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Java example source code file (CNNProcessorTest.java)

This example Java source code file (CNNProcessorTest.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

cnnprocessortest, cnntofeedforwardpreprocessor, convolutionlayersetup, dataset, exception, feedforwardtocnnpreprocessor, indarray, mnistdatasetiterator, multilayernetwork, test

The CNNProcessorTest.java Java example source code

package org.deeplearning4j.nn.conf.preprocessor;

import static org.junit.Assert.*;

import org.deeplearning4j.berkeley.Pair;
import org.deeplearning4j.datasets.iterator.impl.MnistDataSetIterator;
import org.deeplearning4j.nn.api.OptimizationAlgorithm;
import org.deeplearning4j.nn.conf.MultiLayerConfiguration;
import org.deeplearning4j.nn.conf.NeuralNetConfiguration;
import org.deeplearning4j.nn.conf.layers.*;
import org.deeplearning4j.nn.conf.layers.setup.ConvolutionLayerSetup;
import org.deeplearning4j.nn.gradient.Gradient;
import org.deeplearning4j.nn.multilayer.MultiLayerNetwork;
import org.deeplearning4j.nn.weights.WeightInit;
import org.junit.Test;
import org.nd4j.linalg.api.ndarray.INDArray;
import org.nd4j.linalg.convolution.Convolution;
import org.nd4j.linalg.dataset.DataSet;
import org.nd4j.linalg.factory.Nd4j;
import org.nd4j.linalg.lossfunctions.LossFunctions;

/**
 **/

public class CNNProcessorTest {
    private static int rows = 28;
    private static int cols = 28;
    private static INDArray in2D = Nd4j.create(1, 784);
    private static INDArray in3D = Nd4j.create(20, 784, 7);
    private static INDArray in4D = Nd4j.create(20, 1, 28, 28);


    @Test
    public void testFeedForwardToCnnPreProcessor() {
        FeedForwardToCnnPreProcessor convProcessor = new FeedForwardToCnnPreProcessor(rows, cols, 1);

        INDArray check2to4 = convProcessor.preProcess(in2D,-1);
        int val2to4 = check2to4.shape().length;
        assertTrue(val2to4 == 4);
        assertEquals(Nd4j.create(1, 1, 28, 28), check2to4);

        INDArray check4to4 = convProcessor.preProcess(in4D,-1);
        int val4to4 = check4to4.shape().length;
        assertTrue(val4to4 == 4);
        assertEquals(Nd4j.create(20, 1, 28, 28), check4to4);
    }

    @Test
    public void testFeedForwardToCnnPreProcessor2() {
        int[] nRows = {1, 5, 20};
        int[] nCols = {1, 5, 20};
        int[] nDepth = {1, 3};
        int[] nMiniBatchSize = {1, 5};
        for( int rows : nRows ){
            for( int cols : nCols) {
                for( int d : nDepth) {
                    FeedForwardToCnnPreProcessor convProcessor = new FeedForwardToCnnPreProcessor(rows, cols, d);

                    for( int miniBatch : nMiniBatchSize ) {
                        int[] ffShape = new int[]{miniBatch,rows*cols*d};
                        INDArray rand = Nd4j.rand(ffShape);
                        INDArray ffInput_c = Nd4j.create(ffShape, 'c');
                        INDArray ffInput_f = Nd4j.create(ffShape,'f');
                        ffInput_c.assign(rand);
                        ffInput_f.assign(rand);
                        assertEquals(ffInput_c, ffInput_f);

                        //Test forward pass:
                        INDArray convAct_c = convProcessor.preProcess(ffInput_c,-1);
                        INDArray convAct_f = convProcessor.preProcess(ffInput_f,-1);
                        int[] convShape = {miniBatch,d,rows,cols};
                        assertArrayEquals(convShape, convAct_c.shape());
                        assertArrayEquals(convShape, convAct_f.shape());
                        assertEquals(convAct_c,convAct_f);

