Froog

                Authors:  Roodschild Matías     / mroodschild@gmail.com
                          Jorge Gotay Sardiñas  / jgotay57@gmail.com
                          Adrián Will           / adrian.will.01@gmail.com
                          Sebastián Rodriguez   / sebastian.rodriguez@gitia.org

Introduction

This project was created with mainly academic purposes for my PhD Tesis, but it is also free for commercial uses. Its design goals are: 1) To be accessible to both novices and experts, and 2) To facilitate neural networks manipulations. Froog is free, written 100% in Java, and has been released under Apache 2 license.

Currently Froog supports:

• Backpropagation Algorithm
• Stochastic Gradient Descent
• Conjugate Gradient
• Scaled Conjugate Gradient
• Acceleration methods (Momentum, Momentum Rumelhart, Adam)
• Weight Initialization (Default (Xavier), He, Pitfall, PositiveRandom, SmallRandom)
• Weight Normalization L2
• Dropout
• Loss Functions (RMSE, MSE, CrossEntropy, Logistic)
• Transfer Functions (Logsig, Tansig, Softmax, Purelim, Softplus, ReLU)
• Confusion Matrix
• Early Stop (Max Iteration Only)

Documentation - Example

        //get data
        SimpleMatrix input = CSV.open("src/main/resources/iris/iris-in.csv");
        SimpleMatrix output = CSV.open("src/main/resources/iris/iris-out.csv");

        //Standard Desviation
        STD std = new STD();
        std.fit(input);

        //normalization
        input = std.eval(input);
        Random random = new Random(1);
        
        //set data in horizontal format (a column is a register and a row is a feature)
        input = input.transpose();
        output = output.transpose();

        //setting backpropagation
        Backpropagation bp = new Backpropagation();
        bp.setEpoch(1000);
        bp.setMomentum(0.9);
        bp.setClassification(true);
        bp.setLossFunction(LossFunction.CROSSENTROPY);

        //number of neurons
        int Nhl = 2;

        Feedforward net = new Feedforward();

        //add layers to neural network
        net.addLayer(new Dense(input.numRows(), Nhl, TransferFunction.TANSIG, random));
        net.addLayer(new Dense(Nhl, output.numRows(), TransferFunction.SOFTMAX, random));
        
        //train your net
        bp.train(net, input, output);
        
        //show results
        System.out.println("Print all output");
        SimpleMatrix salida = net.output(input);
        ConfusionMatrix confusionMatrix = new ConfusionMatrix();
        confusionMatrix.eval(Compite.eval(salida.transpose()), output.transpose());
        confusionMatrix.printStats();
        

Maven - jitpack.io

Froog is in Maven jitpack.io and can easily be added to Maven, and similar project managers.

    <repositories>
        <repository>
            <id>jitpack.io</id>
            <url>https://jitpack.io</url>
        </repository>
    </repositories> 
    <dependencies>
        <dependency>
            <groupId>com.github.mroodschild</groupId>
            <artifactId>froog</artifactId>
            <version>0.5.1</version>
        </dependency>
    </dependencies>

Dependencies

---

The main Froog modules depends on the following libraries

• [ EJML 0.41 ] ( http://ejml.org )
• [ Apache Commons-lang3 ] ( https://commons.apache.org/proper/commons-lang/ )

The following is required for unit tests

• [ JUnit ] ( http://junit.sourceforge.net/ )

License

Froog is released under the Apache 2 open source license.