accuracy curves
plt.figure(figsize=(6,6))
plt.plot(train_acc,'bo')
plt.plot(test_acc,'rx')
Understanding the multiple hidden layers graph

From the preceding output graph, we reach about 68 percent training accuracy and maybe 63 percent validation...

Summary

In this chapter, we embraced deep learning with TensorFlow. Though we started with the simple model of one hidden layer of neurons, it didn't take you long to develop and train a deep neural network for the font classification problem.

You learned about the single and multiple hidden layer model and understood those in detail. You also understand the different types of neural networks and built and trained our first neural network with TensorFlow.

In the next chapter, we'll prove our model with convolutional neural networks, a powerful tool for image classification.

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Key benefits

  • Explore various possibilities with deep learning and gain amazing insights from data using Google’s brainchild-- TensorFlow
  • Want to learn what more can be done with deep learning? Explore various neural networks with the help of this comprehensive guide
  • Rich in concepts, advanced guide on deep learning that will give you background to innovate in your environment

Description

Dan Van Boxel’s Deep Learning with TensorFlow is based on Dan’s best-selling TensorFlow video course. With deep learning going mainstream, making sense of data and getting accurate results using deep networks is possible. Dan Van Boxel will be your guide to exploring the possibilities with deep learning; he will enable you to understand data like never before. With the efficiency and simplicity of TensorFlow, you will be able to process your data and gain insights that will change how you look at data. With Dan’s guidance, you will dig deeper into the hidden layers of abstraction using raw data. Dan then shows you various complex algorithms for deep learning and various examples that use these deep neural networks. You will also learn how to train your machine to craft new features to make sense of deeper layers of data. In this book, Dan shares his knowledge across topics such as logistic regression, convolutional neural networks, recurrent neural networks, training deep networks, and high level interfaces. With the help of novel practical examples, you will become an ace at advanced multilayer networks, image recognition, and beyond.

Who is this book for?

If you are a data scientist who performs machine learning on a regular basis, are familiar with deep neural networks, and now want to gain expertise in working with convoluted neural networks, then this book is for you. Some familiarity with C++ or Python is assumed.

What you will learn

  • Set up your computing environment and install TensorFlow
  • Build simple TensorFlow graphs for everyday computations
  • Apply logistic regression for classification with TensorFlow
  • Design and train a multilayer neural network with TensorFlow
  • Intuitively understand convolutional neural networks for image recognition
  • Bootstrap a neural network from simple to more accurate models
  • See how to use TensorFlow with other types of networks
  • Program networks with SciKit-Flow, a high-level interface to TensorFlow
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Table of Contents

6 Chapters
1. Getting Started Chevron down icon Chevron up icon
2. Deep Neural Networks Chevron down icon Chevron up icon
3. Convolutional Neural Networks Chevron down icon Chevron up icon
4. Introducing Recurrent Neural Networks Chevron down icon Chevron up icon
5. Wrapping Up Chevron down icon Chevron up icon
Index Chevron down icon Chevron up icon

Customer reviews

Rating distribution
Full star icon Full star icon Half star icon Empty star icon Empty star icon 2.8
(4 Ratings)
5 star 0%
4 star 25%
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1 star 25%
Amazon buyer Sep 28, 2018
Full star icon Full star icon Full star icon Full star icon Empty star icon 4
Practical approach
Amazon Verified review Amazon
Maxwell B. Anselm May 11, 2018
Full star icon Full star icon Full star icon Empty star icon Empty star icon 3
If you've done some reading about machine learning and already know the gist of how neural networks work, this book will get you up to speed with some simple, practical examples. It's very light and handwavy on the theory, so I wouldn't recommend it to someone who is completely fresh to the topic. But I liked that it took a very hands-on approach with the code examples, explaining every line and generally doing things "the hard way" so that you actually felt like you were in control of the networks you set up.I followed along using the latest TensorFlow libraries available on Arch Linux and I was surprised that the examples were already a little out of date. I got quite a few deprecation warnings and one example was straight up broken because of outdated syntax, but it was easy to figure out how to fix everything.I happen to know that the author originally presented the material as a video series and this book was transcribed by a third party and... unfortunately, it shows. There are some weird wordings that I can only assume were incorrectly transcribed, and some of the text refers to code examples that must be downloaded separately as if they're right there in the text. But again, nothing got in the way of understanding the material.
Amazon Verified review Amazon
Dimitri Shvorob Mar 11, 2018
Full star icon Full star icon Full star icon Empty star icon Empty star icon 3
Wishing to learn about TensorFlow, I decided to survey TF books available from Amazon, and pick one or two for further study. I excluded self-published offerings, and ended up with this longish list, dominated by Packt titles:"Machine Learning with TensorFlow" by Shukla, published by Manning in 2018-02, 272 pp, $43"Mastering TensorFlow 1.x" by Fandango, Packt, 2018-01, 474 pp, $35"Pro Deep Learning with TensorFlow" by Pattanayak, Apress, 2017-12, 398 pp, $37"TensorFlow 1.x Deep Learning Cookbook" by Gulli and Kapoor, Packt, 2017-12, 536 pp, $32"Neural Network Programming with TensorFlow" by Ghotra and Dua, Packt, 2017-11, 274 pp, $40"Predictive Analytics with TensorFlow" by Karim, Packt, 2017-11, 522 pp, $50"Machine Learning with TensorFlow 1.x" by Hua and Azeem, Packt, 2017-11, 304 pp, $39"Learning TensorFlow" by Hope and Resheff, O'Reilly, 2017-08, 242 pp, $25"Hands-On Deep Learning with TensorFlow" by Van Boxel, Packt, 2017-07, 174 pp, $35"Deep Learning with TensorFlow" by Zaccone, Karim and Menshawy, Packt, 2017-04, 320 pp, $50"TensorFlow Machine Learning Cookbook" by McClure, Packt, 2017-02, 370 pp, $30"Building Machine Learning Projects with TensorFlow" by Bonnin, Packt, 2016-11, 291 pp, $35"Getting Started with TensorFlow" by Zaccone, Packt, 2016-07, 180 pp, $35I reviewed the doc on tensorflow.org - including the doc for older releases - then started looking at books. One week later, I am still not done, but some options can already be discarded."Hands-On Deep Learning with TensorFlow" is one of them. The book is the thinnest of the bunch; with just 174 Packt pages - equivalent to under 100 of "regular" ones - to play with, it cannot really be a TensorFlow reference, only a (sketchy) TensorFlow introduction. In this case, page count is kept down by (with one exception) focusing on a single problem, MNIST character recognition. Despite a recent release date, the book does not cover the higher-level APIs of Estimators and Datasets, and adopts the "old school", low-level approach. It is really not bad, and does add value to the doc (for release 1.3 or so) and online treatments of "TensorFlow vs. MNIST", but the truth is, for $35, you can find something more substantial. Consider "Hands-On Deep Learning with TensorFlow" if you see it on sale.
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Lipin Pius Feb 28, 2018
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Please don't buy this book if you're looking for some good learning material
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