In recent years, deep learning has emerged as a powerful technique for solving complex problems in various domains such as computer vision, natural language processing, and speech recognition. While Python is the de facto language for deep learning, Java developers can also leverage the Nashorn JavaScript engine to build and deploy deep learning models. In this blog post, we will explore how to implement deep learning models with Nashorn.

What is Nashorn?

Nashorn is a JavaScript engine that comes bundled with Java 8 and later versions. It provides a way to execute JavaScript code within a Java application. With Nashorn, you can call Java code from JavaScript and vice versa, enabling seamless integration between the two languages.

Using Nashorn for Deep Learning

To begin using Nashorn for deep learning, we need to use a deep learning library that is compatible with JavaScript, such as Deeplearning4j or TensorFlow.js. In this example, we will demonstrate how to use Deeplearning4j with Nashorn.

First, we need to include the necessary dependencies in our Java project. We can add the following Maven dependencies to our pom.xml file:

<dependencies>
    <dependency>
        <groupId>org.nd4j</groupId>
        <artifactId>nd4j-native-platform</artifactId>
        <version>1.0.0-beta7</version>
    </dependency>
    <dependency>
        <groupId>org.deeplearning4j</groupId>
        <artifactId>deeplearning4j-core</artifactId>
        <version>1.0.0-beta7</version>
    </dependency>
    <!-- Add more dependencies as needed -->
</dependencies>

Next, we can write our JavaScript code that uses the Deeplearning4j library. For example, let’s say we want to train a simple neural network to classify images of cats and dogs. Here’s how our JavaScript code might look like:

// Import necessary classes from Deeplearning4j
var DataSet = Java.type('org.nd4j.linalg.dataset.api.iterator.DataSetIterator');
var ImageLoader = Java.type('org.nd4j.linalg.dataset.api.preprocessor.ImageLoader');
var NativeImageLoader = Java.type('org.nd4j.linalg.dataset.api.preprocessor.loader.NativeImageLoader');
var RecordReaderDataSetIterator = Java.type('org.deeplearning4j.datasets.iterator.impl.RecordReaderDataSetIterator');
var ScalingType = Java.type('org.nd4j.linalg.dataset.api.preprocessor.ImagePreProcessingScaler.ScalingType');
var ImagePreProcessingScaler = Java.type('org.nd4j.linalg.dataset.api.preprocessor.ImagePreProcessingScaler');

// Load and preprocess the training dataset
var loader = new NativeImageLoader(28, 28, 1);
var trainData = loader.asMatrix('./train_images');
var trainLabels = loader.asMatrix('./train_labels');

// Create a dataset iterator
var dataSetIterator = new RecordReaderDataSetIterator.Builder(trainData, trainLabels)
    .preProcessor(new ImagePreProcessingScaler(0, 1, ScalingType.CENTER))
    .build();

// Define the neural network architecture
var numClasses = 2;
var model = new org.deeplearning4j.nn.conf.NeuralNetConfiguration.Builder()
    .list()
    .layer(0, new org.deeplearning4j.nn.conf.layers.DenseLayer.Builder()
        .nIn(28 * 28)
        .nOut(100)
        .activation(org.nd4j.linalg.activations.Activation.RELU)
        .build())
    .layer(1, new org.deeplearning4j.nn.conf.layers.OutputLayer.Builder()
        .nIn(100)
        .nOut(numClasses)
        .activation(org.nd4j.linalg.activations.Activation.SOFTMAX)
        .lossFunction(org.nd4j.linalg.lossfunctions.LossFunctions.LossFunction.NEGATIVELOGLIKELIHOOD)
        .build())
    .build();

// Train the model
var trainer = new org.deeplearning4j.nn.api.TrainingMaster.Builder(32)
    .epochs(10)
    .build();

model.init();
model.fit(dataSetIterator, trainer);

This JavaScript code makes use of the classes and functions provided by Deeplearning4j to load, preprocess, define the neural network architecture, and train the model. We can run this code using Nashorn within our Java application to train our deep learning model.

Conclusion

In this blog post, we have seen how Nashorn, the JavaScript engine for Java, can be used for implementing deep learning models. By leveraging compatible deep learning libraries like Deeplearning4j, Java developers can harness the power of deep learning in their applications. With Nashorn, the barrier between Java and JavaScript is reduced, opening up new possibilities for building intelligent applications with deep learning capabilities.

#deeplearning #nashorn