cavis/cavis-dnn/cavis-dnn-core/src/test/java/org/deeplearning4j/gradientcheck/CapsnetGradientCheckTest.java

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package org.deeplearning4j.gradientcheck;

import static org.junit.jupiter.api.Assertions.assertTrue;

import org.deeplearning4j.BaseDL4JTest;
import org.deeplearning4j.TestUtils;
import org.deeplearning4j.nn.conf.MultiLayerConfiguration;
import org.deeplearning4j.nn.conf.NeuralNetConfiguration;
import org.deeplearning4j.nn.conf.distribution.UniformDistribution;
import org.deeplearning4j.nn.conf.inputs.InputType;
import org.deeplearning4j.nn.conf.layers.ActivationLayer;
import org.deeplearning4j.nn.conf.layers.CapsuleLayer;
import org.deeplearning4j.nn.conf.layers.CapsuleStrengthLayer;
import org.deeplearning4j.nn.conf.layers.LossLayer;
import org.deeplearning4j.nn.conf.layers.PrimaryCapsules;
import org.deeplearning4j.nn.multilayer.MultiLayerNetwork;
import org.deeplearning4j.nn.weights.WeightInitDistribution;

import org.junit.jupiter.api.Test;
import org.nd4j.linalg.activations.impl.ActivationSoftmax;
import org.nd4j.linalg.api.buffer.DataType;
import org.nd4j.linalg.api.ndarray.INDArray;
import org.nd4j.linalg.factory.Nd4j;
import org.nd4j.linalg.learning.config.NoOp;
import org.nd4j.linalg.lossfunctions.impl.LossNegativeLogLikelihood;

import java.util.Random;

////@Ignore
public class CapsnetGradientCheckTest extends BaseDL4JTest {

    @Test
    public void testCapsNet() {

        int[] minibatchSizes = {8, 16};

        int width = 6;
        int height = 6;
        int inputDepth = 4;

        int[] primaryCapsDims = {2, 4};
        int[] primaryCapsChannels = {8};
        int[] capsules = {5};
        int[] capsuleDims = {4, 8};
        int[] routings = {1};

        Nd4j.getRandom().setSeed(12345);

        for (int routing : routings) {
            for (int primaryCapsDim : primaryCapsDims) {
                for (int primarpCapsChannel : primaryCapsChannels) {
                    for (int capsule : capsules) {
                        for (int capsuleDim : capsuleDims) {
                            for (int minibatchSize : minibatchSizes) {

                                INDArray input = Nd4j.rand(minibatchSize, inputDepth * height * width).mul(10)
                                        .reshape(-1, inputDepth, height, width);
                                INDArray labels = Nd4j.zeros(minibatchSize, capsule);
                                for (int i = 0; i < minibatchSize; i++) {
                                    labels.putScalar(new int[]{i, i % capsule}, 1.0);
                                }

                                MultiLayerConfiguration conf = new NeuralNetConfiguration.Builder()
                                        .dataType(DataType.DOUBLE)
                                        .seed(123)
                                        .updater(new NoOp())
                                        .weightInit(new WeightInitDistribution(new UniformDistribution(-6, 6)))
                                        .list()
                                        .layer(new PrimaryCapsules.Builder(primaryCapsDim, primarpCapsChannel)
                                                .kernelSize(3, 3)
                                                .stride(2, 2)
                                                .build())
                                        .layer(new CapsuleLayer.Builder(capsule, capsuleDim, routing).build())
                                        .layer(new CapsuleStrengthLayer.Builder().build())
                                        .layer(new ActivationLayer.Builder(new ActivationSoftmax()).build())
                                        .layer(new LossLayer.Builder(new LossNegativeLogLikelihood()).build())
                                        .setInputType(InputType.convolutional(height, width, inputDepth))
                                        .build();

                                MultiLayerNetwork net = new MultiLayerNetwork(conf);
                                net.init();

                                for (int i = 0; i < 4; i++) {
                                    System.out.println("nParams, layer " + i + ": " + net.getLayer(i).numParams());
                                }

                                String msg = "minibatch=" + minibatchSize +
                                        ", PrimaryCaps: " + primarpCapsChannel +
                                        " channels, " + primaryCapsDim + " dimensions, Capsules: " + capsule +
                                        " capsules with " + capsuleDim + " dimensions and " + routing + " routings";
                                System.out.println(msg);

                                boolean gradOK = GradientCheckUtil.checkGradients(new GradientCheckUtil.MLNConfig().net(net).input(input)
                                        .labels(labels).subset(true).maxPerParam(100));

                                assertTrue(gradOK, msg);

                                TestUtils.testModelSerialization(net);
                            }
                        }
                    }
                }
            }
        }
    }
}