Сравнение моей нейронной сети с JMH, но как мне смешать мои зависимости maven?

Я следовал этому руководству (http://tutorials.jenkov.com/java-performance/jmh.html) и открыли новый проект с этим классом MyBenchmark, который выглядит следующим образом:

package com.jenkov;

import org.openjdk.jmh.annotations.Benchmark;
import org.datavec.api.records.reader.RecordReader;
import org.datavec.api.records.reader.impl.csv.CSVRecordReader;
import org.datavec.api.split.FileSplit;
import org.deeplearning4j.datasets.datavec.RecordReaderDataSetIterator;
import org.deeplearning4j.nn.conf.MultiLayerConfiguration;
import org.deeplearning4j.nn.conf.NeuralNetConfiguration;
import org.deeplearning4j.nn.conf.layers.DenseLayer;
import org.deeplearning4j.nn.conf.layers.OutputLayer;
import org.deeplearning4j.nn.multilayer.MultiLayerNetwork;
import org.deeplearning4j.nn.weights.WeightInit;
import org.deeplearning4j.optimize.listeners.PerformanceListener;
import org.deeplearning4j.optimize.listeners.ScoreIterationListener;
import org.deeplearning4j.examples.utils.DownloaderUtility;
import org.nd4j.evaluation.classification.Evaluation;
import org.nd4j.linalg.activations.Activation;
import org.nd4j.linalg.api.ndarray.INDArray;
import org.nd4j.linalg.dataset.DataSet;
import org.nd4j.linalg.dataset.SplitTestAndTrain;
import org.nd4j.linalg.dataset.api.iterator.DataSetIterator;
import org.nd4j.linalg.dataset.api.preprocessor.DataNormalization;
import org.nd4j.linalg.dataset.api.preprocessor.NormalizerStandardize;
import org.nd4j.linalg.learning.config.Sgd;
import org.nd4j.linalg.lossfunctions.LossFunctions;
import org.slf4j.Logger;
import org.slf4j.LoggerFactory;

import java.io.File;
import java.io.File;

public class MyBenchmark {

    @Benchmark
    public void testMethod() {


            Logger log = LoggerFactory.getLogger(MyBenchmark.class);


                //First: get the dataset using the record reader. CSVRecordReader handles loading/parsing
                int numLinesToSkip = 0;
                char delimiter = ',';
                RecordReader recordReader = new CSVRecordReader(numLinesToSkip,delimiter);
                recordReader.initialize(new FileSplit(new File(DownloaderUtility.IRISDATA.Download(),"iris.txt")));

                //Second: the RecordReaderDataSetIterator handles conversion to DataSet objects, ready for use in neural network
                int labelIndex = 4;     //5 values in each row of the iris.txt CSV: 4 input features followed by an integer label (class) index. Labels are the 5th value (index 4) in each row
                int numClasses = 3;     //3 classes (types of iris flowers) in the iris data set. Classes have integer values 0, 1 or 2
                int batchSize = 150;    //Iris data set: 150 examples total. We are loading all of them into one DataSet (not recommended for large data sets)

                DataSetIterator iterator = new RecordReaderDataSetIterator(recordReader,batchSize,labelIndex,numClasses);
                DataSet allData = iterator.next();
                allData.shuffle();
                SplitTestAndTrain testAndTrain = allData.splitTestAndTrain(0.65);  //Use 65% of data for training

                DataSet trainingData = testAndTrain.getTrain();
                DataSet testData = testAndTrain.getTest();

                //We need to normalize our data. We'll use NormalizeStandardize (which gives us mean 0, unit variance):
                DataNormalization normalizer = new NormalizerStandardize();
                normalizer.fit(trainingData);           //Collect the statistics (mean/stdev) from the training data. This does not modify the input data
                normalizer.transform(trainingData);     //Apply normalization to the training data
                normalizer.transform(testData);         //Apply normalization to the test data. This is using statistics calculated from the *training* set


                final int numInputs = 4;
                int outputNum = 3;
                long seed = 6;


                log.info("Build model....");
                MultiLayerConfiguration conf = new NeuralNetConfiguration.Builder()
                    .seed(seed)
                    .activation(Activation.TANH)
                    .weightInit(WeightInit.XAVIER)
                    .updater(new Sgd(0.1))
                    .l2(1e-4)
                    .list()
                    .layer(new DenseLayer.Builder().nIn(numInputs).nOut(3)
                        .build())
                    .layer(new DenseLayer.Builder().nIn(3).nOut(3)
                        .build())
                    .layer( new OutputLayer.Builder(LossFunctions.LossFunction.NEGATIVELOGLIKELIHOOD)
                        .activation(Activation.SOFTMAX) //Override the global TANH activation with softmax for this layer
                        .nIn(3).nOut(outputNum).build())
                    .build();

                //run the model
                MultiLayerNetwork model = new MultiLayerNetwork(conf);
                model.init();
                //record score once every 100 iterations
                model.setListeners(new ScoreIterationListener(100));
                model.setListeners(new PerformanceListener(100));
                for(int i=0; i<1000; i++ ) {
                    model.fit(trainingData);
                }

                //evaluate the model on the test set
                Evaluation eval = new Evaluation(3);
                INDArray output = model.output(testData.getFeatures());
                eval.eval(testData.getLabels(), output);
                log.info(eval.stats());

            }

        }

и вот мой pom.xml с зависимостью от deeplearning4j-examples:

<project xmlns="http://maven.apache.org/POM/4.0.0" xmlns:xsi="http://www.w3.org/2001/XMLSchema-instance"
     xsi:schemaLocation="http://maven.apache.org/POM/4.0.0 http://maven.apache.org/xsd/maven-4.0.0.xsd">
<modelVersion>4.0.0</modelVersion>

