manumafe98/edux

EDUX is a developer friendly Java library for machine learning educational tasks

EDUX - Java Machine Learning Library

EDUX is a user-friendly library for solving problems with a machine learning approach.

Features

EDUX supports a variety of machine learning algorithms including:

• Multilayer Perceptron (Neural Network): Suitable for regression and classification problems, MLPs can approximate non-linear functions.
• K Nearest Neighbors: A simple, instance-based learning algorithm used for classification and regression.
• Decision Tree: Offers visual and explicitly laid out decision making based on input features.
• Support Vector Machine: Effective for binary classification, and can be adapted for multi-class problems.
• RandomForest: An ensemble method providing high accuracy through building multiple decision trees.

Augmentations

Edux supports a variety of image augmentations, which can be used to increase the performance of your model.

Few examples:

Color Equalization

Monochrome + Noise

Code Example

Single Image

    AugmentationSequence augmentationSequence=
        new AugmentationBuilder()
        .addAugmentation(new ResizeAugmentation(250,250))
        .addAugmentation(new ColorEqualizationAugmentation())
        .build();

        BufferedImage augmentedImage=augmentationSequence.applyTo(image);

Run for all images in a directory

    AugmentationSequence augmentationSequence=
        new AugmentationBuilder()
        .addAugmentation(new ResizeAugmentation(250,250))
        .addAugmentation(new ColorEqualizationAugmentation())
        .addAugmentation(new BlurAugmentation(25))
        .addAugmentation(new RandomDeleteAugmentation(10,20,20))
        .build()
        .run(trainImagesDir,numberOfWorkers,outputDir);

Battle Royale - Which algorithm is the best?

We run all algorithms on the same dataset and compare the results. Benchmark

Goal

The main goal of this project is to create a user-friendly library for solving problems using a machine learning approach. The library is designed to be easy to use, enabling the solution of problems with just a few lines of code.

Features

The library currently supports:

• Multilayer Perceptron (Neural Network)
• K Nearest Neighbors
• Decision Tree
• Support Vector Machine
• RandomForest

Get started

Include the library as a dependency in your Java project file.

Gradle

 implementation 'io.github.samyssmile:edux:1.0.7'

Maven

  <dependency>
     <groupId>io.github.samyssmile</groupId>
     <artifactId>edux</artifactId>
     <version>1.0.7</version>
 </dependency>

Hardware Acceleration (preview feature)

EDUX supports Nvidia GPU acceleration.

Requirements

• Nvidia GPU with CUDA support
• CUDA Toolkit 11.8

Getting started tutorial

This section guides you through using EDUX to process your dataset, configure a multilayer perceptron (Multilayer Neural Network), perform training and evaluation.

A multi-layer perceptron (MLP) is a feedforward artificial neural network that generates a set of outputs from a set of input features. An MLP is characterized by several layers of input nodes connected as a directed graph between the input and output layers.

Step 0: Get Familiar with the Dataset

In this example we use the famouse MNIST Dataset. The MNIST database contains 60,000 training images and 10,000 testing

Step 1: Data Processing

    String trainImages = "train-images.idx3-ubyte";
    String trainLabels = "train-labels.idx1-ubyte";
    String testImages = "t10k-images.idx3-ubyte";
    String testLabels = "t10k-labels.idx1-ubyte";
    Loader trainLoader = new ImageLoader(trainImages, trainLabels, batchSize);
    Loader testLoader = new ImageLoader(testImages, testLabels, batchSize);

Step 2: Configure the MultilayerPerceptron

    int batchSize = 100;
    int threads = 1;
    int epochs = 10;
    float initialLearningRate = 0.1f;
    float finalLearningRate = 0.001f;

    MetaData trainMetaData = trainLoader.open();
    int inputSize = trainMetaData.getInputSize();
    int outputSize = trainMetaData.getExpectedSize();
    trainLoader.close();

Step 3: Build the Network

We use the NetworkBuilder Class

    new NetworkBuilder()
        .addLayer(new DenseLayer(inputSize, 32))  //32 Neurons as output size
        .addLayer(new ReLuLayer())
        .addLayer(new DenseLayer(32, outputSize)) //32 Neurons as input size
        .addLayer(new SoftmaxLayer())
        .withBatchSize(batchSize)
        .withLearningRates(initialLearningRate, finalLearningRate)
        .withExecutionMode(singleThread)
        .withEpochs(epochs)
        .build()
        .printArchitecture()
        .fit(trainLoader, testLoader)
        .saveModel("model.edux"); // Save the trained model

Step 4: Load the model and continue training

Load 'model.edux' and continue training for 10 epochs.

    NeuralNetwork nn =
        new NetworkBuilder().withEpochs(10).loadModel("model.edux").fit(trainLoader, testLoader);

Results

........................Epoch: 1, Loss: 1,14, Accuracy: 91,04
...
........................Epoch: 10, Loss: 0,13, Accuracy: 96,16

Working examples

You can find more fully working examples for all algorithms in the examples folder.

For examples we use the

• IRIS dataset.
• SEABORNE PENGUINS dataset.

Contributions

Contributions are warmly welcomed! If you find a bug, please create an issue with a detailed description of the problem. If you wish to suggest an improvement or fix a bug, please make a pull request. Also checkout the Rules and Guidelines page for more information.