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Java Deep Learning Cookbook
Java Deep Learning Cookbook

Java Deep Learning Cookbook: Train neural networks for classification, NLP, and reinforcement learning using Deeplearning4j

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Java Deep Learning Cookbook

Data Extraction, Transformation, and Loading

Let's discuss the most important part of any machine learning puzzle: data preprocessing and normalization. Garbage in, garbage out would be the most appropriate statement for this situation. The more noise we let pass through, the more undesirable outputs we will receive. Therefore, you need to remove noise and keep signals at the same time.

Another challenge is handling various types of data. We need to convert raw datasets into a suitable format that a neural network can understand and perform scientific computations on. We need to convert data into a numeric vector so that it is understandable to the network and so that computations can be applied with ease. Remember that neural networks are constrained to only one type of data: vectors.

There has to be an approach regarding how data is loaded into a neural network. We cannot...

Technical requirements

Concrete implementations of the use cases that will be discussed in this chapter can be found at https://github.com/PacktPublishing/Java-Deep-Learning-Cookbook/tree/master/02_Data_Extraction_Transform_and_Loading/sourceCode/cookbook-app/src/main/java/com/javadeeplearningcookbook/app.

After cloning our GitHub repository, navigate to the
Java-Deep-Learning-Cookbook/02_Data_Extraction_Transform_and_Loading/sourceCode directory. Then, import the cookbook-app project as a Maven project by importing the pom.xml file inside the cookbook-app directory.

The datasets that are required for this chapter are located in the Chapter02 root directory (Java-Deep-Learning-Cookbook/02_Data_Extraction_Transform_and_Loading/). You may keep it in a different location, for example, your local directory, and then refer to it in the source code accordingly.

...

Reading and iterating through data

ETL is an important stage in neural network training since it involves data. Data extraction, transformation, and loading needs to be addressed before we proceed with neural network design. Bad data is a much worse situation than a less efficient neural network. We need to have a basic understanding of the following aspects as well:

  • The type of data you are trying to process
  • File-handling strategies

In this recipe, we will demonstrate how to read and iterate data using DataVec.

Getting ready

As a prerequisite, make sure that the required Maven dependencies have been added for DataVec in your pom.xml file, as we mentioned in previous chapter, Configuring Maven for DL4J recipe.

The following...

Performing schema transformations

Data transformation is an important data normalization process. It's a possibility that bad data occurs, such as duplicates, missing values, non-numeric features, and so on. We need to normalize them by applying schema transformation so that data can be processed in a neural network. A neural network can only process numeric features. In this recipe, we will demonstrate the schema creation process.

How to do it...

  1. Identify the outliers in the data: For a small dataset with just a few features, we can spot outliers/noise via manual inspection. For a dataset with a large number of features, we can perform Principal Component Analysis (PCA), as shown in the following code:
INDArray factor...

Building a transformation process

The next step after schema creation is to define a data transformation process by adding all the required transformations. We can manage an ordered list of transformations using TransformProcess. During the schema creation process, we only defined a structure for the data with all its existing features and didn't really perform transformation. Let's look at how we can transform the features in the datasets from a non-numeric format into a numeric format. Neural networks cannot understand raw data unless it is mapped to numeric vectors. In this recipe, we will build a transformation process from the given schema.

How to do it...

  1. Add a list of transformations to TransformProcess....

Serializing transforms

DataVec gives us the ability to serialize the transforms so that they're portable for production environments. In this recipe, we will serialize the transformation process.

How to do it...

  1. Serialize the transforms into a human-readable format. We can transform to JSON using TransformProcess as follows:
String serializedTransformString = transformProcess.toJson()

We can transform to YAML using TransformProcess as follows:

String serializedTransformString = transformProcess.toYaml()

Executing a transform process

After the transformation process has been defined, we can execute it in a controlled pipeline. It can be executed using batch processing, or we can distribute the effort to a Spark cluster. Previously, we look at TransformProcessRecordReader, which automatically does the transformation in the background. We cannot feed and execute the data if the dataset is huge. Effort can be distributed to a Spark cluster for a larger dataset. You can also perform regular local execution. In this recipe, we will discuss how to execute a transform process locally as well as remotely.

How to do it...

  1. Load the dataset into RecordReader. Load the CSV data in the case of CSVRecordReader:
RecordReader reader = new...

Normalizing data for network efficiency

Normalization makes a neural network's job much easier. It helps the neural network treat all the features the same, irrespective of their range of values. The main goal of normalization is to arrange the numeric values in a dataset on a common scale without actually disturbing the difference in the range of values. Not all datasets require a normalization strategy, but if they do have different numeric ranges, then it is a crucial step to perform normalization on the data. Normalization has a direct impact on the stability/accuracy of the model. ND4J has various preprocessors to handle normalization. In this recipe, we will normalize the data.

