- Create a SequenceRecordReader instance to extract and load features from the time series data:
SequenceRecordReader trainFeaturesSequenceReader = new CSVSequenceRecordReader();
trainFeaturesSequenceReader.initialize(new NumberedFileInputSplit(new File(trainfeatureDir).getAbsolutePath()+"/%d.csv",0,449));
- Create a SequenceRecordReader instance to extract and load labels from the time series data:
SequenceRecordReader trainLabelsSequenceReader = new CSVSequenceRecordReader();
trainLabelsSequenceReader.initialize(new NumberedFileInputSplit(new File(trainlabelDir).getAbsolutePath()+"/%d.csv",0,449));
- Create sequence readers for testing and evaluation:
SequenceRecordReader testFeaturesSequenceReader = new CSVSequenceRecordReader();
testFeaturesSequenceReader.initialize(new NumberedFileInputSplit(new File(testfeatureDir).getAbsolutePath()+"/%d.csv",0,149));
SequenceRecordReader testLabelsSequenceReader = new CSVSequenceRecordReader();
testLabelsSequenceReader.initialize(new NumberedFileInputSplit(new File(testlabelDir).getAbsolutePath()+"/%d.csv",0,149));|
- Use SequenceRecordReaderDataSetIterator to feed the data into our neural network:
DataSetIterator trainIterator = new SequenceRecordReaderDataSetIterator(trainFeaturesSequenceReader,trainLabelsSequenceReader,batchSize,numOfClasses);
DataSetIterator testIterator = new SequenceRecordReaderDataSetIterator(testFeaturesSequenceReader,testLabelsSequenceReader,batchSize,numOfClasses);
- Rewrite the train/test iterator (with AlignmentMode) to support time series of varying lengths:
DataSetIterator trainIterator = new SequenceRecordReaderDataSetIterator(trainFeaturesSequenceReader,trainLabelsSequenceReader,batchSize,numOfClasses,false, SequenceRecordReaderDataSetIterator.AlignmentMode.ALIGN_END);