Book Image

Java Deep Learning Cookbook

By : Rahul Raj
Book Image

Java Deep Learning Cookbook

By: Rahul Raj

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)

Training with MNIST images

Once the layers are constructed and the neural network is formed, we can initiate the training session. During the training session, we reconstruct the input multiple times and evaluate the reconstruction error. In previous recipes, we completed the autoencoder network configuration by defining the input and output layers as required. Note that we are going to train the network with its own input features, not the labels. Since we use an autoencoder for anomaly detection, we encode the data and then decode it back to measure the reconstruction error. Based on that, we list the most probable anomalies in MNIST data.

How to do it...

  1. Choose the correct training approach. Here is what is expected to...