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Ich implementiere einen Auto-Encoder für Anomalieerkennung von IoT-Sensordaten. Mein Datensatz stammt aus einer Simulation, aber grundsätzlich sind es Beschleunigungsmesserdaten - drei Dimensionen, eine für jede Achse.DeepLearning4J: Formen stimmen nicht mit FeedForward-Auto-Encoder überein
ich es aus einer CSV-Datei gerade lese, Spalte 2-4 die Daten enthalten, - sorry für die Code-Qualität, ist es schnell und schmutzig:
private static DataSetIterator getTrainingData(int batchSize, Random rand) {
double[] ix = new double[nSamples];
double[] iy = new double[nSamples];
double[] iz = new double[nSamples];
double[] ox = new double[nSamples];
double[] oy = new double[nSamples];
double[] oz = new double[nSamples];
Reader in;
try {
in = new FileReader("/Users/romeokienzler/Downloads/lorenz_healthy.csv");
Iterable<CSVRecord> records;
records = CSVFormat.DEFAULT.parse(in);
int index = 0;
for (CSVRecord record : records) {
String[] recordArray = record.get(0).split(";");
ix[index] = Double.parseDouble(recordArray[1]);
iy[index] = Double.parseDouble(recordArray[2]);
iz[index] = Double.parseDouble(recordArray[3]);
ox[index] = Double.parseDouble(recordArray[1]);
oy[index] = Double.parseDouble(recordArray[2]);
oz[index] = Double.parseDouble(recordArray[3]);
index++;
}
INDArray ixNd = Nd4j.create(ix);
INDArray iyNd = Nd4j.create(iy);
INDArray izNd = Nd4j.create(iz);
INDArray oxNd = Nd4j.create(ox);
INDArray oyNd = Nd4j.create(oy);
INDArray ozNd = Nd4j.create(oz);
INDArray iNd = Nd4j.hstack(ixNd, iyNd, izNd);
INDArray oNd = Nd4j.hstack(oxNd, oyNd, ozNd);
DataSet dataSet = new DataSet(iNd, oNd);
List<DataSet> listDs = dataSet.asList();
Collections.shuffle(listDs, rng);
return new ListDataSetIterator(listDs, batchSize);
} catch (IOException e) {
// TODO Auto-generated catch block
e.printStackTrace();
System.exit(-1);
return null;
}
}
Dies ist das Netz:
public static void main(String[] args) {
// Generate the training data
DataSetIterator iterator = getTrainingData(batchSize, rng);
// Create the network
int numInput = 3;
int numOutputs = 3;
int nHidden = 1;
int listenerFreq = batchSize/5;
MultiLayerConfiguration conf = new NeuralNetConfiguration.Builder().seed(seed)
.gradientNormalization(GradientNormalization.ClipElementWiseAbsoluteValue)
.gradientNormalizationThreshold(1.0).iterations(iterations).momentum(0.5)
.momentumAfter(Collections.singletonMap(3, 0.9))
.optimizationAlgo(OptimizationAlgorithm.CONJUGATE_GRADIENT).list(2)
.layer(0,
new AutoEncoder.Builder().nIn(numInput).nOut(nHidden).weightInit(WeightInit.XAVIER)
.lossFunction(LossFunction.RMSE_XENT).corruptionLevel(0.3).build())
.layer(1, new OutputLayer.Builder(LossFunction.NEGATIVELOGLIKELIHOOD).activation("softmax").nIn(nHidden)
.nOut(numOutputs).build())
.pretrain(true).backprop(false).build();
MultiLayerNetwork model = new MultiLayerNetwork(conf);
model.init();
model.setListeners(Collections.singletonList((IterationListener) new ScoreIterationListener(listenerFreq)));
for (int i = 0; i < nEpochs; i++) {
iterator.reset();
model.fit(iterator);
}
}