How to do it...

  1. Create a dataset...
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Key benefits

  • Install and configure Deeplearning4j to implement deep learning models from scratch
  • Explore recipes for developing, training, and fine-tuning your neural network models in Java
  • Model neural networks using datasets containing images, text, and time-series data

Description

Java is one of the most widely used programming languages in the world. With this book, you will see how to perform deep learning using Deeplearning4j (DL4J) – the most popular Java library for training neural networks efficiently. This book starts by showing you how to install and configure Java and DL4J on your system. You will then gain insights into deep learning basics and use your knowledge to create a deep neural network for binary classification from scratch. As you progress, you will discover how to build a convolutional neural network (CNN) in DL4J, and understand how to construct numeric vectors from text. This deep learning book will also guide you through performing anomaly detection on unsupervised data and help you set up neural networks in distributed systems effectively. In addition to this, you will learn how to import models from Keras and change the configuration in a pre-trained DL4J model. Finally, you will explore benchmarking in DL4J and optimize neural networks for optimal results. By the end of this book, you will have a clear understanding of how you can use DL4J to build robust deep learning applications in Java.

Who is this book for?

If you are a data scientist, machine learning developer, or a deep learning enthusiast who wants to implement deep learning models in Java, this book is for you. Basic understanding of Java programming as well as some experience with machine learning and neural networks is required to get the most out of this book.

What you will learn

  • Perform data normalization and wrangling using DL4J
  • Build deep neural networks using DL4J
  • Implement CNNs to solve image classification problems
  • Train autoencoders to solve anomaly detection problems using DL4J
  • Perform benchmarking and optimization to improve your model s performance
  • Implement reinforcement learning for real-world use cases using RL4J
  • Leverage the capabilities of DL4J in distributed systems

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Publication date : Nov 08, 2019
Length: 304 pages
Edition : 1st
Language : English
ISBN-13 : 9781788999472
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Product Details

Publication date : Nov 08, 2019
Length: 304 pages
Edition : 1st
Language : English
ISBN-13 : 9781788999472
Vendor :
Oracle
Category :
Languages :
Concepts :

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Table of Contents

13 Chapters
Introduction to Deep Learning in Java Chevron down icon Chevron up icon
Data Extraction, Transformation, and Loading Chevron down icon Chevron up icon
Building Deep Neural Networks for Binary Classification Chevron down icon Chevron up icon
Building Convolutional Neural Networks Chevron down icon Chevron up icon
Implementing Natural Language Processing Chevron down icon Chevron up icon
Constructing an LSTM Network for Time Series Chevron down icon Chevron up icon
Constructing an LSTM Neural Network for Sequence Classification Chevron down icon Chevron up icon
Performing Anomaly Detection on Unsupervised Data Chevron down icon Chevron up icon
Using RL4J for Reinforcement Learning Chevron down icon Chevron up icon
Developing Applications in a Distributed Environment Chevron down icon Chevron up icon
Applying Transfer Learning to Network Models Chevron down icon Chevron up icon
Benchmarking and Neural Network Optimization Chevron down icon Chevron up icon
Other Books You May Enjoy Chevron down icon Chevron up icon

Customer reviews

Rating distribution
Full star icon Full star icon Full star icon Full star icon Half star icon 4.5
(2 Ratings)
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1 star 0%
Maxwell Dec 16, 2019
Full star icon Full star icon Full star icon Full star icon Full star icon 5
This is an excellently put together book on deep learning. The author puts you on a path to truly understanding how to use java to build learning applications. Granted, you will need the skills to back it up, but if you're like me and just needed a bit of help to get you started -- then this is book is an excellent choice.I liked how well this book flowed as well. Seeing a huge wall of text can be be off-putting when trying to learn something complex like this. I can confidently say I came away with more knowledge than I had when I started. So for me, it was worth the purchase.
Amazon Verified review Amazon
Cliente Amazon Mar 23, 2020
Full star icon Full star icon Full star icon Full star icon Empty star icon 4
Thee book is awesome if you don't have knowledge about DL4J. It explains quite well how the basic of algorithms for Machine Learning work. I found really good examples, but incompletes in some way. My humble opinion: it's a good choice if you don't have prior knowledge in DL4J.
Amazon Verified review Amazon
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