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- Herausgeber: Packt Publishing
- Kategorie: Fachliteratur
- Sprache: Englisch
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
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Seitenzahl: 216
Veröffentlichungsjahr: 2018
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Keras Deep Learning Cookbook
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First published: October 2018
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Contributors
About the authors
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.
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.
About the reviewer
Sujit Pal works at Elsevier Labs, a research and development group within the Reed-Elsevier PLC Group. His interests are in information retrieval, distributed processing, ontology development, natural language processing, and machine learning, and codes in Python, Scala, and Java. He combines his skills in these areas in order to help build new features or feature improvements in different products across the company. He believes in lifelong learning and blogs about his experiences at sujitpal.blogspot.com.
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Table of Contents
Title Page
Copyright and Credits
Keras Deep Learning Cookbook
Packt Upsell
Why subscribe?
Packt.com
Contributors
About the authors
About the reviewer
Packt is searching for authors like you
Preface
Who this book is for
What this book covers
To get the most out of this book
Download the example code files
Download the color images
Conventions used
Sections
Getting ready
How to do it…
How it works…
There's more…
See also
Get in touch
Reviews
Keras Installation
Introduction
Installing Keras on Ubuntu 16.04
Getting ready
How to do it...
Installing miniconda
Installing numpy and scipy
Installing mkl
Installing TensorFlow
Installing Keras
Using the Theano backend with Keras
Installing Keras with Jupyter Notebook in a Docker image
Getting ready
How to do it...
Installing the Docker container 
Installing the Docker container with the host volume mapped
Installing Keras on Ubuntu 16.04 with GPU enabled
Getting ready
How to do it...
Installing cuda
Installing cudnn
Installing NVIDIA CUDA profiler tools interface development files
Installing the TensorFlow GPU version
Installing Keras
Working with Keras Datasets and Models
Introduction
CIFAR-10 dataset
How to do it...
CIFAR-100 dataset
How to do it...
Specifying the label mode
MNIST dataset
How to do it...
Load data from a CSV file
How to do it...
Models in Keras – getting started
Anatomy of a model
Types of models
Sequential models
How to do it...
Create a Sequential model
Compile the model
Train the model 
Evaluate the model
Predict using the model
Putting it all together
Model inspection internals
Model compilation internals
Initialize the loss
Model training
Output of the sample 
Shared layer models
Introduction – shared input layer
How to do it...
Concatenate function
Keras functional APIs
How to do it...
The output of the example
Keras functional APIs – linking the layers
How to do it...
Model class
Image classification using Keras functional APIs
How to do it...
Data Preprocessing, Optimization, and Visualization
Feature standardization of image data
Getting ready
How to do it...
Initializing ImageDataGenerator
Sequence padding
Getting ready
How to do it...
Pre-padding with default 0.0 padding
Post-padding
Padding with truncation
Padding with a non-default value
Model visualization
Getting ready
How to do it...
Code listing
Optimization 
Common code for samples
Optimization with stochastic gradient descent
Getting ready
How to do it...
Optimization with Adam
Getting ready
How to do it...
Optimization with AdaDelta
Getting ready
How to do it...
Adadelta optimizer
Optimization with RMSProp
Getting ready
How to do it...
Classification Using Different Keras Layers
Introduction
Classification for breast cancer
How to do it...
Data processing
Modeling
Full code listing 
Classification for spam detection
How to do it...
Data processing
Modeling
Full code listing
Implementing Convolutional Neural Networks
Introduction
Cervical cancer classification
Getting ready
How to do it…
Data processing
Modeling
Predictions
Digit recognition
Getting ready
How to do it…
Modeling
Generative Adversarial Networks
Introduction
GAN overview
Basic GAN
Getting ready
How to do it...
Building a generator 
Building a discriminator
Initialize the GAN instance
Training the GAN 
Output plots
Average metrics of the GAN
Boundary seeking GAN
Getting ready
How to do it...
Generator
Discriminator
Initializing the BGAN class
Boundary seeking loss
Train the BGAN
Output the plots
Iteration 0
Iteration 10000
Metrics of the BGAN model
Plotting the metrics 
DCGAN
Getting ready
How to do it...
Generator
Summary of the generator
Training the generator
Discriminator
Build the discriminator
Summary of the discriminator
Compile the discriminator
Combined model - generator and discriminator
Train the generator using feedback from a discriminator
Putting it all together
The output of the program
Average metrics of the model
Recurrent Neural Networks
Introduction
The need for RNNs
Simple RNNs for time series data
Getting ready
Loading the dataset
How to do it…
Instantiate a sequential model
LSTM networks for time series data
LSTM networks
LSTM memory example
Getting ready
How to do it...
