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Home > Data > Deep Learning > Keras Deep Learning Cookbook
Keras Deep Learning Cookbook
Keras Deep Learning Cookbook

Keras Deep Learning Cookbook: Over 30 recipes for implementing deep neural networks in Python

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Keras Deep Learning Cookbook

Working with Keras Datasets and Models

In this chapter, we will cover the following recipes:

  • CIFAR-10 dataset
  • CIFAR-100 dataset
  • MNIST dataset
  • Load data from a CSV file
  • Models in Keras - getting started
  • Sequential models
  • Shared layer models
  • Keras functional APIs
  • Keras functional APIs - linking the layers
  • Image classification using Keras functional APIs

Introduction

In this chapter, we will explore various datasets available by default in Keras and how to load and use them.

CIFAR-10 dataset

Load the CIFAR-10 small images classification dataset from https://www.cs.toronto.edu/~kriz/cifar-10-python.tar.gz. The CIFAR-10 dataset is made up of 60,000 32 x 32 color images in 10 classes, and there are 6000 images per class. The dataset consists of 50,000 training images and 10,000 test images.

The dataset has been divided into five training batches and one test batch, each with 10,000 images. The test batch contains 1,000 randomly selected images from each class. The training batches contain the rest of the images in a random order; some training batches may contain more images from one class than another. The training batches contain 5,000 images from each class, such as shown in the following image:

Reference: https://www.cs.toronto.edu/~kriz/cifar.html.

...

CIFAR-100 dataset

A training dataset of 50,000 32 x 32 pixel color images labeled over 100 categories and 10,000 test images, this dataset is similar to CIFAR-10, but it has 100 classes with 600 images in each class. Five-hundred training images and 100 testing images are in each class. The 100 classes in CIFAR-100 are grouped into 20 superclasses. Each image comes with a coarse label (the superclass to which it belongs) and a fine label (the class to which it belongs).
A list of classes in CIFAR-100 is as follows:

Superclass Classes
aquatic mammals beaver, dolphin, otter, seal, and whale
fish aquarium fish, flatfish, ray, shark, and trout
flowers orchids, poppies, roses, sunflowers, and tulips
food containers bottles, bowls, cans, cups, and plates
fruit and vegetables apples, mushrooms, oranges, pears, and sweet peppers
household electrical devices clock, computer...

MNIST dataset

MNIST is a dataset of 60,000 28 x 28 pixel grayscale images of 10 digits. It also contains a test set of 10,000 images. The dataset consists of the following four files:

Data in these files is stored in the IDX format. The IDX file format is a format for vectors and multidimensional matrices of various numerical types. You can find more info...

Load data from a CSV file

Keras can take data directly from a numpy array in addition to preexisting datasets.

How to do it...

Let's take an existing .csv file from the internet and use it to create a Keras dataset:

dataset = numpy.loadtxt("https://raw.githubusercontent.com/jbrownlee/Datasets/master/pima-indians-diabetes.data.csv", delimiter=",")
# split into input (X) and output (Y) variables
X = dataset[:,0:8]
Y = dataset[:,8]

Note that the dataset can be directly loaded from the URL with the .csv file.

The output of the preceding code is listed in the following snippet:

[ 6. 148. 72. 35. 0. 33.6 0.627 50. ]
1.0

Models in Keras – getting started

Let's look at creating a basic model in Keras.

Anatomy of a model

Model is a subclass of Network. The Model class adds training and evaluation routines to a Network. The following diagram shows the relationship between the various classes.

A network is not a class that developers use directly, so some info in this section is for your information only.

Types of models

There are two types of models in Keras:

  • Sequential models
  • Models created using functional APIs
...

Sequential models

A Sequential model can be created by passing a stack of layers to the constructor of a class called Sequential. 

How to do it...

Creating a basic Sequential mode involves specifying one or more layers.

Create a Sequential model

We will create a Sequential network with four layers.

  1. Layer 1 is a dense layer which has input_shape of (*, 784) and an output_shape of (*, 32)
A dense layer is a regular densely-connected neural network layer. A Dense layer implements the operation output = activation(dot(input, kernel) + bias), where activation...

Shared layer models

Multiple layers in Keras can share the output from one layer. There can be multiple different feature extraction layers from an input, or multiple layers can be used to predict the output from a feature extraction layer.

Let's look at both of these examples.

Introduction – shared input layer

In this section, we show how multiple convolutional layers with differently sized kernels interpret an image input. The model takes colored CIFAR images with a size of 32 x 32 x 3 pixels. There are two CNN feature extraction submodels that share this input; the first has a kernel size of 4, the second a kernel size of 8. The outputs from these feature extraction sub-models are flattened into vectors and...

