We need to calculate the reconstruction error for all the feature sets. Based on that, we will find the outlier data for all the MNIST digits (0 to 9). Finally, we will display the outlier data in the JFrame window. We also need feature values from a test set for the evaluation. We also need label values from the test set, not for evaluation, but for mapping anomalies with labels. Then, we can plot outlier data against each label. The labels are only used for plotting outlier data in JFrame against respective labels. In this recipe, we evaluate the trained autoencoder model for MNIST anomaly detection, and then sort the results and display them.
Java Deep Learning Cookbook
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Java Deep Learning Cookbook
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Overview of this book
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.
Table of Contents (14 chapters)
Preface
Free Chapter
Introduction to Deep Learning in Java
Data Extraction, Transformation, and Loading
Building Deep Neural Networks for Binary Classification
Building Convolutional Neural Networks
Implementing Natural Language Processing
Constructing an LSTM Network for Time Series
Constructing an LSTM Neural Network for Sequence Classification
Performing Anomaly Detection on Unsupervised Data
Using RL4J for Reinforcement Learning
Developing Applications in a Distributed Environment
Applying Transfer Learning to Network Models
Benchmarking and Neural Network Optimization
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