1. Create an array to assign weights to minor labels:

INDArray weightsArray = Nd4j.create(new double[]{0.35, 0.65});

2. Modify OutPutLayer to evenly balance the labels in the dataset:

new OutputLayer.Builder(new LossMCXENT(weightsArray)).nIn(incomingConnectionCount).nOut(labelCount).activation(Activation.SOFTMAX))
.build();

3. Initialize the neural network and add the training listeners:

MultiLayerConfiguration configuration = builder.build();
   MultiLayerNetwork multiLayerNetwork = new MultiLayerNetwork(configuration);
 multiLayerNetwork.init();
 multiLayerNetwork.setListeners(new ScoreIterationListener(iterationCount));

4. Add the DL4J UI Maven dependency to analyze the training process:

<dependency>
 <groupId>org.deeplearning4j</groupId>
 <artifactId>deeplearning4j-ui_2.10</artifactId>
 <version>1.0.0-beta3</version>
 </dependency>

5. Start the UI server and add temporary storage to store the model information:

UIServer uiServer = UIServer.getInstance();
 StatsStorage statsStorage = new InMemoryStatsStorage();

Replace InMemoryStatsStorage with FileStatsStorage (in case of memory restrictions):

multiLayerNetwork.setListeners(new ScoreIterationListener(100),
 new StatsListener(statsStorage));

6. Assign the temporary storage space to the UI server:

uiServer.attach(statsStorage);

7. Train the neural network by calling fit():

multiLayerNetwork.fit(dataSetIteratorSplitter.getTrainIterator(),100);

8. Evaluate the model by calling evaluate():

Evaluation evaluation = multiLayerNetwork.evaluate(dataSetIteratorSplitter.getTestIterator(),Arrays.asList("0","1"));
 System.out.println(evaluation.stats()); //printing the evaluation metrics