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August 22, 2017 14:42
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| package com.ukc.deeplearning; | |
| /** | |
| * Created by Jon Baker on 20/08/2017. <Part of Socialsense> Copyright University of Kent | |
| */ | |
| 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.eval.Evaluation; | |
| import org.deeplearning4j.nn.api.OptimizationAlgorithm; | |
| import org.deeplearning4j.nn.conf.GradientNormalization; | |
| import org.deeplearning4j.nn.conf.MultiLayerConfiguration; | |
| import org.deeplearning4j.nn.conf.NeuralNetConfiguration; | |
| import org.deeplearning4j.nn.conf.Updater; | |
| import org.deeplearning4j.nn.conf.layers.OutputLayer; | |
| import org.deeplearning4j.nn.conf.layers.RBM; | |
| import org.deeplearning4j.nn.multilayer.MultiLayerNetwork; | |
| import org.deeplearning4j.nn.weights.WeightInit; | |
| import org.deeplearning4j.optimize.listeners.ScoreIterationListener; | |
| import org.nd4j.linalg.activations.Activation; | |
| import org.nd4j.linalg.api.ndarray.INDArray; | |
| import org.nd4j.linalg.dataset.DataSet; | |
| 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.lossfunctions.LossFunctions.LossFunction; | |
| import java.io.File; | |
| import java.io.IOException; | |
| public class DeepLearning { | |
| public static void main(String[] args) throws Exception { | |
| int labelIndex = 0; | |
| int numClasses = 28; | |
| int batchSizeTraining = 2828; //entire training size | |
| DataSet trainingData = readCSVDataset( | |
| "csv/train.csv", | |
| batchSizeTraining, labelIndex, numClasses); | |
| // this is the data we want to classify | |
| int batchSizeTest = 11605; | |
| DataSet testData = readCSVDataset("csv/eval.csv", | |
| batchSizeTest, labelIndex, numClasses); | |
| //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 | |
| trainingData.scale(); | |
| testData.scale(); | |
| //run the model | |
| MultiLayerNetwork model = buildModel(); | |
| model.fit(trainingData); | |
| //evaluate the model on the test set | |
| Evaluation eval = new Evaluation(numClasses); | |
| INDArray output = model.output(testData.getFeatureMatrix()); | |
| eval.eval(testData.getLabels(), output); | |
| System.out.println(eval.stats()); | |
| } | |
| public static MultiLayerNetwork buildModel() { | |
| MultiLayerConfiguration conf = new NeuralNetConfiguration.Builder() | |
| .seed(123) | |
| .miniBatch(false) | |
| .weightInit(WeightInit.RELU) | |
| .iterations(10) | |
| .learningRate(0.2) | |
| .updater(Updater.ADAGRAD).gradientNormalization(GradientNormalization.ClipL2PerLayer) | |
| .regularization(true).l2(1e-1).l1(1e-3) | |
| .optimizationAlgo(OptimizationAlgorithm.LBFGS) | |
| .list() | |
| .layer(0, new RBM.Builder() //RBM is apparently 2-layer (1 visible 1 hidden) | |
| .nIn(19) // Input nodes | |
| .nOut(1024) // Output nodes | |
| //.activation(Activation.RELU) // Activation function type | |
| .weightInit(WeightInit.RELU) // Weight initialization | |
| .visibleUnit(RBM.VisibleUnit.GAUSSIAN) | |
| .hiddenUnit(RBM.HiddenUnit.RECTIFIED) | |
| .build()) | |
| .layer(1, new RBM.Builder() | |
| .nIn(1024) // Input nodes | |
| .nOut(1024) // Output nodes | |
| .activation(Activation.RELU) // Activation function type | |
| .weightInit(WeightInit.RELU) // Weight initialization | |
| .hiddenUnit(RBM.HiddenUnit.RECTIFIED) | |
| .build()) | |
| .layer(2, new OutputLayer.Builder(LossFunction.NEGATIVELOGLIKELIHOOD) | |
| .weightInit(WeightInit.RELU) | |
| .activation(Activation.SOFTMAX).nIn(1024).nOut(28).build()) | |
| .backprop(true).pretrain(false) | |
| .build(); | |
| MultiLayerNetwork net = new MultiLayerNetwork(conf); | |
| net.init(); | |
| net.setListeners(new ScoreIterationListener(10)); | |
| return net; | |
| } | |
| /** | |
| * used for testing and training | |
| * | |
| * @param csvFileClasspath | |
| * @param batchSize | |
| * @param labelIndex | |
| * @param numClasses | |
| * @return | |
| * @throws IOException | |
| * @throws InterruptedException | |
| */ | |
| private static DataSet readCSVDataset( | |
| String csvFileClasspath, int batchSize, int labelIndex, int numClasses) | |
| throws IOException, InterruptedException { | |
| RecordReader rr = new CSVRecordReader(); | |
| rr.initialize(new FileSplit(new File(csvFileClasspath))); | |
| DataSetIterator iterator = new RecordReaderDataSetIterator(rr, batchSize, labelIndex, numClasses); | |
| return iterator.next(); | |
| } | |
| } |
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