First, we need to create a new DataSetIterator to process the data. The parameters for the MnistDataSetIterator constructor are the batch size, 1000 in this case, and the total number of samples to process. We then get our next dataset, shuffle the data to randomize, and split our data to be tested and trained. As we discussed earlier in the chapter, we typically use 65% of the data to train the data and the remaining 35% is used for testing:
DataSetIterator iter = new MnistDataSetIterator(1000, MnistDataFetcher.NUM_EXAMPLES); DataSet dataset = iter.next(); dataset.shuffle(); SplitTestAndTrain testAndTrain = dataset.splitTestAndTrain(0.65); DataSet trainingData = testAndTrain.getTrain(); DataSet testData = testAndTrain.getTest(); ...
