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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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Profile Icon Ghotra Profile Icon Dua Profile Icon Sujit Pal
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Arrow left icon
Profile Icon Ghotra Profile Icon Dua Profile Icon Sujit Pal
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$38.99
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Paperback Oct 2018 252 pages 1st Edition
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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 NetworkThe 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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Table of Contents

11 Chapters
Keras Installation Chevron down icon Chevron up icon
Working with Keras Datasets and Models Chevron down icon Chevron up icon
Data Preprocessing, Optimization, and Visualization Chevron down icon Chevron up icon
Classification Using Different Keras Layers Chevron down icon Chevron up icon
Implementing Convolutional Neural Networks Chevron down icon Chevron up icon
Generative Adversarial Networks Chevron down icon Chevron up icon
Recurrent Neural Networks Chevron down icon Chevron up icon
Natural Language Processing Using Keras Models Chevron down icon Chevron up icon
Text Summarization Using Keras Models Chevron down icon Chevron up icon
Reinforcement Learning Chevron down icon Chevron up icon
Other Books You May Enjoy Chevron down icon Chevron up icon

Customer reviews

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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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