Introduction

In this chapter, we will explore various datasets available by default in Keras and how to load and use them.

CIFAR-10 dataset

Load the CIFAR-10 small images classification dataset from https://www.cs.toronto.edu/~kriz/cifar-10-python.tar.gz. The CIFAR-10 dataset is made up of 60,000 32 x 32 color images in 10 classes, and there are 6000 images per class. The dataset consists of 50,000 training images and 10,000 test images.

 

The dataset has been divided into five training batches and one test batch, each with 10,000 images. The test batch contains 1,000 randomly selected images from each class. The training batches contain the rest of the images in a random order; some training batches may contain more images from one class than another. The training batches contain 5,000 images from each class, such as shown in the following image:

Reference: https://www.cs.toronto.edu/~kriz/cifar.html.

How to do it...

Let's load this dataset using the Keras APIs and print the shape and size:

from keras.datasets import cifar10

(X_train, y_train), (X_test, y_test) = cifar10.load_data()
print("X_train shape: " + str...

CIFAR-100 dataset

A training dataset of 50,000 32 x 32 pixel color images labeled over 100 categories and 10,000 test images, this dataset is similar to CIFAR-10, but it has 100 classes with 600 images in each class. Five-hundred training images and 100 testing images are in each class. The 100 classes in CIFAR-100 are grouped into 20 superclasses. Each image comes with a coarse label (the superclass to which it belongs) and a fine label (the class to which it belongs). A list of classes in CIFAR-100 is as follows:

Superclass

Classes

aquatic mammals

beaver, dolphin, otter, seal, and whale

fish

aquarium fish, flatfish, ray, shark, and trout

flowers

orchids, poppies, roses, sunflowers, and tulips

food containers

bottles, bowls, cans, cups, and plates

fruit and vegetables

apples, mushrooms, oranges, pears, and sweet peppers

household electrical devices

clock, computer keyboard, lamp, telephone, and television

household furniture

bed, chair, couch, table, and wardrobe

insects

bee, beetle, butterfly, caterpillar...

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Key benefits

  • Understand different neural networks and their implementation using Keras
  • Explore recipes for training and fine-tuning your neural network models
  • Put your deep learning knowledge to practice with real-world use-cases, tips, and tricks

Description

Keras has quickly emerged as a popular deep learning library. Written in Python, it allows you to train convolutional as well as recurrent neural networks with speed and accuracy. The Keras Deep Learning Cookbook shows you how to tackle different problems encountered while training efficient deep learning models, with the help of the popular Keras library. Starting with installing and setting up Keras, the book demonstrates how you can perform deep learning with Keras in the TensorFlow. From loading data to fitting and evaluating your model for optimal performance, you will work through a step-by-step process to tackle every possible problem faced while training deep models. You will implement convolutional and recurrent neural networks, adversarial networks, and more with the help of this handy guide. In addition to this, you will learn how to train these models for real-world image and language processing tasks. By the end of this book, you will have a practical, hands-on understanding of how you can leverage the power of Python and Keras to perform effective deep learning

Who is this book for?

Keras Deep Learning Cookbook is for you if you are a data scientist or machine learning expert who wants to find practical solutions to common problems encountered while training deep learning models. A basic understanding of Python and some experience in machine learning and neural networks is required for this book.

What you will learn

  • Install and configure Keras in TensorFlow
  • Master neural network programming using the Keras library
  • Understand the different Keras layers
  • Use Keras to implement simple feed-forward neural networks, CNNs and RNNs
  • Work with various datasets and models used for image and text classification
  • Develop text summarization and reinforcement learning models using Keras
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Publication date : Oct 31, 2018
Length: 252 pages
Edition : 1st
Language : English
ISBN-13 : 9781788621755
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Category :
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Languages :
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Concepts :
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Tools :
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Estimated delivery fee Deliver to Australia

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Product Details

Publication date : Oct 31, 2018
Length: 252 pages
Edition : 1st
Language : English
ISBN-13 : 9781788621755
Vendor :
Google
Category :
Data
Languages :
Python
Concepts :
Deep Learning
Tools :
Keras

