Java机器学习框架deeplearing4j入门教程
1.添加项目
maven添加依赖 or 导入jar包 or 使用jvm
<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>YOURPROJECTNAME.com</groupId> <artifactId>YOURPROJECTNAME</artifactId> <version>1.0-SNAPSHOT</version> <packaging>jar</packaging> <name>YOURNAME</name> <url>http://maven.apache.org</url> <properties> <nd4j.backend>nd4j-native-platform</nd4j.backend> <project.build.sourceEncoding>UTF-8</project.build.sourceEncoding> <shadedClassifier>bin</shadedClassifier> <java.version>1.7</java.version> <nd4j.version>0.6.0</nd4j.version> <dl4j.version>0.6.0</dl4j.version> <datavec.version>0.6.0</datavec.version> <arbiter.version>0.6.0</arbiter.version> <guava.version>19.0</guava.version> <logback.version>1.1.7</logback.version> <jfreechart.version>1.0.13</jfreechart.version> <maven-shade-plugin.version>2.4.3</maven-shade-plugin.version> <exec-maven-plugin.version>1.4.0</exec-maven-plugin.version> <maven.minimum.version>3.3.1</maven.minimum.version> </properties> <dependencyManagement> <dependencies> <dependency> <groupId>org.nd4j</groupId> <artifactId>nd4j-native-platform</artifactId> <version>${nd4j.version}</version> </dependency> <dependency> <groupId>org.nd4j</groupId> <artifactId>nd4j-cuda-7.5-platform</artifactId> <version>${nd4j.version}</version> </dependency> </dependencies> </dependencyManagement> <dependencies> <dependency> <groupId>junit</groupId> <artifactId>junit</artifactId> <version>3.8.1</version> <scope>test</scope> </dependency> <!-- ND4J后端。每个DL4J项目都需要一个。一般将artifactId指定为"nd4j-native-platform"或者"nd4j-cuda-7.5-platform" --> <dependency> <groupId>org.nd4j</groupId> <artifactId>${nd4j.backend}</artifactId> </dependency> <!-- DL4J核心功能 --> <dependency> <groupId>org.deeplearning4j</groupId> <artifactId>deeplearning4j-core</artifactId> <version>${dl4j.version}</version> </dependency> <dependency> <groupId>org.deeplearning4j</groupId> <artifactId>deeplearning4j-nlp</artifactId> <version>${dl4j.version}</version> </dependency> <!-- deeplearning4j-ui用于HistogramIterationListener + 可视化：参见http://deeplearning4j.org/cn/visualization --> <dependency> <groupId>org.deeplearning4j</groupId> <artifactId>deeplearning4j-ui</artifactId> <version>${dl4j.version}</version> </dependency> <!-- 强制指定使用UI/HistogramIterationListener时的guava版本 --> <dependency> <groupId>com.google.guava</groupId> <artifactId>guava</artifactId> <version>${guava.version}</version> </dependency> <!-- datavec-data-codec：仅用于在视频处理示例中加载视频数据 --> <dependency> <artifactId>datavec-data-codec</artifactId> <groupId>org.datavec</groupId> <version>${datavec.version}</version> </dependency> <!-- 用于前馈/分类/MLP*和前馈/回归/RegressionMathFunctions示例 --> <dependency> <groupId>jfree</groupId> <artifactId>jfreechart</artifactId> <version>${jfreechart.version}</version> </dependency> <!-- Arbiter：用于超参数优化示例 --> <dependency> <groupId>org.deeplearning4j</groupId> <artifactId>arbiter-deeplearning4j</artifactId> <version>${arbiter.version}</version> </dependency> </dependencies> </project>
2.项目引用库
import org.deeplearning4j.nn.multilayer._ import org.deeplearning4j.nn.graph._ import org.deeplearning4j.nn.conf._ import org.deeplearning4j.nn.conf.inputs._ import org.deeplearning4j.nn.conf.layers._ import org.deeplearning4j.nn.weights._ import org.deeplearning4j.optimize.listeners._ import org.deeplearning4j.datasets.datavec.RecordReaderMultiDataSetIterator import org.deeplearning4j.eval.Evaluation import org.nd4j.linalg.learning.config._ // for different updaters like Adam, Nesterovs, etc. import org.nd4j.linalg.activations.Activation // defines different activation functions like RELU, SOFTMAX, etc. import org.nd4j.linalg.lossfunctions.LossFunctions // mean squared error, multiclass cross entropy, etc.
