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

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Installing Keras on Ubuntu 16.04 with GPU enabled

In this recipe, we will install Keras on Ubuntu 16.04 with NVIDIA GPU enabled.

Getting ready

We are going to launch a GPU-enabled AWS EC2 instance and prepare it for the installed TensorFlow with the GPU and Keras. Launch the following AMI: Ubuntu Server 16.04 LTS (HVM), SSD Volume Type - ami-aa2ea6d0:

This is an AMI with Ubuntu 16.04 64 bit pre-installed, and it has the SSD volume type.

Choose the appropriate instance type: g3.4xlarge:

Once the VM is launched, assign the appropriate key that you will use to SSH into it. In our case, we used a pre-existing key:

SSH into the instance:

ssh -i aws/rd_app.pem [email protected]

How to do it...

Run the following commands to update and upgrade the OS:

sudo apt-get update
sudo apt-get upgrade

Install the gcc compiler and make the tool:

sudo apt install gcc
sudo apt install make

Installing cuda

Execute the following command to execute cuda:

sudo apt-get install -y cuda

Check that cuda is installed and run a basic program:

ls /usr/local/cuda-8.0
bin extras lib64 libnvvp nvml README share targets version.txt
doc include libnsight LICENSE nvvm samples src tools

Let's run one of the cuda samples after compiling it locally:

export PATH=/usr/local/cuda-8.0/bin${PATH:+:${PATH}}
export LD_LIBRARY_PATH=/usr/local/cuda-8.0/lib64\${LD_LIBRARY_PATH:+:${LD_LIBRARY_PATH}}
cd /usr/local/cuda-8.0/samples/5_Simulations/nbody

Compile the sample and run it as follows:

sudo make

./nbody

You will see output similar to the following listing:

Run "nbody -benchmark [-numbodies=<numBodies>]" to measure performance.
 -fullscreen (run n-body simulation in fullscreen mode)
 -fp64 (use double precision floating point values for simulation)
 -hostmem (stores simulation data in host memory)
 -benchmark (run benchmark to measure performance)
 -numbodies=<N> (number of bodies (>= 1) to run in simulation)
 -device=<d> (where d=0,1,2.... for the CUDA device to use)
 -numdevices=<i> (where i=(number of CUDA devices > 0) to use for simulation)
 -compare (compares simulation results running once on the default GPU and once on the CPU)
 -cpu (run n-body simulation on the CPU)
 -tipsy=<file.bin> (load a tipsy model file for simulation)

Next we install cudnn, which is a deep learning library from NVIDIA. You can find more information at https://developer.nvidia.com/cudnn.

Installing cudnn

Download cudnn from the NVIDIA site (https://developer.nvidia.com/rdp/assets/cudnn-8.0-linux-x64-v5.0-ga-tgz) and decompress the binary:

Note

Please note, you will need an NVIDIA developer account.

tar xvf cudnn-8.0-linux-x64-v5.1.tgz

We obtain the following output after decompressing the .tgz file:

cuda/include/cudnn.h
cuda/lib64/libcudnn.so
cuda/lib64/libcudnn.so.5
cuda/lib64/libcudnn.so.5.1.10
cuda/lib64/libcudnn_static.a

Copy these files to the /usr/local folder, as follows:

sudo cp cuda/include/cudnn.h /usr/local/cuda/include
sudo cp cuda/lib64/libcudnn* /usr/local/cuda/lib64

sudo chmod a+r /usr/local/cuda/include/cudnn.h /usr/local/cuda/lib64/libcudnn*

Installing NVIDIA CUDA profiler tools interface development files

Install the NVIDIA CUDA profiler tools interface development files that are needed for TensorFlow GPU installation with the following code:

sudo apt-get install libcupti-dev

Installing the TensorFlow GPU version

Execute the following command to install the TensorFlow GPU version:

sudo pip install tensorflow-gpu

Installing Keras

For Keras, use the sample command, as used for the installation with GPUs:

sudo pip install keras

In this recipe, we learned how to install Keras on top of the TensorFlow GPU hooked to cuDNN and CUDA.

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 on Ubuntu 16.04 with GPU enabled

Getting ready

How to do it...

Installing cuda

Installing cudnn

Note

Installing NVIDIA CUDA profiler tools interface development files

Installing the TensorFlow GPU version

Installing Keras