I am trying a time prediction model in deeplearning4j for text processing which takes no of words,sentences,char as input features and produces time as output.But while modelling input data to output i am having difficulties to transform these values and how to tell the network for these values of input these are respective output values.
Also should i reduce dimensionality from just having x1 and y.instead of x1-x4?
training-data.csv has the below columns with 100 values. x1,x2,x3,x4(inputs) y(output)
I tried using SequenceRecorder and Iterator which can capture variant inputs. below is my code
public static void main(String[] args) throws Exception
{
// Initlizing parametres
final Logger log = LoggerFactory.getLogger(MainExpert.class);
final int seed =123;
final int numInput = 4;
final int numOutput = 1;
final int numHidden = 20;
final double learningRate = 0.015;
final int batchSize =30;
final int nEpochs =30;
//final int inputFeatures =4;
//Constructing Training data
final File baseFolder =new File("/home/aj/my/samples/corpus");
final File testFolder = new File("/home/aj/my/samples/corpus/train_data_0.csv");
SequenceRecordReader trainReader = new CSVSequenceRecordReader(0,",");
trainReader.initialize(new NumberedFileInputSplit(baseFolder.getAbsolutePath() + "/train_data_%d.csv",0,0));
DataSetIterator trainIterator = new SequenceRecordReaderDataSetIterator(trainReader,batchSize,-1,4,true);
SequenceRecordReader testReader = new CSVSequenceRecordReader(0,",");
testReader.initialize(new NumberedFileInputSplit(baseFolder.getAbsolutePath() + "/test_data_%d.csv",0,0));
DataSetIterator testIterator = new SequenceRecordReaderDataSetIterator(testReader,batchSize,-1,4,true);
DataSet trainData = trainIterator.next();
System.out.println(trainData);
DataSet testData = testIterator.next();
NormalizerMinMaxScaler normalizer = new NormalizerMinMaxScaler(0, 1);
normalizer.fitLabel(true);
normalizer.fit(trainData);
normalizer.transform(trainData);
normalizer.transform(testData);
//Configuring Network
log.info("Building Model");
MultiLayerConfiguration config = new NeuralNetConfiguration.Builder()
.seed(seed)
.iterations(1)
.optimizationAlgo(OptimizationAlgorithm.STOCHASTIC_GRADIENT_DESCENT)
.learningRate(learningRate)
.updater(Updater.NESTEROVS).momentum(0.9)
.list()
.layer(0, new DenseLayer.Builder()
.nIn(numInput)
.nOut(numHidden)
.weightInit(WeightInit.XAVIER).
activation(Activation.RELU)
.build())
.layer(1, new DenseLayer.Builder()
.nIn(numHidden)
.nOut(numHidden)
.weightInit(WeightInit.XAVIER)
.activation(Activation.RELU)
.build())
.layer(2, new OutputLayer.Builder(LossFunction.MSE)
.nIn(numHidden)
.nOut(numOutput)
.weightInit(WeightInit.XAVIER)
.activation(Activation.IDENTITY)
.build())
.pretrain(false).backprop(true).build();
//Initializing network
log.info("initlizing model");
MultiLayerNetwork model = new MultiLayerNetwork(config);
model.init();
model.setListeners(new ScoreIterationListener(1));
log.info("Training Model");
for(int i=0;i<nEpochs;i++)
{
model.fit(trainData);
}
//Evaluation
RegressionEvaluation reval=new RegressionEvaluation(1);
while(testIterator.hasNext())
{
INDArray feat =testData.getFeatureMatrix();
INDArray labels =testData.getLabels();
INDArray prediction =model.output(feat);
reval.eval(labels, prediction);
}
System.out.println(reval.stats());
}
}
my data has four input values and one output values.
But i get an exception
org.deeplearning4j.exception.DL4JInvalidInputException: Input that is not a matrix; expected matrix (rank 2), got rank 3 array with shape [1, 4, 107]
