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Java example source code file (GradientCheckTestsMasking.java)

This example Java source code file (GradientCheckTestsMasking.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

default_eps, default_max_rel_error, gradientchecktestsmasking, indarray, multilayerconfiguration, multilayernetwork, normaldistribution, print_results, random, return_on_first_failure, string, test, util

The GradientCheckTestsMasking.java Java example source code

package org.deeplearning4j.gradientcheck;

import org.deeplearning4j.nn.conf.MultiLayerConfiguration;
import org.deeplearning4j.nn.conf.NeuralNetConfiguration;
import org.deeplearning4j.nn.conf.Updater;
import org.deeplearning4j.nn.conf.distribution.NormalDistribution;
import org.deeplearning4j.nn.conf.layers.GravesLSTM;
import org.deeplearning4j.nn.conf.layers.RnnOutputLayer;
import org.deeplearning4j.nn.multilayer.MultiLayerNetwork;
import org.deeplearning4j.nn.weights.WeightInit;
import org.junit.Test;
import org.nd4j.linalg.api.buffer.DataBuffer;
import org.nd4j.linalg.api.buffer.util.DataTypeUtil;
import org.nd4j.linalg.api.ndarray.INDArray;
import org.nd4j.linalg.factory.NDArrayFactory;
import org.nd4j.linalg.factory.Nd4j;
import org.nd4j.linalg.lossfunctions.LossFunctions;

import java.util.Random;

import static org.junit.Assert.assertTrue;

/**Gradient checking tests with masking (i.e., variable length time series inputs, one-to-many and many-to-one etc)
 */
public class GradientCheckTestsMasking {

    private static final boolean PRINT_RESULTS = true;
    private static final boolean RETURN_ON_FIRST_FAILURE = false;
    private static final double DEFAULT_EPS = 1e-6;
    private static final double DEFAULT_MAX_REL_ERROR = 1e-3;

    static {
        //Force Nd4j initialization, then set data type to double:
        Nd4j.zeros(1);
        DataTypeUtil.setDTypeForContext(DataBuffer.Type.DOUBLE);
    }

    @Test
    public void gradientCheckMaskingOutputSimple(){

        int timeSeriesLength = 5;
        boolean[][] mask = new boolean[5][0];
        mask[0] = new boolean[]{true,true,true,true,true};          //No masking
        mask[1] = new boolean[]{false,true,true,true,true};         //mask first output time step
        mask[2] = new boolean[]{false,false,false,false,true};      //time series classification: mask all but last
        mask[3] = new boolean[]{false,false,true,false,true};       //time series classification w/ variable length TS
        mask[4] = new boolean[]{true,true,true,false,true};         //variable length TS

        int nIn = 4;
        int layerSize = 3;
        int nOut = 2;


        Random r = new Random(12345L);
        INDArray input = Nd4j.zeros(1, nIn, timeSeriesLength);
        for( int m=0; m<1; m++ ){
            for( int j=0; j<nIn; j++ ){
                for( int k=0; k<timeSeriesLength; k++ ){
                    input.putScalar(new int[]{m,j,k},r.nextDouble() - 0.5);
                }
            }
        }

        INDArray labels = Nd4j.zeros(1,nOut,timeSeriesLength);
        for( int m=0; m<1; m++){
            for( int j=0; j<timeSeriesLength; j++ ){
                int idx = r.nextInt(nOut);
                labels.putScalar(new int[]{m,idx,j}, 1.0f);
            }
        }

        for(int i=0; i<mask.length; i++ ) {

            //Create mask array:
            INDArray maskArr = Nd4j.create(1,timeSeriesLength);
            for(int j=0; j<mask[i].length; j++){
                maskArr.putScalar(new int[]{0,j},mask[i][j] ? 1.0 : 0.0);
            }

            MultiLayerConfiguration conf = new NeuralNetConfiguration.Builder()
                    .regularization(false)
                    .seed(12345L)
                    .list()
                    .layer(0, new GravesLSTM.Builder().nIn(nIn).nOut(layerSize).weightInit(WeightInit.DISTRIBUTION)
                            .dist(new NormalDistribution(0, 1)).updater(Updater.NONE).build())
                    .layer(1, new RnnOutputLayer.Builder(LossFunctions.LossFunction.MCXENT).activation("softmax").nIn(layerSize).nOut(nOut)
                            .weightInit(WeightInit.DISTRIBUTION).dist(new NormalDistribution(0, 1)).updater(Updater.NONE).build())
                    .pretrain(false).backprop(true)
                    .build();
            MultiLayerNetwork mln = new MultiLayerNetwork(conf);
            mln.init();

            mln.setLayerMaskArrays(null,maskArr);

            boolean gradOK = GradientCheckUtil.checkGradients(mln, DEFAULT_EPS, DEFAULT_MAX_REL_ERROR,
                    PRINT_RESULTS, RETURN_ON_FIRST_FAILURE, input, labels, true);

            String msg = "gradientCheckMaskingOutputSimple() - timeSeriesLength=" + timeSeriesLength + ", miniBatchSize=" + 1;
            assertTrue(msg, gradOK);

        }
    }

}

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