# Incorrect regression output data **URL:** \ **Category:** DL4J\ **Created:** [September 19, 2024, 3:16am UTC](https://community.konduit.ai/t/incorrect-regression-output-data/3252 "2024-09-19T03:16:26Z")\ **Posts on this page:** 5\ **Page:** 1
**Author:** ![st331man](https://avatars.discourse-cdn.com/v4/letter/s/35a633/32.png) [@st331man](https://community.konduit.ai/u/st331man)\ **Post date:** [September 19, 2024, 3:16am UTC](https://community.konduit.ai/t/incorrect-regression-output-data/3252/1 "2024-09-19T03:16:27Z")
Hello! Please tell me what the problem might be. I have a recurrent network for regression based on a sequence of 100 time steps. An example of one step in the sequence: `[98.99920226267315, 98.74175067082459, 99.00645442019002, 98.69461164696499, 183762.0]` The network should predict the next time step, so I specify one step of the sequence as the label. After applying the revert with the normalizer ```auto INDArray timeSeriesOutput = net.output(finalData); normalizer.revertFeatures(timeSeriesOutput); normalizer.revertLabels(timeSeriesOutput); ``` I get various data like this: `[827.3333, 882.7206, 846.4208, 873.9750, 3.9037e6]` Which doesn’t even closely resemble the input of the 4 columns around the number 100. Moreover, depending on the number of epochs, different numbers are always returned, sometimes exceeding the predicted values by several times. I have tried various configurations of the output layer, but the output data has never even been close to 100. Can you suggest what I might be doing wrong? ---
**Author:** ![agibsonccc](https://yyz1.discourse-cdn.com/flex035/user_avatar/community.konduit.ai/agibsonccc/32/697_2.png) [@agibsonccc](https://community.konduit.ai/u/agibsonccc)\ **Post date:** [September 19, 2024, 4:04am UTC](https://community.konduit.ai/t/incorrect-regression-output-data/3252/2 "2024-09-19T04:04:18Z")
@st331man yeah those numbers look weird. Can you setup a standalone example that’s easier to look at? That usually indicates that the internal distribution statistics are off somehow. It also could mean your network wasn’t very accurate though. ---
**Author:** ![st331man](https://avatars.discourse-cdn.com/v4/letter/s/35a633/32.png) [@st331man](https://community.konduit.ai/u/st331man)\ **Post date:** [September 19, 2024, 5:28pm UTC](https://community.konduit.ai/t/incorrect-regression-output-data/3252/3 "2024-09-19T17:28:21Z")
@agibsonccc My code looks like this: ```auto public class RegressionExperimental_ex { private static final String NEURAL_NAME = "data-d86660f4"; private static final String DATASET_NAME = "/regression-datasets/" + NEURAL_NAME; public static File datasetName = new File(Configs.baseDir, DATASET_NAME + File.separator); private static File featuresDir = new File(datasetName, "features"); private static File labelsDir = new File(datasetName, "labels"); private static final int EPOCHS = 15; public static void main(String[] args) throws Exception { var files = Files.list(featuresDir.toPath()).collect(Collectors.toList()); int filesSize = files.size(); int eightyPrecents = (int) (filesSize * 0.80); //Train data SequenceRecordReader trainFeatures = new CSVSequenceRecordReader(0, ";"); trainFeatures.initialize(new NumberedFileInputSplit(featuresDir.getAbsolutePath() + "/%d.csv", 1, eightyPrecents)); SequenceRecordReader trainLabels = new CSVSequenceRecordReader(0, ";"); trainLabels.initialize(new NumberedFileInputSplit(labelsDir.getAbsolutePath() + "/%d.csv", 1, eightyPrecents)); //17999 boolean regression = true; int miniBatchSize = 30; int numLabelClasses = 1; SequenceRecordReaderDataSetIterator trainData = new SequenceRecordReaderDataSetIterator(trainFeatures, trainLabels, miniBatchSize, numLabelClasses, regression, SequenceRecordReaderDataSetIterator.AlignmentMode.ALIGN_END); //Normalize data NormalizerStandardize normalizer = new NormalizerStandardize(); normalizer.fit(trainData); trainData.setPreProcessor(normalizer); trainData.reset(); SequenceRecordReader testFeatures = new CSVSequenceRecordReader(0, ";"); testFeatures.initialize(new NumberedFileInputSplit(featuresDir.getAbsolutePath() + "/%d.csv", eightyPrecents + 1, filesSize - 1)); //18000 - 21640 SequenceRecordReader testLabels = new CSVSequenceRecordReader(0, ";"); testLabels.initialize(new NumberedFileInputSplit(labelsDir.getAbsolutePath() + "/%d.csv", eightyPrecents + 1, filesSize - 1)); SequenceRecordReaderDataSetIterator testData = new