                        //Check values:
                        //CNN reshaping (for each example) takes a 1d vector and converts it to 3d
                        // (4d total, for minibatch data)
                        //1d vector is assumed to be rows from depth 0 concatenated, followed by depth 1, etc
                        for( int ex=0; ex<miniBatch; ex++ ) {
                            for (int r = 0; r < rows; r++) {
                                for (int c = 0; c < cols; c++) {
                                    for (int depth = 0; depth < d; depth++) {
                                        int origPosition = depth * (rows * cols) + r * cols + c;  //pos in vector
                                        double vecValue = ffInput_c.getDouble(ex,origPosition);
                                        double convValue = convAct_c.getDouble(ex,depth,r,c);
                                        assertEquals(vecValue,convValue,0.0);
                                    }
                                }
                            }
                        }

                        //Test backward pass:
                        //Idea is that backward pass should do opposite to forward pass
                        INDArray epsilon4_c = Nd4j.create(convShape,'c');
                        INDArray epsilon4_f = Nd4j.create(convShape,'f');
                        epsilon4_c.assign(convAct_c);
                        epsilon4_f.assign(convAct_f);
                        INDArray epsilon2_c = convProcessor.backprop(epsilon4_c,-1);
                        INDArray epsilon2_f = convProcessor.backprop(epsilon4_f,-1);
                        assertEquals(ffInput_c,epsilon2_c);
                        assertEquals(ffInput_c,epsilon2_f);
                    }
                }
            }
        }
    }


    @Test
    public void testFeedForwardToCnnPreProcessorBackprop() {
        FeedForwardToCnnPreProcessor convProcessor = new FeedForwardToCnnPreProcessor(rows, cols, 1);
        convProcessor.preProcess(in2D,-1);

        INDArray check2to2 = convProcessor.backprop(in2D,-1);
        int val2to2 = check2to2.shape().length;
        assertTrue(val2to2 == 2);
        assertEquals(Nd4j.create(1, 784), check2to2);
    }

    @Test
    public void testCnnToFeedForwardProcessor() {
        CnnToFeedForwardPreProcessor convProcessor = new CnnToFeedForwardPreProcessor(rows, cols, 1);

        INDArray check2to4 = convProcessor.backprop(in2D,-1);
        int val2to4 = check2to4.shape().length;
        assertTrue(val2to4 == 4);
        assertEquals(Nd4j.create(1, 1, 28, 28), check2to4);

        INDArray check4to4 = convProcessor.backprop(in4D,-1);
        int val4to4 = check4to4.shape().length;
        assertTrue(val4to4 == 4);
        assertEquals(Nd4j.create(20, 1, 28, 28), check4to4);
    }

    @Test
    public void testCnnToFeedForwardPreProcessorBackprop() {
        CnnToFeedForwardPreProcessor convProcessor = new CnnToFeedForwardPreProcessor(rows, cols, 1);
        convProcessor.preProcess(in4D,-1);

        INDArray check2to2 = convProcessor.preProcess(in2D,-1);
        int val2to2 = check2to2.shape().length;
        assertTrue(val2to2 == 2);
        assertEquals(Nd4j.create(1, 784), check2to2);

        INDArray check4to2 = convProcessor.preProcess(in4D, -1);
        int val4to2 = check4to2.shape().length;
        assertTrue(val4to2 == 2);
        assertEquals(Nd4j.create(20, 784), check4to2);
    }

    @Test
    public void testCnnToFeedForwardPreProcessor2() {
        int[] nRows = {1, 5, 20};
        int[] nCols = {1, 5, 20};
        int[] nDepth = {1, 3};
        int[] nMiniBatchSize = {1, 5};
        for( int rows : nRows ){
            for( int cols : nCols ){
                for( int d : nDepth ){
                    CnnToFeedForwardPreProcessor convProcessor = new CnnToFeedForwardPreProcessor(rows, cols, d);

                    for( int miniBatch : nMiniBatchSize ) {
                        int[] convActShape = new int[]{miniBatch,d,rows,cols};
                        INDArray rand = Nd4j.rand(convActShape);
                        INDArray convInput_c = Nd4j.create(convActShape, 'c');
                        INDArray convInput_f = Nd4j.create(convActShape, 'f');
                        convInput_c.assign(rand);
                        convInput_f.assign(rand);
                        assertEquals(convInput_c, convInput_f);

