Java Deep Learning Cookbook

How to do it...

1. Create iterators for the MNIST data using MnistDataSetIterator:

DataSetIterator iter = new MnistDataSetIterator(miniBatchSize,numOfExamples,binarize);

2. Use SplitTestAndTrain to split the base iterator into train/test iterators:

DataSet ds = iter.next(); SplitTestAndTrain split = ds.splitTestAndTrain(numHoldOut, new Random(12345));

3. Create lists to store the feature sets from the train/test iterators:

List<INDArray> featuresTrain = new ArrayList<>(); List<INDArray> featuresTest = new ArrayList<>(); List<INDArray> labelsTest = new ArrayList<>();

4. Populate the values into the feature/label lists that were previously created:

featuresTrain.add(split.getTrain().getFeatures()); DataSet dsTest = split.getTest(); featuresTest.add(dsTest.getFeatures()); ...