ich erhalte die folgende Fehlermeldung: Shapes nicht übereinstimmen: x.shape = [1, 9000], y.shape = [1, 3]
Exception in thread "main" java.lang.IllegalArgumentException: Shapes do not match: x.shape=[1, 9000], y.shape=[1, 3]
at org.nd4j.linalg.api.parallel.tasks.cpu.CPUTaskFactory.getTransformAction(CPUTaskFactory.java:92)
at org.nd4j.linalg.api.ops.executioner.DefaultOpExecutioner.doTransformOp(DefaultOpExecutioner.java:409)
at org.nd4j.linalg.api.ops.executioner.DefaultOpExecutioner.exec(DefaultOpExecutioner.java:62)
at org.nd4j.linalg.api.ndarray.BaseNDArray.subi(BaseNDArray.java:2660)
at org.nd4j.linalg.api.ndarray.BaseNDArray.subi(BaseNDArray.java:2641)
at org.nd4j.linalg.api.ndarray.BaseNDArray.sub(BaseNDArray.java:2419)
at org.deeplearning4j.nn.layers.feedforward.autoencoder.AutoEncoder.computeGradientAndScore(AutoEncoder.java:123)
at org.deeplearning4j.optimize.solvers.BaseOptimizer.gradientAndScore(BaseOptimizer.java:132)
at org.deeplearning4j.optimize.solvers.BaseOptimizer.optimize(BaseOptimizer.java:151)
at org.deeplearning4j.optimize.Solver.optimize(Solver.java:52)
at org.deeplearning4j.nn.layers.BaseLayer.fit(BaseLayer.java:486)
at org.deeplearning4j.nn.multilayer.MultiLayerNetwork.pretrain(MultiLayerNetwork.java:170)
at org.deeplearning4j.nn.multilayer.MultiLayerNetwork.fit(MultiLayerNetwork.java:1134)
at org.deeplearning4j
.examples.feedforward.autoencoder.AnomalyDetector.main (AnomalyDetector.java:136)
Aber ich definiere Dimension nirgendwo und IMHO die Dimensionen von Eingang und Ausgang sollte (3,3000) und (3,3000 sein). Wo ist mein Fehler?
viel im Voraus Danke ...
EDIT: UPDATE neueste Version 13.9.16 ich die gleichen Fehler (semantisch), hier ist das, was ich jetzt tue:
private static DataSetIterator getTrainingData(int batchSize, Random rand) {
double[] ix = new double[nSamples];
double[] iy = new double[nSamples];
double[] iz = new double[nSamples];
double[] ox = new double[nSamples];
double[] oy = new double[nSamples];
double[] oz = new double[nSamples];
try {
RandomAccessFile in = new RandomAccessFile(new File("/Users/romeokienzler/Downloads/lorenz_healthy.csv"),
"r");
int index = 0;
String record;
while ((record = in.readLine()) != null) {
String[] recordArray = record.split(";");
ix[index] = Double.parseDouble(recordArray[1]);
iy[index] = Double.parseDouble(recordArray[2]);
iz[index] = Double.parseDouble(recordArray[3]);
ox[index] = Double.parseDouble(recordArray[1]);
oy[index] = Double.parseDouble(recordArray[2]);
oz[index] = Double.parseDouble(recordArray[3]);
index++;
}
INDArray ixNd = Nd4j.create(ix);
INDArray iyNd = Nd4j.create(iy);
INDArray izNd = Nd4j.create(iz);
INDArray oxNd = Nd4j.create(ox);
INDArray oyNd = Nd4j.create(oy);
INDArray ozNd = Nd4j.create(oz);
INDArray iNd = Nd4j.hstack(ixNd, iyNd, izNd);
INDArray oNd = Nd4j.hstack(oxNd, oyNd, ozNd);
DataSet dataSet = new DataSet(iNd, oNd);
List<DataSet> listDs = dataSet.asList();
Collections.shuffle(listDs, rng);
return new ListDataSetIterator(listDs, batchSize);
} catch (IOException e) {
// TODO Auto-generated catch block
e.printStackTrace();
System.exit(-1);
return null;
}
}
Und hier das Netz:
// Set up network. 784 in/out (as MNIST images are 28x28).
