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Java example source code file (LayerBuilderTest.java)

This example Java source code file (LayerBuilderTest.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

batchnormalization, bytearrayinputstream, bytearrayoutputstream, convolutionlayer, delta, denselayer, exception, hiddenunit, neuralnetconfiguration, objectinputstream, string, test, visibleunit, yaml

The LayerBuilderTest.java Java example source code

package org.deeplearning4j.nn.conf.layers;

import org.deeplearning4j.nn.conf.GradientNormalization;
import org.deeplearning4j.nn.conf.NeuralNetConfiguration;
import org.deeplearning4j.nn.conf.Updater;
import org.deeplearning4j.nn.conf.distribution.Distribution;
import org.deeplearning4j.nn.conf.distribution.NormalDistribution;
import org.deeplearning4j.nn.conf.layers.*;
import org.deeplearning4j.nn.conf.layers.OutputLayer;
import org.deeplearning4j.nn.conf.layers.RBM.*;
import org.deeplearning4j.nn.conf.layers.SubsamplingLayer.PoolingType;
import org.deeplearning4j.nn.weights.WeightInit;
import org.junit.Test;
import static org.junit.Assert.*;

import org.nd4j.linalg.convolution.Convolution;
import org.nd4j.linalg.lossfunctions.LossFunctions.LossFunction;

import java.io.*;

/**
 * @author Jeffrey Tang.
 */
public class LayerBuilderTest {
    final double DELTA = 1e-15;

    int numIn = 10;
    int numOut = 5;
    double drop = 0.3;
    String act = "softmax";
    PoolingType poolType = PoolingType.MAX;
    int[] kernelSize = new int[]{2, 2};
    int[] stride = new int[]{2, 2};
    int[] padding = new int[]{1,1};
    HiddenUnit hidden = HiddenUnit.RECTIFIED;
    VisibleUnit visible = VisibleUnit.GAUSSIAN;
    int k  = 1;
    Convolution.Type convType = Convolution.Type.VALID;
    LossFunction loss = LossFunction.MCXENT;
    WeightInit weight = WeightInit.XAVIER;
    double corrupt = 0.4;
    double sparsity = 0.3;
    double corruptionLevel = 0.5;
    Distribution dist = new NormalDistribution(1.0, 0.1);
    double dropOut = 0.1;
    Updater updater = Updater.ADAGRAD;
    GradientNormalization gradNorm = GradientNormalization.ClipL2PerParamType;
    double gradNormThreshold = 8;

    @Test
    public void testLayer() throws Exception {
        DenseLayer layer = new DenseLayer.Builder()
            .activation(act).weightInit(weight).dist(dist).dropOut(dropOut).updater(updater)
            .gradientNormalization(gradNorm).gradientNormalizationThreshold(gradNormThreshold)
            .build();

        checkSerialization(layer);

        assertEquals(act, layer.getActivationFunction());
        assertEquals(weight, layer.getWeightInit());
        assertEquals(dist, layer.getDist());
        assertEquals(dropOut, layer.getDropOut(), DELTA);
        assertEquals(updater, layer.getUpdater());
        assertEquals(gradNorm, layer.getGradientNormalization());
        assertEquals(gradNormThreshold, layer.getGradientNormalizationThreshold(), 0.0);
    }

    @Test
    public void testFeedForwardLayer() throws Exception {
        DenseLayer ff = new DenseLayer.Builder().nIn(numIn).nOut(numOut).build();

        checkSerialization(ff);

        assertEquals(numIn, ff.getNIn());
        assertEquals(numOut, ff.getNOut());
    }
    @Test
    public void testConvolutionLayer() throws Exception {
        ConvolutionLayer conv = new ConvolutionLayer.Builder(kernelSize, stride, padding)
                .convolutionType(convType).build();

        checkSerialization(conv);

        assertEquals(convType, conv.getConvolutionType());
        assertArrayEquals(kernelSize, conv.getKernelSize());
        assertArrayEquals(stride, conv.getStride());
        assertArrayEquals(padding, conv.getPadding());
    }

    @Test
    public void testRBM() throws Exception {
        RBM rbm = new RBM.Builder(hidden, visible).sparsity(sparsity).k(k).build();

        checkSerialization(rbm);

        assertEquals(hidden, rbm.getHiddenUnit());
        assertEquals(visible, rbm.getVisibleUnit());
        assertEquals(k, rbm.getK());
        assertEquals(sparsity, rbm.getSparsity(), DELTA);
    }

    @Test
    public void testSubsamplingLayer() throws Exception {
        SubsamplingLayer sample = new SubsamplingLayer.Builder(poolType, stride)
                .kernelSize(kernelSize)
                .padding(padding)
                .build();

        checkSerialization(sample);

        assertArrayEquals(padding, sample.getPadding());
        assertArrayEquals(kernelSize, sample.getKernelSize());
        assertEquals(poolType, sample.getPoolingType());
        assertArrayEquals(stride, sample.getStride());
    }

