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="/POM/4.0.0" xmlns:xsi="/2001/XMLSchema-instance"
xsi:schemaLocation="/POM/4.0.0 /xsd/maven-4.0.0.xsd">
<modelVersion>4.0.0</modelVersion>
<groupId>YOURPROJECTNAME</groupId>
<artifactId>YOURPROJECTNAME</artifactId>
<version>1.0-SNAPSHOT</version>
<packaging>jar</packaging>
<name>YOURNAME</name>
<url></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>${dl4j.version}</version>
</dependency>
<!-- deeplearning4j-ui⽤于HistogramIterationListener + 可视化：参见/cn/visualization -->
<dependency>
<groupId>org.deeplearning4j</groupId>
<artifactId>deeplearning4j-ui</artifactId>
<version>${dl4j.version}</version>
</dependency>
<!-- 强制指定使⽤UI/HistogramIterationListener时的guava版本 -->
<dependency>
<groupId&le.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 ultilayer._
import aph._
import f._
import f.inputs._
import f.layers._
import weights._
import org.deeplearning4j.optimize.listeners._
import org.deeplearning4j.datasets.datavec.RecordReaderMultiDataSetIterator
import org.deeplearning4j.eval.Evaluation
import org.nd4j.fig._ // 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()
maven下载教程.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()
// 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()
// 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())

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