diff --git a/src/main/java/de/lluni/javann/examples/ExampleSine.java b/src/main/java/de/lluni/javann/examples/ExampleSine.java
index 52edae9..3dbddfb 100644
--- a/src/main/java/de/lluni/javann/examples/ExampleSine.java
+++ b/src/main/java/de/lluni/javann/examples/ExampleSine.java
@@ -18,19 +18,26 @@ public class ExampleSine {
     private static final int TEST_SIZE = 1000;
 
     public static void main(String[] args) {
+        // training set
         SimpleMatrix[] X_train = new SimpleMatrix[TRAINING_SIZE];
         SimpleMatrix[] y_train = new SimpleMatrix[TRAINING_SIZE];
+        // test set
+        SimpleMatrix[] X_test = new SimpleMatrix[TEST_SIZE];
+        SimpleMatrix[] y_test = new SimpleMatrix[TEST_SIZE];
+        // identical test set for plotting
         double[] X_test_linspace = Utilities.linspace(0, 2 * Math.PI, TEST_SIZE);
         double[] y_test_true = new double[TEST_SIZE];
         double[] y_test_pred = new double[TEST_SIZE];
-        SimpleMatrix[] X_test = new SimpleMatrix[TEST_SIZE];
-        SimpleMatrix[] y_test = new SimpleMatrix[TEST_SIZE];
+
         Random random = new Random();
+
+        // generate training set from random data
         for (int i = 0; i < TRAINING_SIZE; i++) {
             double temp = random.nextDouble(0, 2 * Math.PI);
             X_train[i] = new SimpleMatrix(new double[][]{{temp}});
             y_train[i] = new SimpleMatrix(new double[][]{{Math.sin(temp)}});
         }
+        // generate test set
         for (int i = 0; i < TEST_SIZE; i++) {
             X_test[i] = new SimpleMatrix(new double[][]{{X_test_linspace[i]}});
             y_test[i] = new SimpleMatrix(new double[][]{{Math.sin(X_test_linspace[i])}});
@@ -45,9 +52,13 @@ public class ExampleSine {
         network.addLayer(new ActivationLayer(ActivationFunctions::LeakyReLu, ActivationFunctions::LeakyReLuPrime));
         network.addLayer(new FCLayer(1));
 
+        // configure loss function for the network
         network.use(LossFunctions::MSE, LossFunctions::MSEPrime);
+
+        // train network on X_train and y_train
         network.fit(X_train, y_train, 100, 0.05d);
 
+        // predict X_test and output results to console
         SimpleMatrix[] output = network.predict(X_test);
         for (int i = 0; i < output.length; i++) {
             y_test_pred[i] = output[i].get(0);
@@ -58,6 +69,7 @@ public class ExampleSine {
             System.out.println();
         }
 
+        // create and display chart
         XYChart chart = new XYChartBuilder().title("sin(x) predictions").xAxisTitle("x").yAxisTitle("y").build();
         chart.addSeries("sin(x) true", X_test_linspace, y_test_true);
         chart.addSeries("sin(x) predictions", X_test_linspace, y_test_pred);