    @Test
    public void testOutputLayer() throws Exception {
        OutputLayer out = new OutputLayer.Builder(loss).build();

        checkSerialization(out);

        assertEquals(loss, out.getLossFunction());
    }
    
    @Test
    public void testRnnOutputLayer() throws Exception {
    	RnnOutputLayer out = new RnnOutputLayer.Builder(loss).build();
    	
    	checkSerialization(out);
    	
    	assertEquals(loss, out.getLossFunction());
    }

    @Test
    public void testAutoEncoder() throws Exception {
        AutoEncoder enc = new AutoEncoder.Builder().corruptionLevel(corruptionLevel).sparsity(sparsity).build();

        checkSerialization(enc);

        assertEquals(corruptionLevel, enc.getCorruptionLevel(), DELTA);
        assertEquals(sparsity, enc.getSparsity(), DELTA);
    }
    
    @Test
    public void testGravesLSTM() throws Exception {
    	GravesLSTM glstm = new GravesLSTM.Builder()
                .forgetGateBiasInit(1.5)
                .activation("tanh")
    			.nIn(numIn).nOut(numOut).build();
    	
    	checkSerialization(glstm);

        assertEquals(glstm.getForgetGateBiasInit(),1.5,0.0);
    	assertEquals(glstm.nIn, numIn);
    	assertEquals(glstm.nOut,numOut);
    	assertEquals(glstm.activationFunction,"tanh");
    }

    @Test
    public void testGravesBidirectionalLSTM() throws Exception {
        final GravesBidirectionalLSTM glstm = new GravesBidirectionalLSTM.Builder()
                .forgetGateBiasInit(1.5)
                .activation("tanh")
                .nIn(numIn).nOut(numOut).build();

        checkSerialization(glstm);

        assertEquals(glstm.getForgetGateBiasInit(),1.5,0.0);
        assertEquals(glstm.nIn,numIn);
        assertEquals(glstm.nOut,numOut);
        assertEquals(glstm.activationFunction,"tanh");
    }
    
    @Test
    public void testGRU() throws Exception {
    	GRU gru = new GRU.Builder().activation("tanh")
    			.nIn(numIn).nOut(numOut).build();
    	
    	checkSerialization(gru);
    	
    	assertEquals(gru.nIn,numIn);
    	assertEquals(gru.nOut,numOut);
    	assertEquals(gru.activationFunction,"tanh");
    }

    @Test
    public void testEmbeddingLayer() throws Exception {
        EmbeddingLayer el = new EmbeddingLayer.Builder().nIn(10).nOut(5).build();
        checkSerialization(el);

        assertEquals(10,el.getNIn());
        assertEquals(5,el.getNOut());
    }

    @Test
    public void testBatchNormLayer() throws Exception {
        BatchNormalization bN = new BatchNormalization.Builder()
                .nIn(numIn).nOut(numOut)
                .gamma(2).beta(1).decay(0.5).lockGammaBeta(true).build();

        checkSerialization(bN);

        assertEquals(numIn, bN.nIn);
        assertEquals(numOut, bN.nOut);
        assertEquals(true, bN.isLockGammaBeta());
        assertEquals(0.5, bN.decay, 1e-4);
        assertEquals(2, bN.gamma, 1e-4);
        assertEquals(1, bN.beta, 1e-4);
    }

    @Test
    public void testActivationLayer() throws Exception {
        ActivationLayer activationLayer = new ActivationLayer.Builder()
                .nIn(numIn).nOut(numOut).activation(act).build();

        checkSerialization(activationLayer);

        assertEquals(numIn, activationLayer.nIn);
        assertEquals(numOut, activationLayer.nOut);
        assertEquals(act, activationLayer.activationFunction);
    }

    private void checkSerialization(Layer layer) throws Exception {
        NeuralNetConfiguration confExpected = new NeuralNetConfiguration.Builder()
                .layer(layer)
                .build();
        NeuralNetConfiguration confActual;

        // check Java serialization
        byte[] data;
        try (ByteArrayOutputStream bos = new ByteArrayOutputStream();
             ObjectOutput out = new ObjectOutputStream(bos)) {
            out.writeObject(confExpected);
            data = bos.toByteArray();
        }
        try (ByteArrayInputStream bis = new ByteArrayInputStream(data);
             ObjectInput in = new ObjectInputStream(bis)) {
            confActual = (NeuralNetConfiguration) in.readObject();
        }
        assertEquals("unequal Java serialization", confExpected.getLayer(), confActual.getLayer());

        // check JSON
        String json = confExpected.toJson();
        confActual = NeuralNetConfiguration.fromJson(json);
        assertEquals("unequal JSON serialization", confExpected.getLayer(), confActual.getLayer());

        // check YAML
        String yaml = confExpected.toYaml();
        confActual = NeuralNetConfiguration.fromYaml(yaml);
        assertEquals("unequal YAML serialization", confExpected.getLayer(), confActual.getLayer());

        // check the layer's use of callSuper on equals method
        confActual.getLayer().setDropOut(new java.util.Random().nextDouble());
        assertNotEquals("broken equals method (missing callSuper?)", confExpected.getLayer(), confActual.getLayer());
    }

}

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