SequenceRecordReaderDataSetIterator(testFeatures, testLabels, miniBatchSize, numLabelClasses, regression, SequenceRecordReaderDataSetIterator.AlignmentMode.ALIGN_END); testData.setPreProcessor(normalizer); // -- Final eval -- SequenceRecordReader finalFeatures = new CSVSequenceRecordReader(0, ";"); finalFeatures.initialize(new NumberedFileInputSplit(featuresDir.getAbsolutePath() + "/%d.csv", filesSize, filesSize)); SequenceRecordReader finalLabels = new CSVSequenceRecordReader(0, ";"); finalLabels.initialize(new NumberedFileInputSplit(labelsDir.getAbsolutePath() + "/%d.csv", filesSize, filesSize)); SequenceRecordReaderDataSetIterator finalData = new SequenceRecordReaderDataSetIterator(finalFeatures, finalLabels, miniBatchSize, numLabelClasses, regression, SequenceRecordReaderDataSetIterator.AlignmentMode.ALIGN_END); finalData.setPreProcessor(normalizer); MultiLayerConfiguration conf = new NeuralNetConfiguration.Builder() .seed(123) .weightInit(WeightInit.XAVIER) .updater(new Nadam()) .list() .layer(new LSTM.Builder() .activation(Activation.TANH) .nIn(5) .nOut(30) .build()) .layer(new LSTM.Builder() .activation(Activation.TANH) .nIn(30) .nOut(14) .build()) .layer(new RnnOutputLayer.Builder(LossFunctions.LossFunction.MSE) .activation(Activation.IDENTITY) .nIn(30) .nOut(5) .build()) .build(); MultiLayerNetwork net = new MultiLayerNetwork(conf); net.init(); System.out.printf("Started: miniBatchSize: %s, epohs: %s %n", miniBatchSize, EPOCHS); for (int i = 0; i < EPOCHS; i++) { long time = System.nanoTime(); net.fit(trainData); var eval = net.evaluateRegression(testData); System.out.println(eval); System.out.println(eval.stats()); time = System.nanoTime() - time; System.out.println(DATASET_NAME + " Time for epoch № " + (i + 1) + ": " + SECONDS.convert(time, TimeUnit.NANOSECONDS) + " sec"); } System.out.println("Final:"); RegressionEvaluation regEval = net.evaluateRegression(trainData); System.out.println(regEval); System.out.println("---------->"); INDArray timeSeriesOutput = net.output(finalData); normalizer.revertFeatures(timeSeriesOutput); normalizer.revertLabels(timeSeriesOutput); System.out.println(timeSeriesOutput); long timeSeriesLength = timeSeriesOutput.size(2); INDArray lastTimeStepProbabilities = timeSeriesOutput.get(NDArrayIndex.point(0), NDArrayIndex.all(), NDArrayIndex.point(timeSeriesLength - 1)); System.out.println("Result: " + lastTimeStepProbabilities); double[] results = lastTimeStepProbabilities.toDoubleVector(); System.out.println("Double arr: " + Arrays.toString(results)); System.out.println("== End =="); } ``` And my dataset: [data-d86660f4.zip - Google Drive](https://drive.google.com/file/d/1EDQHrYm2hXi11AQfChNgfjoeN8cF9mWC/view?usp=drive_link) ---
**Author:** ![agibsonccc](https://yyz1.discourse-cdn.com/flex035/user_avatar/community.konduit.ai/agibsonccc/32/697_2.png) [@agibsonccc](https://community.konduit.ai/u/agibsonccc)\ **Post date:** [September 27, 2024, 11:49am UTC](https://community.konduit.ai/t/incorrect-regression-output-data/3252/4 "2024-09-27T11:49:34Z")
@st331man sorry pleas e ping me if I don’t reply. I get busy. Looking at this again, ensure that you set preprocessing the labels to true. Label normalization is important. Add this: myNormalizer.fitLabel(true); We don’t do that by default due to classifiers not normally needing that. ---
**Author:** ![st331man](https://avatars.discourse-cdn.com/v4/letter/s/35a633/32.png) [@st331man](https://community.konduit.ai/u/st331man)\ **Post date:** [September 28, 2024, 5:41am UTC](https://community.konduit.ai/t/incorrect-regression-output-data/3252/5 "2024-09-28T05:41:17Z")
@agibsonccc Thank you for the hint; I didn’t know that I had to do it this way. Nevertheless, the problem turned out to be something else. Apparently, the issue lies in the fact that I am incorrectly inputting data into the neural network, or I am doing it with an incorrect number of dimensions. Each sequence is in a separate file, and each label is also in a separate file. Currently, I am using only one time step in the label, but in the future, I want to enable forecasting for several steps ahead. It seems that I am somehow incorrectly feeding the data into the iterator, resulting in all the data mixing together, and ultimately yielding incorrect results. I noticed that the output somehow ends up containing identical data. For some reason, the network also outputs a sequence that contains repeated data, while I only need the label. ![image](https://canada1.discourse-cdn.com/flex035/uploads/konduit/original/2X/c/c9d2e3304cc20d07484f8ef82a912add7341fc7f.png)