3.准备加载数据
dl4j有数据迭代器。帮助批处理和迭代数据集。Deeplearning4j带有一个内置的BaseDatasetIteratorEMNIST 实现，
称为EmnistDataSetIterator。这个特殊的迭代器是一个便利实用程序，用于处理数据的下载和准备。
可以创建多个数据迭代器，用于训练模型或者评估模型等。
创建迭代器代码
import org.deeplearning4j.datasets.iterator.impl.EmnistDataSetIterator //引入数据迭代器库 val batchSize = 16 // how many examples to simultaneously train in the network //数据集大小 val emnistSet = EmnistDataSetIterator.Set.BALANCED val emnistTrain = new EmnistDataSetIterator(emnistSet, batchSize, true) //实例化训练迭代器 val emnistTest = new EmnistDataSetIterator(emnistSet, batchSize, false) //实例化评估迭代器
4.建立神经网络
在dl4j中使用的任何与神经网络有关的操作是在NeuralNetConfiguration类中的。可在此处配置超参数和算法的学习方式。
val outputNum = EmnistDataSetIterator.numLabels(emnistSet) // total output classes val rngSeed = 123 // integer for reproducability of a random number generator val numRows = 28 // number of "pixel rows" in an mnist digit val numColumns = 28 val conf = new NeuralNetConfiguration.Builder() .seed(rngSeed) .optimizationAlgo(OptimizationAlgorithm.STOCHASTIC_GRADIENT_DESCENT) .updater(new Adam()) .l2(1e-4) .list() .layer(new DenseLayer.Builder() .nIn(numRows * numColumns) // Number of input datapoints. .nOut(1000) // Number of output datapoints. .activation(Activation.RELU) // Activation function. .weightInit(WeightInit.XAVIER) // Weight initialization. .build()) .layer(new OutputLayer.Builder(LossFunctions.LossFunction.NEGATIVELOGLIKELIHOOD) .nIn(1000) .nOut(outputNum) .activation(Activation.SOFTMAX) .weightInit(WeightInit.XAVIER) .build()) .pretrain(false).backprop(true) .build()
5.训练模型
现在我们已经构建了一个NeuralNetConfiguration，我们可以使用配置来实例化一个MultiLayerNetwork。当我们init()在网络上调用该 方法时，它会在网络上应用所选的权重初始化，并允许我们将数据传递给训练。如果我们想在培训期间看到损失分数，我们也可以将听众传递给网络。
实例化模型有一个fit()接受数据集迭代器（扩展的迭代器BaseDatasetIterator），单个DataSet或ND数组（实现INDArray）的方法。由于我们的EMNIST迭代器已经扩展了迭代器基类，我们可以直接传递它来适应。如果我们想要训练多个时代，DL4J还提供了一个MultipleEpochsIterator可以为我们处理多个时代的类。
// create the MLN val network = new MultiLayerNetwork(conf) network.init() // pass a training listener that reports score every 10 iterations val eachIterations = 5 network.addListeners(new ScoreIterationListener(eachIterations)) // fit a dataset for a single epoch // network.fit(emnistTrain) // fit for multiple epochs // val numEpochs = 2 // network.fit(new MultipleEpochsIterator(numEpochs, emnistTrain))
6.评估模型
Deeplearning4j公开了几种工具来评估模型的性能。您可以执行基本评估并获取精度和准确度等指标，或使用接收器操作特性（ROC）。请注意，通用ROC类适用于二进制分类器，而ROCMultiClass适用于分类器，例如我们在此构建的模型。
A MultiLayerNetwork方便地有一些内置的方法来帮助我们进行评估。您可以将包含测试/验证数据的数据集迭代器传递给evaluate()方法。
// evaluate basic performance val eval = network.evaluate(emnistTest) eval.accuracy() eval.precision() eval.recall() // evaluate ROC and calculate the Area Under Curve val roc = network.evaluateROCMultiClass(emnistTest) roc.calculateAverageAUC() val classIndex = 0 roc.calculateAUC(classIndex) // optionally, you can print all stats from the evaluations print(eval.stats()) print(roc.stats())
// evaluate basic performance val eval = network.evaluate(emnistTest) eval.accuracy() eval.precision() eval.recall() // evaluate ROC and calculate the Area Under Curve val roc = network.evaluateROCMultiClass(emnistTest) roc.calculateAverageAUC() val classIndex = 0 roc.calculateAUC(classIndex) // optionally, you can print all stats from the evaluations print(eval.stats()) print(roc.stats())
加油
