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Java example source code file (DenseTest.java)

This example Java source code file (DenseTest.java) is included in the alvinalexander.com "Java Source Code Warehouse" project. The intent of this project is to help you "Learn Java by Example" TM.

Learn more about this Java project at its project page.

Java - Java tags/keywords

dataset, datasetiterator, denselayer, densetest, evaluation, indarray, irisdatasetiterator, multilayerconfiguration, multilayernetwork, neuralnetconfiguration, test, util

The DenseTest.java Java example source code

package org.deeplearning4j.nn.layers.feedforward.dense;

import org.nd4j.linalg.dataset.api.iterator.DataSetIterator;
import org.deeplearning4j.datasets.iterator.impl.IrisDataSetIterator;
import org.deeplearning4j.eval.Evaluation;
import org.deeplearning4j.nn.api.*;
import org.deeplearning4j.nn.api.Layer;
import org.deeplearning4j.nn.conf.MultiLayerConfiguration;
import org.deeplearning4j.nn.conf.NeuralNetConfiguration;
import org.deeplearning4j.nn.conf.layers.*;
import org.deeplearning4j.nn.conf.layers.DenseLayer;
import org.deeplearning4j.nn.layers.factory.LayerFactories;
import org.deeplearning4j.nn.multilayer.MultiLayerNetwork;
import org.deeplearning4j.nn.weights.WeightInit;
import org.deeplearning4j.optimize.api.IterationListener;
import org.deeplearning4j.optimize.listeners.ScoreIterationListener;
import org.junit.Test;
import org.nd4j.linalg.api.ndarray.INDArray;
import org.nd4j.linalg.dataset.DataSet;
import org.nd4j.linalg.factory.Nd4j;
import org.nd4j.linalg.lossfunctions.LossFunctions;

import java.util.Arrays;

import static org.junit.Assert.assertEquals;

/**
 * Created by nyghtowl on 8/31/15.
 */
public class DenseTest {

    private int numSamples = 150;
    private int batchSize = 150;
    private DataSetIterator iter = new IrisDataSetIterator(batchSize, numSamples);
    private DataSet data;

    @Test
    public void testDenseBiasInit() {
        DenseLayer build = new DenseLayer.Builder()
                .nIn(1)
                .nOut(3)
                .biasInit(1)
                .build();

        NeuralNetConfiguration conf = new NeuralNetConfiguration.Builder()
                .layer(build)
                .build();

        int numParams = LayerFactories.getFactory(conf).initializer().numParams(conf,true);
        INDArray params = Nd4j.create(1, numParams);
        Layer layer =  LayerFactories.getFactory(conf).create(conf, null, 0, params, true);

        assertEquals(1, layer.getParam("b").size(0));
    }

    @Test
    public void testMLPMultiLayerPretrain(){
        // Note CNN does not do pretrain
        MultiLayerNetwork model = getDenseMLNConfig(false, true);
        model.fit(iter);

        MultiLayerNetwork model2 = getDenseMLNConfig(false, true);
        model2.fit(iter);
        iter.reset();

        DataSet test = iter.next();

        assertEquals(model.params(), model2.params());

        Evaluation eval = new Evaluation();
        INDArray output = model.output(test.getFeatureMatrix());
        eval.eval(test.getLabels(), output);
        double f1Score = eval.f1();

        Evaluation eval2 = new Evaluation();
        INDArray output2 = model2.output(test.getFeatureMatrix());
        eval2.eval(test.getLabels(), output2);
        double f1Score2 = eval2.f1();

        assertEquals(f1Score, f1Score2, 1e-4);

    }

    @Test
    public void testMLPMultiLayerBackprop(){
        MultiLayerNetwork model = getDenseMLNConfig(true, false);
        model.fit(iter);

        MultiLayerNetwork model2 = getDenseMLNConfig(true, false);
        model2.fit(iter);
        iter.reset();

        DataSet test = iter.next();

        assertEquals(model.params(), model2.params());

        Evaluation eval = new Evaluation();
        INDArray output = model.output(test.getFeatureMatrix());
        eval.eval(test.getLabels(), output);
        double f1Score = eval.f1();

        Evaluation eval2 = new Evaluation();
        INDArray output2 = model2.output(test.getFeatureMatrix());
        eval2.eval(test.getLabels(), output2);
        double f1Score2 = eval2.f1();

        assertEquals(f1Score, f1Score2, 1e-4);

    }


    //////////////////////////////////////////////////////////////////////////////////

    private static MultiLayerNetwork getDenseMLNConfig(boolean backprop, boolean pretrain) {
        int numInputs = 4;
        int outputNum = 3;
        int iterations = 10;
        long seed = 6;

        MultiLayerConfiguration conf = new NeuralNetConfiguration.Builder()
                .seed(seed)
                .iterations(iterations)
                .learningRate(1e-3)
                .l1(0.3)
                .regularization(true).l2(1e-3)
                .list()
                .layer(0, new org.deeplearning4j.nn.conf.layers.DenseLayer.Builder()
                        .nIn(numInputs)
                        .nOut(3)
                        .activation("tanh")
                        .weightInit(WeightInit.XAVIER)
                        .build())
                .layer(1, new org.deeplearning4j.nn.conf.layers.DenseLayer.Builder()
                        .nIn(3).nOut(2)
                        .activation("tanh")
                        .weightInit(WeightInit.XAVIER)
                        .build())
                .layer(2, new OutputLayer.Builder(LossFunctions.LossFunction.MCXENT)
                        .weightInit(WeightInit.XAVIER)
                        .nIn(2).nOut(outputNum).build())
                .backprop(backprop)
                .pretrain(pretrain)
                .build();

        MultiLayerNetwork model = new MultiLayerNetwork(conf);
        model.init();
        return model;

    }
}

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