Encoder
LSTM configuration and model
Train the model
Full code listing 
Time series forecasting with LSTM
Getting ready
 Load the dataset
How to do it…
Instantiate a sequential model
Observation
Sequence to sequence learning for the same length output with LSTM
Getting ready
How to do it…
Training data 
Model creation
Model fit and prediction
Natural Language Processing Using Keras Models
Introduction
Word embedding
Getting ready
How to do it...
Without embeddings
With embeddings
Sentiment analysis
Getting ready
How to do it…
Full code listing 
Text Summarization Using Keras Models
Introduction
Text summarization for reviews
How to do it…
Data processing
Encoder-decoder architecture
Training
See also
Reinforcement Learning
Introduction
The CartPole game with Keras
How to do it...
Implementing the DQN agent
The memory and remember
The replay function
The act function
Hyperparameters for the DQN
DQN agent class
Training the agent
Dueling DQN to play Cartpole 
Getting ready
DQN agent
init method
Setting the last layer of the network
Dueling policy
Init code base
BoltzmannQPolicy
Adjustment during training
Sequential memory
How to do it...
Plotting the training and testing results
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Preface
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. This book covers installing and setting up Keras, while also demonstrating how you can perform deep learning with Keras in the TensorFlow, Apache MXNet, and CNTK backends.
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 this book is 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 this book covers
Chapter 1, Keras Installation, covers various installation and setup procedures, as well as defining various Keras configurations.
Chapter 2, Working with Keras Datasets and Models, covers using various datasets, such as CIFAR10, CIFAR100, or MNIST, and many other datasets and models used for image classification.
Chapter 3, Data Preprocessing, Optimization, and Visualization, covers various preprocessing and optimization techniques using Keras. The optimization techniques covered include TFOptimizer, AdaDelta, and many more.
Chapter 4, Classification Using Different Keras Layers, details various Keras layers, for example, recurrent layers, and convolutional layers.
Chapter 5, Implementing Convolutional Neural Networks, teaches you convolutional neural network algorithms in detail, using the example of cervical cancer classification and the digit recognition dataset.
Chapter 6, Generative Adversarial Networks, covers basic generative adversarial networks (GANs) and boundary-seeking GAN.
Chapter 7, Recurrent Neural Networks, covers the basics of recurrent neural networks in order to implement Keras based on historical datasets.
Chapter 8, Natural Language Processing Using Keras Models, covers the basics of NLP for word analysis and sentiment analysis using Keras.
Chapter 9, Text Summarization Using Keras Models, shows you how to use Keras models for text summarization when using the Amazon reviews dataset.
Chapter 10, Reinforcement Learning, focuses on formulating and developing reinforcement learning models using Keras.
To get the most out of this book
Readers should have some basic knowledge of Keras and deep learning.
Download the example code files
You can download the example code files for this book from your account at www.packt.com. If you purchased this book elsewhere, you can visit www.packt.com/support and register to have the files emailed directly to you.
You can download the code files by following these steps:
Log in or register at
www.packt.com
.
Select the
SUPPORT
tab.
Click on
Code Downloads & Errata
.
Enter the name of the book in the
Search
box and follow the onscreen instructions.
Once the file is downloaded, please make sure that you unzip or extract the folder using the latest version of:
WinRAR/7-Zip for Windows
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7-Zip/PeaZip for Linux
The code bundle for the book is also hosted on GitHub at https://github.com/PacktPublishing/Keras-Deep-Learning-Cookbook. In case there's an update to the code, it will be updated on the existing GitHub repository.
We also have other code bundles from our rich catalog of books and videos available at https://github.com/PacktPublishing/. Check them out!
Download the color images
We also provide a PDF file that has color images of the screenshots/diagrams used in this book. You can download it here: http://www.packtpub.com/sites/default/files/downloads/9781788621755_ColorImages.pdf.
Sections
In this book, you will find several headings that appear frequently (Getting ready, How to do it..., How it works..., There's more..., and See also).
To give clear instructions on how to complete a recipe, use these sections as follows:
Getting ready
This section tells you what to expect in the recipe and describes how to set up any software or any preliminary settings required for the recipe.
How to do it…
This section contains the steps required to follow the recipe.
How it works…
This section usually consists of a detailed explanation of what happened in the previous section.
There's more…
This section consists of additional information about the recipe in order to make you more knowledgeable about the recipe.
See also
This section provides helpful links to other useful information for the recipe.
Get in touch
Feedback from our readers is always welcome.
General feedback: If you have questions about any aspect of this book, mention the book title in the subject of your message and email us at [email protected].
Errata: Although we have taken every care to ensure the accuracy of our content, mistakes do happen. If you have found a mistake in this book, we would be grateful if you would report this to us. Please visit www.packt.com/submit-errata, selecting your book, clicking on the Errata Submission Form link, and entering the details.