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Table of Contents

11 Chapters
Keras Installation Chevron down icon Chevron up icon
Keras Installation
Introduction
Installing Keras on Ubuntu 16.04
Installing Keras with Jupyter Notebook in a Docker image
Installing Keras on Ubuntu 16.04 with GPU enabled
Working with Keras Datasets and Models Chevron down icon Chevron up icon
Working with Keras Datasets and Models
Introduction
CIFAR-10 dataset
CIFAR-100 dataset
MNIST dataset
Load data from a CSV file
Models in Keras – getting started
Sequential models
Shared layer models
Keras functional APIs
Keras functional APIs – linking the layers
Image classification using Keras functional APIs
Data Preprocessing, Optimization, and Visualization Chevron down icon Chevron up icon
Data Preprocessing, Optimization, and Visualization
Feature standardization of image data
Sequence padding
Model visualization
Optimization 
Common code for samples
Optimization with stochastic gradient descent
Optimization with Adam
Optimization with AdaDelta
Optimization with RMSProp
Classification Using Different Keras Layers Chevron down icon Chevron up icon
Classification Using Different Keras Layers
Introduction
Classification for breast cancer
Classification for spam detection
Implementing Convolutional Neural Networks Chevron down icon Chevron up icon
Implementing Convolutional Neural Networks
Introduction
Cervical cancer classification
Digit recognition
Generative Adversarial Networks Chevron down icon Chevron up icon
Generative Adversarial Networks
Introduction
Basic GAN
Boundary seeking GAN
DCGAN
Recurrent Neural Networks Chevron down icon Chevron up icon
Recurrent Neural Networks
Introduction
Simple RNNs for time series data
LSTM networks for time series data
Time series forecasting with LSTM
Sequence to sequence learning for the same length output with LSTM
Natural Language Processing Using Keras Models Chevron down icon Chevron up icon
Natural Language Processing Using Keras Models
Introduction
Word embedding
Sentiment analysis
Text Summarization Using Keras Models Chevron down icon Chevron up icon
Text Summarization Using Keras Models
Introduction
Text summarization for reviews
Reinforcement Learning Chevron down icon Chevron up icon
Reinforcement Learning
Introduction
The CartPole game with Keras
Dueling DQN to play Cartpole 
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Anubhav Srivastava Nov 16, 2018
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Pretty much the worst book I have come across on this subject. There is literally no explanation of any of the code - this is just a massive dump of the author's Github. While I understand this is a "cookbook", you still need to explain some of the intricacies of the code. for e.g. why is the input share of LSTM different than say Dense layer? When do you want to use return_sequences=True or False? There is very little commentary on the logic behind why a certain code or variable is used.Very low effort work and super disappointing.
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About the 3 authors

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Profile icon Ghotra
Ghotra
Manpreet Singh Ghotra has more than 15 years experience in software development for both enterprise and big data software. He is currently working at Salesforce on developing a machine learning platform/APIs using open source libraries and frameworks such as Keras, Apache Spark, and TensorFlow. He has worked on various machine learning systems, including sentiment analysis, spam detection, and anomaly detection. He was part of the machine learning group at one of the largest online retailers in the world, working on transit time calculations using Apache Mahout, and the R recommendation system, again using Apache Mahout. With a master's and postgraduate degree in machine learning, he has contributed to, and worked for, the machine learning community.
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Profile icon Dua
Dua
Rajdeep Dua has over 18 years experience in the cloud and big data space. He has taught Spark and big data at some of the most prestigious tech schools in India: IIIT Hyderabad, ISB, IIIT Delhi, and Pune College of Engineering. He currently leads the developer relations team at Salesforce India. He has also presented BigQuery and Google App Engine at the W3C conference in Hyderabad. He led the developer relations teams at Google, VMware, and Microsoft, and has spoken at hundreds of other conferences on the cloud. Some of the other references to his work can be seen at Your Story and on ACM digital library. His contributions to the open source community relate to Docker, Kubernetes, Android, OpenStack, and Cloud Foundry.
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Sujit Pal
Sujit Pal is a Technology Research Director at Elsevier Labs, an advanced technology group within the Reed-Elsevier Group of companies. His interests include semantic search, natural language processing, machine learning, and deep learning. At Elsevier, he has worked on several initiatives involving search quality measurement and improvement, image classification and duplicate detection, and annotation and ontology development for medical and scientific corpora.
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The orders shipped to the countries that are listed under EU27 will not bear custom charges. They are paid by Packt as part of the order.

List of EU27 countries: www.gov.uk/eu-eea:

A custom duty or localized taxes may be applicable on the shipment and would be charged by the recipient country outside of the EU27 which should be paid by the customer and these duties are not included in the shipping charges been charged on the order.

How do I know my custom duty charges? Chevron down icon Chevron up icon

The amount of duty payable varies greatly depending on the imported goods, the country of origin and several other factors like the total invoice amount or dimensions like weight, and other such criteria applicable in your country.

For example:

  • If you live in Mexico, and the declared value of your ordered items is over $ 50, for you to receive a package, you will have to pay additional import tax of 19% which will be $ 9.50 to the courier service.
  • Whereas if you live in Turkey, and the declared value of your ordered items is over € 22, for you to receive a package, you will have to pay additional import tax of 18% which will be € 3.96 to the courier service.
How can I cancel my order? Chevron down icon Chevron up icon

Cancellation Policy for Published Printed Books:

You can cancel any order within 1 hour of placing the order. Simply contact customercare@packt.com with your order details or payment transaction id. If your order has already started the shipment process, we will do our best to stop it. However, if it is already on the way to you then when you receive it, you can contact us at customercare@packt.com using the returns and refund process.

