如何解决使用 JMH 对我的神经网络进行基准测试，但如何混合我的 Maven 依赖项？
我遵循本指南 (http://tutorials.jenkov.com/java-performance/jmh.html) 并使用 MyBenchmark 类打开了一个新项目，如下所示：
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());
}
}
我的 pom.xml 中包含 deeplearning4j-examples 依赖项：
<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>
现在据我所知，我必须将我的神经网络代码放在那里（这是一个 DL4J-Example-NN），在终端中运行“mvn clean install”并运行“java -jar target/benchmarks.js”。 jar”，但 My.Benchmark.java 无法导入
import org.deeplearning4j.examples.utils.DownloaderUtility;
即使我声明了依赖
<dependency>
<groupId>org.deeplearning4j</groupId>
<artifactId>deeplearning4j-examples</artifactId>
<version>0.0.3.1</version>
</dependency>
为了它 我错过了什么？这种方法是否正确？
非常感谢
解决方法
您需要构建一个可执行的 JAR。
参见例如How can I create an executable JAR with dependencies using Maven? 了解如何使用 Maven 执行此操作。
您可以使用 maven 程序集或 maven shade 插件。
,这可能有点棘手，我不知道你到底做了什么。但这是让它工作的方法。
-
制作一个简单的 hello world jmh 类型的项目并测试它是否有效。
-
下载 dl4j 示例 github 并测试示例以确保其有效。
-
既然两者都在工作，请进入 DL4J 示例并注意不同的包如何具有不同的 pom 文件。此外，还有一个用于整个 dl4j 父级的主 pom。您应该使用 DL4J 主示例 POM！不要尝试手工创建自己的
-
我的建议是创建一个新项目。使用 dl4j master 作为你的父 pom 文件，然后在你的项目目录中添加第二个 pom 文件。您可以在 dl4j 示例 github 中清楚地看到这一点，这似乎也是您的情况的最佳实践。
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