Keras Deep Learning Cookbook

Keras Deep Learning Cookbook

By : Rajdeep Dua, Sujit Pal, Manpreet Singh Ghotra

Buy this Book

Keras Deep Learning Cookbook

By: Rajdeep Dua, Sujit Pal, Manpreet Singh Ghotra

Buy this Book

Overview of this book

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

Title Page

Packt Upsell

Contributors

Preface

Free Chapter

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

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

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

Introduction

Classification for breast cancer

Classification for spam detection

Implementing Convolutional Neural Networks

Introduction

Cervical cancer classification

Digit recognition

Generative Adversarial Networks

Introduction

Basic GAN

Boundary seeking GAN

DCGAN

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

Introduction

Word embedding

Sentiment analysis

Text Summarization Using Keras Models

Introduction

Text summarization for reviews

Reinforcement Learning

Introduction

The CartPole game with Keras

Dueling DQN to play Cartpole

Other Books You May Enjoy

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Installing Keras with Jupyter Notebook in a Docker image

In this recipe, we learn how to install and use a Docker container running Keras inside a container and access it using Jupyter.

Getting ready

Install the latest version of the Docker CLI from https://docs.docker.com/engine/installation/.

How to do it...

In the following section, we will be learning how to install the Docker container.

Installing the Docker container

Execute the following command on the Terminal to run the container. The container image is available with the tag rajdeepd/jupyter-keras:

docker run -d -p 8888:8888 rajdeepd/jupyter-keras start-notebook.sh --NotebookApp.token=''

This will install the Notebook locally and start it as well. You can execute the docker ps -a command and see the output in the Terminal, as follows:

CONTAINER IDIMAGE COMMANDCREATED STATUS PORTS NAMES
45998a5eea89rajdeepd/jupyter-keras"tini -- start-not..." About an hour ago Up About an hour 0.0.0.0:8888->8888/tcpadmiring_wing

Please note that the host port of 8888 is mapped to the container port of 8888.

Open the browser at the following URL http://localhost:8888:

You will notice that Jupyter is running. You can create a new Notebook and run Keras-specific code.

Installing the Docker container with the host volume mapped

In this section, we look at how to map the local volume $(pwd)/keras-samples to the work directory in the container.

Execute the note -v flag command, which does the volume mapping:

docker run -d -v /$(pwd)/keras-samples:/home/jovyan/work \
 -p 8888:8888 rajdeepd/jupyter-keras start-notebook.sh --NotebookApp.token=''

If you go to the URL, you will notice the sample page being displayed.

If you got /$(pwd)/keras-samples, you will notice that the Notebooks are available in the host directory, and they also can be seen being loaded by Jupyter:

rdua1-ltm:keras-samples rdua$ pwd
 /Users/rdua/personal/keras-samples
 rdua1-ltm:keras-samples rdua$ ls
 MNIST CNN.ipynb sample_one.ipynb

If you open MNIST CNN.ipynb, it is a Keras CNN sample, which we will learn more about in the subsequent chapters.

In this recipe, we used the Docker image rajdeepd/jupyter-keras to create a Keras environment and access it from Jupyter running in the host environment.

Keras Deep Learning Cookbook

By : Rajdeep Dua, Sujit Pal, Manpreet Singh Ghotra

Keras Deep Learning Cookbook

By: Rajdeep Dua, Sujit Pal, Manpreet Singh Ghotra

Overview of this book

Related Content you might be interested in

Current Title:

Keras Deep Learning Cookbook

Deep Learning with Keras

Deep Learning Quick Reference

Generative Adversarial Networks Cookbook

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