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Java example source code file (WeightInitUtilTest.java)

This example Java source code file (WeightInitUtilTest.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

before, distribution, indarray, test, weightinitutiltest

The WeightInitUtilTest.java Java example source code

package org.deeplearning4j.nn.weights;

import org.apache.commons.math3.util.FastMath;
import org.deeplearning4j.nn.conf.distribution.Distributions;
import org.deeplearning4j.nn.conf.distribution.GaussianDistribution;
import org.junit.Before;
import org.junit.Test;
import org.nd4j.linalg.api.ndarray.INDArray;
import org.nd4j.linalg.api.rng.distribution.Distribution;
import org.nd4j.linalg.factory.Nd4j;



import static org.junit.Assert.*;

/**
 * Created by nyghtowl on 11/14/15.
 */
public class WeightInitUtilTest {
    protected int[] shape = new int[]{2, 2};
    protected Distribution dist = Distributions.createDistribution(new GaussianDistribution(0.0, 0.1));

    @Before
    public void doBefore(){
        Nd4j.getRandom().setSeed(123);
    }

    @Test
    public void testDistribution(){
        INDArray params = Nd4j.create(shape,'f');
        INDArray weightsActual = WeightInitUtil.initWeights(shape, WeightInit.DISTRIBUTION, dist, params);

        // expected calculation
        Nd4j.getRandom().setSeed(123);
        INDArray weightsExpected = dist.sample(shape);

        assertEquals(weightsExpected, weightsActual);
    }

    @Test
    public void testNormalize(){
        INDArray params = Nd4j.create(shape,'f');
        INDArray weightsActual = WeightInitUtil.initWeights(shape, WeightInit.NORMALIZED, dist, params);

        // expected calculation
        Nd4j.getRandom().setSeed(123);
        INDArray weightsExpected =  Nd4j.rand('f',shape);
        weightsExpected.subi(0.5).divi(shape[0]);

        assertEquals(weightsExpected, weightsActual);
    }

    @Test
    public void testRelu(){
        INDArray params = Nd4j.create(shape,'f');
        INDArray weightsActual = WeightInitUtil.initWeights(shape, WeightInit.RELU, dist,params);

        // expected calculation
        Nd4j.getRandom().setSeed(123);
        INDArray weightsExpected = Nd4j.randn('f',shape).muli(FastMath.sqrt(2.0 / shape[0]));

        assertEquals(weightsExpected, weightsActual);
    }

    @Test
    public void testSize(){
        INDArray params = Nd4j.create(shape,'f');
        INDArray weightsActual = WeightInitUtil.initWeights(shape, WeightInit.SIZE, dist, params);

        // expected calculation
        Nd4j.getRandom().setSeed(123);
        double min = -4.0 * Math.sqrt(6.0 / (double) (shape[0] + shape[1]));
        double max = 4.0 * Math.sqrt(6.0 / (double) (shape[0] + shape[1]));
        INDArray weightsExpected = Nd4j.rand(shape, Nd4j.getDistributions().createUniform(min,max));

        assertEquals(weightsExpected, weightsActual);
    }

    @Test
    public void testUniform(){
        INDArray params = Nd4j.create(shape,'f');
        INDArray weightsActual = WeightInitUtil.initWeights(shape, WeightInit.UNIFORM, dist, params);

        // expected calculation
        Nd4j.getRandom().setSeed(123);
        double a = 1/(double) shape[0];
        INDArray weightsExpected = Nd4j.rand('f',shape).muli(2*a).subi(a);

        assertEquals(weightsExpected, weightsActual);
    }

    @Test
    public void testVI(){
        INDArray params = Nd4j.create(shape,'f');
        INDArray weightsActual = WeightInitUtil.initWeights(shape, WeightInit.VI, dist, params);

        // expected calculation
        Nd4j.getRandom().setSeed(123);
        INDArray weightsExpected = Nd4j.rand('f',shape);
        int numValues = shape[0] + shape[1];
        double r = Math.sqrt(6) / Math.sqrt(numValues + 1);
        weightsExpected.muli(2).muli(r).subi(r);

        assertEquals(weightsExpected, weightsActual);
    }

    @Test
    public void testXavier(){
        INDArray params = Nd4j.create(shape,'f');
        INDArray weightsActual = WeightInitUtil.initWeights(shape, WeightInit.XAVIER, dist, params);

        // expected calculation
        Nd4j.getRandom().setSeed(123);
        INDArray weightsExpected = Nd4j.randn('f',shape);
        weightsExpected.divi(FastMath.sqrt(shape[0] + shape[1]));

        assertEquals(weightsExpected, weightsActual);
    }


    @Test
    public void testZero(){
        INDArray params = Nd4j.create(shape,'f');
        INDArray weightsActual = WeightInitUtil.initWeights(shape, WeightInit.ZERO, dist, params);

        // expected calculation
        INDArray weightsExpected = Nd4j.create(shape,'f');

        assertEquals(weightsExpected, weightsActual);
    }


}

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