Please understand that Packt Publishing cannot provide refunds or cancel any order except for the cases described in our Return Policy (i.e. Packt Publishing agrees to replace your printed book because it arrives damaged or material defect in book), Packt Publishing will not accept returns.

What is your returns and refunds policy? Chevron down icon Chevron up icon

Return Policy:

We want you to be happy with your purchase from Packtpub.com. We will not hassle you with returning print books to us. If the print book you receive from us is incorrect, damaged, doesn't work or is unacceptably late, please contact Customer Relations Team on customercare@packt.com with the order number and issue details as explained below:

  1. If you ordered (eBook, Video or Print Book) incorrectly or accidentally, please contact Customer Relations Team on customercare@packt.com within one hour of placing the order and we will replace/refund you the item cost.
  2. Sadly, if your eBook or Video file is faulty or a fault occurs during the eBook or Video being made available to you, i.e. during download then you should contact Customer Relations Team within 14 days of purchase on customercare@packt.com who will be able to resolve this issue for you.
  3. You will have a choice of replacement or refund of the problem items.(damaged, defective or incorrect)
  4. Once Customer Care Team confirms that you will be refunded, you should receive the refund within 10 to 12 working days.
  5. If you are only requesting a refund of one book from a multiple order, then we will refund you the appropriate single item.
  6. Where the items were shipped under a free shipping offer, there will be no shipping costs to refund.

On the off chance your printed book arrives damaged, with book material defect, contact our Customer Relation Team on customercare@packt.com within 14 days of receipt of the book with appropriate evidence of damage and we will work with you to secure a replacement copy, if necessary. Please note that each printed book you order from us is individually made by Packt's professional book-printing partner which is on a print-on-demand basis.

What tax is charged? Chevron down icon Chevron up icon

Currently, no tax is charged on the purchase of any print book (subject to change based on the laws and regulations). A localized VAT fee is charged only to our European and UK customers on eBooks, Video and subscriptions that they buy. GST is charged to Indian customers for eBooks and video purchases.

What payment methods can I use? Chevron down icon Chevron up icon

You can pay with the following card types:

  1. Visa Debit
  2. Visa Credit
  3. MasterCard
  4. PayPal
What is the delivery time and cost of print books? Chevron down icon Chevron up icon

Shipping Details

USA:

'

Economy: Delivery to most addresses in the US within 10-15 business days

Premium: Trackable Delivery to most addresses in the US within 3-8 business days

UK:

Economy: Delivery to most addresses in the U.K. within 7-9 business days.
Shipments are not trackable

Premium: Trackable delivery to most addresses in the U.K. within 3-4 business days!
Add one extra business day for deliveries to Northern Ireland and Scottish Highlands and islands

EU:

Premium: Trackable delivery to most EU destinations within 4-9 business days.

Australia:

Economy: Can deliver to P. O. Boxes and private residences.
Trackable service with delivery to addresses in Australia only.
Delivery time ranges from 7-9 business days for VIC and 8-10 business days for Interstate metro
Delivery time is up to 15 business days for remote areas of WA, NT & QLD.

Premium: Delivery to addresses in Australia only
Trackable delivery to most P. O. Boxes and private residences in Australia within 4-5 days based on the distance to a destination following dispatch.

India:

Premium: Delivery to most Indian addresses within 5-6 business days

Rest of the World:

Premium: Countries in the American continent: Trackable delivery to most countries within 4-7 business days

Asia:

Premium: Delivery to most Asian addresses within 5-9 business days

Disclaimer:
All orders received before 5 PM U.K time would start printing from the next business day. So the estimated delivery times start from the next day as well. Orders received after 5 PM U.K time (in our internal systems) on a business day or anytime on the weekend will begin printing the second to next business day. For example, an order placed at 11 AM today will begin printing tomorrow, whereas an order placed at 9 PM tonight will begin printing the day after tomorrow.


Unfortunately, due to several restrictions, we are unable to ship to the following countries:

  1. Afghanistan
  2. American Samoa
  3. Belarus
  4. Brunei Darussalam
  5. Central African Republic
  6. The Democratic Republic of Congo
  7. Eritrea
  8. Guinea-bissau
  9. Iran
  10. Lebanon
  11. Libiya Arab Jamahriya
  12. Somalia
  13. Sudan
  14. Russian Federation
  15. Syrian Arab Republic
  16. Ukraine
  17. Venezuela