ND4J provides the CSVRecordReader class, which is useful for reading CSV data. It has three overloaded constructors. The one we will demonstrate is passed two arguments. The first is the number of lines to skip when first reading a file and the second is a string holding the delimiters used to parse the text.

In the following code, we create a new instance of the class, where we do not skip any lines and use only a comma for a delimiter:

RecordReader recordReader = new CSVRecordReader(0, ","); 

The class implements the RecordReader interface. It has an initialize method that is passed an instance of the FileSplit class. One of its constructors is passed an instance of a File object that references a dataset. The FileSplit class assists in splitting the data for training and testing. In this example, we initialize the reader for a file called car.txt that we will use in the Preparing the data section:

recordReader.initialize(new FileSplit(new File("car.txt"))); 

To process the data, we need an iterator such as the DataSetIterator instance shown next. This class possesses a multitude of overloaded constructors. In the following example, the first argument is the RecordReader instance. This is followed by three arguments. The first is the batch size, which is the number of records to retrieve at a time. The next one is the index of the last attribute of the record. The last argument is the number of classes represented by the dataset:

DataSetIterator iterator =  
    new RecordReaderDataSetIterator(recordReader, 1728, 6, 4); 

The file's record's last attribute will hold a class value if we use a dataset for regression. This is precisely how we will use it later. The number of the class's parameter is only used with regression.

In the next code sequence, we will split the dataset into two sets: one for training and one for testing. Starting with the next method, this method returns the next dataset from the source. The size of the dataset is dependent on the batch size used earlier. The shuffle method randomizes the input while the splitTestAndTrain method returns an instance of the SplitTestAndTrain class, which we use to get the training and testing datasets. The splitTestAndTrain method's argument specifies the percentage of the data to be used for training.

DataSet dataset = iterator.next(); 
dataset.shuffle(); 
SplitTestAndTrain testAndTrain = dataset.splitTestAndTrain(0.65); 
DataSet trainingData = testAndTrain.getTrain(); 
DataSet testData = testAndTrain.getTest(); 

We can then use these datasets with a model.

Builder classes are common in DL4J. In the previous example, the NeuralNetConfiguration.Builder class is used. The methods used here are but a few of the many that are available. In the following table, we describe several of them:

Let's examine how a layer is created more closely. In the example, the list method returns a NeuralNetConfiguration.ListBuilder instance. Its layer method takes two arguments. The first is the number of the layer, a zero-based numbering scheme. The second is the Layer instance.

There are two different layers used here with two different builders: a DenseLayer.Builder and an OutputLayer.Builder instance. There are several types of layers available in DL4J. The argument of a builder's constructor may be a loss function, as is the case with the output layer, and is explained next.

In a feedback network, the neural network's guess is compared to what is called the ground truth, which is the error. This error is used to update the network through the modification of weights and biases. The loss function, also called an objective or cost function, measures the difference.

There are several loss functions supported by DL4J:

The remaining methods used with the builder instance are the activation function, the number of inputs and outputs for the layer, and the build method, which creates the layer.

Each layer of a multi-layer network requires the following:

There are many different types of activation functions, each of which can address a particular type of problem.

The activation function is used to determine whether the neuron fires. There are several functions supported, including relu (rectified linear), tanh, sigmoid, softmax, hardtanh, leakyrelu, maxout, softsign, and softplus.

Next, a MultiLayerNetwork instance is created using the defined configuration. The model is initialized, and its listeners are set. The ScoreIterationListener instance will display information as the model trains, which we will see shortly. Its constructor's argument specifies how often that information should be displayed:

MultiLayerNetwork model = new MultiLayerNetwork(conf); 
model.init(); 
model.setListeners(new ScoreIterationListener(100)); 

We are now ready to train the model.