<groupId>com.jenkov</groupId>
<artifactId>first-benchmark</artifactId>
<version>1.0</version>
<packaging>jar</packaging>

<name>JMH benchmark sample: Java</name>

<!--
   This is the demo/sample template build script for building Java benchmarks with JMH.
   Edit as needed.
-->

<dependencies>
    <dependency>
        <groupId>org.openjdk.jmh</groupId>
        <artifactId>jmh-core</artifactId>
        <version>${jmh.version}</version>
    </dependency>
    <dependency>
        <groupId>org.openjdk.jmh</groupId>
        <artifactId>jmh-generator-annprocess</artifactId>
        <version>${jmh.version}</version>
        <scope>provided</scope>
    </dependency>
    <dependency>
        <groupId>org.datavec</groupId>
        <artifactId>datavec-api</artifactId>
        <version>1.0.0-beta7</version>
    </dependency>
    <dependency>
        <groupId>org.deeplearning4j</groupId>
        <artifactId>deeplearning4j-datasets</artifactId>
        <version>1.0.0-beta7</version>
    </dependency>
    <dependency>
        <groupId>org.deeplearning4j</groupId>
        <artifactId>deeplearning4j-examples</artifactId>
        <version>0.0.3.1</version>
    </dependency>
    <dependency>
        <groupId>org.deeplearning4j</groupId>
        <artifactId>deeplearning4j-nn</artifactId>
        <version>1.0.0-beta7</version>
    </dependency>
    <dependency>
        <groupId>com.jenkov</groupId>
        <artifactId>first-benchmark</artifactId>
        <version>1.0</version>
    </dependency>
</dependencies>

<properties>
    <project.build.sourceEncoding>UTF-8</project.build.sourceEncoding>

    <!--
        JMH version to use with this project.
      -->
    <jmh.version>1.28</jmh.version>

    <!--
        Java source/target to use for compilation.
      -->
    <javac.target>1.8</javac.target>

    <!--
        Name of the benchmark Uber-JAR to generate.
      -->
    <uberjar.name>benchmarks</uberjar.name>
</properties>

<build>
    <plugins>
        <plugin>
            <groupId>org.apache.maven.plugins</groupId>
            <artifactId>maven-compiler-plugin</artifactId>
            <version>3.8.0</version>
            <configuration>
                <compilerVersion>${javac.target}</compilerVersion>
                <source>${javac.target}</source>
                <target>${javac.target}</target>
            </configuration>
        </plugin>
        <plugin>
            <groupId>org.apache.maven.plugins</groupId>
            <artifactId>maven-shade-plugin</artifactId>
            <version>3.2.1</version>
            <executions>
                <execution>
                    <phase>package</phase>
                    <goals>
                        <goal>shade</goal>
                    </goals>
                    <configuration>
                        <finalName>${uberjar.name}</finalName>
                        <transformers>
                            <transformer implementation="org.apache.maven.plugins.shade.resource.ManifestResourceTransformer">
                                <mainClass>org.openjdk.jmh.Main</mainClass>
                            </transformer>
                            <transformer implementation="org.apache.maven.plugins.shade.resource.ServicesResourceTransformer"/>
                        </transformers>
                        <filters>
                            <filter>
                                <!--
                                    Shading signed JARs will fail without this.
                                    http://stackoverflow.com/questions/999489/invalid-signature-file-when-attempting-to-run-a-jar
                                -->
                                <artifact>*:*</artifact>
                                <excludes>
                                    <exclude>META-INF/*.SF</exclude>
                                    <exclude>META-INF/*.DSA</exclude>
                                    <exclude>META-INF/*.RSA</exclude>
                                </excludes>
                            </filter>
                        </filters>
                    </configuration>
                </execution>
            </executions>
        </plugin>
    </plugins>
    <pluginManagement>
        <plugins>
            <plugin>
                <artifactId>maven-clean-plugin</artifactId>
                <version>2.5</version>
            </plugin>
            <plugin>
                <artifactId>maven-deploy-plugin</artifactId>
                <version>2.8.1</version>
            </plugin>
            <plugin>
                <artifactId>maven-install-plugin</artifactId>
                <version>2.5.1</version>
            </plugin>
            <plugin>
                <artifactId>maven-jar-plugin</artifactId>
                <version>2.4</version>
            </plugin>
            <plugin>
                <artifactId>maven-javadoc-plugin</artifactId>
                <version>2.9.1</version>
            </plugin>
            <plugin>
                <artifactId>maven-resources-plugin</artifactId>
                <version>2.6</version>
            </plugin>
            <plugin>
                <artifactId>maven-site-plugin</artifactId>
                <version>3.3</version>
            </plugin>
            <plugin>
                <artifactId>maven-source-plugin</artifactId>
                <version>2.2.1</version>
            </plugin>
            <plugin>
                <artifactId>maven-surefire-plugin</artifactId>
                <version>2.17</version>
            </plugin>
        </plugins>
    </pluginManagement>
</build>

Теперь, насколько я понял, я должен поместить туда свой код нейронной сети (который является DL4J-Example-NN), запустить «mvn clean install» в терминале и запустить там «java -jar target/benchmarks.jar». , НО My.Benchmark.java не может импортировать

import org.deeplearning4j.examples.utils.DownloaderUtility;

хотя я объявил зависимость

<dependency>
            <groupId>org.deeplearning4j</groupId>
            <artifactId>deeplearning4j-examples</artifactId>
            <version>0.0.3.1</version>
        </dependency>

за это Что мне не хватает? Этот метод вообще правильный?

Большое спасибо