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Keras Installation
In this chapter, we will cover the following recipes:
Installing Keras on Ubuntu 16.04
Installing Keras with Jupyter Notebook in a Docker image
Installing Keras on Ubuntu 16.04 with GPU enabled
Introduction
In this chapter, we look at how Keras can be installed on Ubuntu and CentOS. We will use Ubuntu 16.04, 64-bit (Canonical, Ubuntu, 16.04 LTS, and amd64 xenial image build on 2017-10-26) for the installation.
Installing Keras on Ubuntu 16.04
Before installing Keras, we have to install the Theano and TensorFlow packages and their dependencies. Since it is a fresh OS, make sure Python is installed. Let's look at the following section for Python installation.
Getting ready
First you need to make sure you have a blank Ubuntu 16.04 OS locally or remotely available in the cloud and with root access.
How to do it...
In the following sections, we take a at the installation of each component that needs to be done before we can go ahead with the installation of Keras.
Installing numpy and scipy
The numpy and scipy packages are prerequisites for Theano installation. The following versions are recommended:
NumPy >= 1.9.1 <= 1.12
SciPy >= 0.14 < 0.17.1: H
ighly recommended
for sparse matrix and support for special functions in Theano, SciPy >=0.8 would do the work
BLAS installation (with Level 3 functionality) the recommended: MKL, this is free through
conda
with the
mkl-service
package
Execute the following command to install
numpy
and
scipy
. (Make sure
conda
is in your
PATH
):
conda install
numpy
conda install scipy
The output of the scipy installation is shown as follows. Notice that it installs libgfortran as part of the scipy installation:
Fetching package metadata ...........
Solving package specifications: .
Package plan for installation in environment /home/ubuntu/miniconda2:
The following new packages will also be installed:
libgfortran-ng: 7.2.0-h9f7466a_2
scipy: 1.0.0-py27hf5f0f52_0
Proceed ([y]/n)?
libgfortran-ng 100% |#############################################################| Time: 0:00:00 36.60 MB/s
scipy-1.0.0-py 100% |#############################################################| Time: 0:00:00 66.62 MB/s
Installing mkl
mkl
is a m
ath library for Intel and compatible processors. It is a part of
numpy
, but we want to make sure it is installed before we install Theano and TensorFlow:
conda install mkl
The output of the installation is given as follows. In our case, miniconda2 has already installed the latest version of mkl:
Fetching package metadata ...........
Solving package specifications: .
# All requested packages already installed.
# packages in environment at /home/ubuntu/miniconda2:
#
mkl 2018.0.1 h19d6760_4
Once all the prerequisites are installed, let's install TensorFlow.
Installing Keras
Next, we will install Keras using
conda
from
conda-forge
Execute the following command on the Terminal:
conda install -c conda-forge keras
The following listed output will confirm that Keras is installed:
Fetching package metadata .............
Solving package specifications: .
Package plan for installation in environment /home/ubuntu/miniconda2:
The following new packages will also be installed:
h5py: 2.7.1-py27_2 conda-forge
hdf5: 1.10.1-1 conda-forge
keras: 2.0.9-py27_0 conda-forge
libgfortran: 3.0.0-1
pyyaml: 3.12-py27_1 conda-forge
Proceed ([y]/n)? y
libgfortran-3. 100% |#############################################################| Time: 0:00:00 35.16 MB/s
hdf5-1.10.1-1. 100% |#############################################################| Time: 0:00:00 34.26 MB/s
pyyaml-3.12-py 100% |#############################################################| Time: 0:00:00 60.08 MB/s
h5py-2.7.1-py2 100% |#############################################################| Time: 0:00:00 58.54 MB/s
keras-2.0.9-py 100% |#############################################################| Time: 0:00:00 45.92 MB/s
Let's verify the Keras installation with the following code:
$ python
Python 2.7.14 |Anaconda, Inc.| (default, Oct 16 2017, 17:29:19)
Execute the following command to verify that Keras has been installed:
> from keras.models import Sequential
Using TensorFlow backend.
>>>
Notice that Keras is using the TensorFlow backend.
Using the Theano backend with Keras
Let's modify the default configuration and change TensorFlow to Theano as the backend of Keras. Modify the
keras.json
file:
vi .keras/keras.json
The default file has the following content:
{ "
image_data_format
":
"channels_last"
, "
epsilon
":
1e-07
, "
floatx
":
"float32"
, "
backend
":
"tensorflow"
}
The modified file will look like the following file. The
"backend"
value has been changed to
"theano"
:
{ "
image_data_format
":
"channels_last"
, "
epsilon
":
1e-07
, "
floatx
":
"float32"
, "
backend
":
"theano"
}
Run the Python console and import
Sequential
from
keras.model
u
sing the Theano backend:
$ python
Python 2.7.14 |Anaconda, Inc.| (default, Oct 16 2017, 17:29:19)
[GCC 7.2.0] on linux2
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