                        //Test forward pass:
                        INDArray ffAct_c = convProcessor.preProcess(convInput_c,-1);
                        INDArray ffAct_f = convProcessor.preProcess(convInput_f,-1);
                        int[] ffActShape = {miniBatch,d*rows*cols};
                        assertArrayEquals(ffActShape, ffAct_c.shape());
                        assertArrayEquals(ffActShape, ffAct_f.shape());
                        assertEquals(ffAct_c,ffAct_f);

                        //Check values:
                        //CNN reshaping (for each example) takes a 1d vector and converts it to 3d
                        // (4d total, for minibatch data)
                        //1d vector is assumed to be rows from depth 0 concatenated, followed by depth 1, etc
                        for( int ex=0; ex<miniBatch; ex++ ) {
                            for (int r = 0; r < rows; r++) {
                                for (int c = 0; c < cols; c++) {
                                    for (int depth = 0; depth < d; depth++) {
                                        int vectorPosition = depth * (rows * cols) + r * cols + c;  //pos in vector after reshape
                                        double vecValue = ffAct_c.getDouble(ex,vectorPosition);
                                        double convValue = convInput_c.getDouble(ex,depth,r,c);
                                        assertEquals(convValue,vecValue,0.0);
                                    }
                                }
                            }
                        }

                        //Test backward pass:
                        //Idea is that backward pass should do opposite to forward pass
                        INDArray epsilon2_c = Nd4j.create(ffActShape,'c');
                        INDArray epsilon2_f = Nd4j.create(ffActShape,'f');
                        epsilon2_c.assign(ffAct_c);
                        epsilon2_f.assign(ffAct_c);
                        INDArray epsilon4_c = convProcessor.backprop(epsilon2_c,-1);
                        INDArray epsilon4_f = convProcessor.backprop(epsilon2_f,-1);
                        assertEquals(convInput_c,epsilon4_c);
                        assertEquals(convInput_c,epsilon4_f);
                    }
                }
            }
        }
    }

    @Test
    public void testCNNInputPreProcessorMnist() throws Exception {
        int numSamples = 1;
        int batchSize = 1;

        DataSet mnistIter = new MnistDataSetIterator(batchSize, numSamples, true).next();
        MultiLayerNetwork model = getCNNMnistConfig();
        model.init();
        model.fit(mnistIter);

        int val2to4 = model.getLayer(0).input().shape().length;
        assertTrue(val2to4 == 4);

        int val4to4 = model.getLayer(1).input().shape().length;
        assertTrue(val4to4 == 4);

    }


    public static MultiLayerNetwork getCNNMnistConfig()  {

        Nd4j.ENFORCE_NUMERICAL_STABILITY = true;

        MultiLayerConfiguration.Builder conf = new NeuralNetConfiguration.Builder()
                .seed(123)
                .iterations(5)
                .optimizationAlgo(OptimizationAlgorithm.LINE_GRADIENT_DESCENT)
                .list()
                .layer(0, new org.deeplearning4j.nn.conf.layers.ConvolutionLayer.Builder(new int[]{9, 9},new int[]{1,1})
                        .nOut(20)
                        .weightInit(WeightInit.XAVIER)
                        .activation("relu")
                        .build())
                .layer(1, new SubsamplingLayer.Builder(SubsamplingLayer.PoolingType.MAX, new int[]{2, 2})
                        .activation("relu")
                        .weightInit(WeightInit.XAVIER)
                        .build())
                .layer(2, new OutputLayer.Builder(LossFunctions.LossFunction.NEGATIVELOGLIKELIHOOD)
                        .nIn(20)
                        .nOut(10)
                        .weightInit(WeightInit.XAVIER)
                        .activation("softmax")
                        .build());
        new ConvolutionLayerSetup(conf,28,28,1);
        return new MultiLayerNetwork(conf.build());
    }
}

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