// 784 -> 250 -> 10 -> 250 -> 784
MultiLayerConfiguration conf = new NeuralNetConfiguration.Builder().seed(12345).iterations(1)
.weightInit(WeightInit.XAVIER).updater(Updater.ADAGRAD).activation("relu")
.optimizationAlgo(OptimizationAlgorithm.STOCHASTIC_GRADIENT_DESCENT).learningRate(learningRate)
.regularization(true).l2(0.0001).list().layer(0, new DenseLayer.Builder().nIn(3).nOut(1).build())
.layer(1, new OutputLayer.Builder().nIn(1).nOut(3).lossFunction(LossFunctions.LossFunction.MSE).build())
.pretrain(false).backprop(true).build();
MultiLayerNetwork net = new MultiLayerNetwork(conf);
net.setListeners(Collections.singletonList((IterationListener) new ScoreIterationListener(1)));
// Load data and split into training and testing sets. 40000 train,
// 10000 test
DataSetIterator iter = getTrainingData(batchSize, rng);
// Train model:
int nEpochs = 30;
while (iter.hasNext()) {
DataSet ds = iter.next();
for (int epoch = 0; epoch < nEpochs; epoch++) {
net.fit(ds.getFeatures(), ds.getLabels());
System.out.println("Epoch " + epoch + " complete");
}
}
Mein Fehler ist:
Exception in thread "main" java.lang.IllegalStateException: Mis matched lengths: [9000] != [3]
at org.nd4j.linalg.util.LinAlgExceptions.assertSameLength(LinAlgExceptions.java:39)
at org.nd4j.linalg.api.ndarray.BaseNDArray.subi(BaseNDArray.java:2786)
at org.nd4j.linalg.api.ndarray.BaseNDArray.subi(BaseNDArray.java:2767)
at org.nd4j.linalg.api.ndarray.BaseNDArray.sub(BaseNDArray.java:2547)
at org.deeplearning4j.nn.layers.BaseOutputLayer.getGradientsAndDelta(BaseOutputLayer.java:182)
at org.deeplearning4j.nn.layers.BaseOutputLayer.backpropGradient(BaseOutputLayer.java:161)
at org.deeplearning4j.nn.multilayer.MultiLayerNetwork.calcBackpropGradients(MultiLayerNetwork.java:1125)
at org.deeplearning4j.nn.multilayer.MultiLayerNetwork.backprop(MultiLayerNetwork.java:1077)
at org.deeplearning4j.nn.multilayer.MultiLayerNetwork.computeGradientAndScore(MultiLayerNetwork.java:1817)
at org.deeplearning4j.optimize.solvers.BaseOptimizer.gradientAndScore(BaseOptimizer.java:152)
at org.deeplearning4j.optimize.solvers.StochasticGradientDescent.optimize(StochasticGradientDescent.java:54)
at org.deeplearning4j.optimize.Solver.optimize(Solver.java:51)
at org.deeplearning4j.nn.multilayer.MultiLayerNetwork.fit(MultiLayerNetwork.java:1445)
at org.deeplearning4j.examples.feedforward.anomalydetection.IoTAnomalyExample.main(IoTAnomalyExample.java:110)
Ich bin mir ziemlich sicher, dass ich mit den Trainingsdaten herumhantiere - die Form der Trainingsdaten ist 3000 Zeilen, 3 Spalten - das selbe für das Ziel (die gleichen Daten, weil ich einen Autoencoder bauen möchte) - Testdaten finden Sie hier: https://pmqsimulator-romeokienzler-2310.mybluemix.net/data
Irgendwelche Ideen?
Zunächst einmal nicht sicher, warum Sie laufen rc3.8. Wir haben das Java-Zeug im Januar losgeworden ... Wir laufen jetzt mit Strom (und haben seit Monaten) Versuchen Sie, zuerst zu aktualisieren. Es ist schwer für uns, eine Version zu rechtfertigen, die so alt ist. –