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Added comments to ExampleSine

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lluni 2022-05-25 19:48:10 +02:00
parent 74e4d05fa1
commit 1b296bb5d0
1 changed files with 14 additions and 2 deletions
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src/main/java/de/lluni/javann/examples/ExampleSine.java
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@ -18,19 +18,26 @@ public class ExampleSine {
private static final int TEST_SIZE = 1000; private static final int TEST_SIZE = 1000;
public static void main(String[] args) { public static void main(String[] args) {
// training set
SimpleMatrix[] X_train = new SimpleMatrix[TRAINING_SIZE]; SimpleMatrix[] X_train = new SimpleMatrix[TRAINING_SIZE];
SimpleMatrix[] y_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[] X_test_linspace = Utilities.linspace(0, 2 * Math.PI, TEST_SIZE);
double[] y_test_true = new double[TEST_SIZE]; double[] y_test_true = new double[TEST_SIZE];
double[] y_test_pred = 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(); Random random = new Random();
// generate training set from random data
for (int i = 0; i < TRAINING_SIZE; i++) { for (int i = 0; i < TRAINING_SIZE; i++) {
double temp = random.nextDouble(0, 2 * Math.PI); double temp = random.nextDouble(0, 2 * Math.PI);
X_train[i] = new SimpleMatrix(new double[][]{{temp}}); X_train[i] = new SimpleMatrix(new double[][]{{temp}});
y_train[i] = new SimpleMatrix(new double[][]{{Math.sin(temp)}}); y_train[i] = new SimpleMatrix(new double[][]{{Math.sin(temp)}});
} }
// generate test set
for (int i = 0; i < TEST_SIZE; i++) { for (int i = 0; i < TEST_SIZE; i++) {
X_test[i] = new SimpleMatrix(new double[][]{{X_test_linspace[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])}}); 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 ActivationLayer(ActivationFunctions::LeakyReLu, ActivationFunctions::LeakyReLuPrime));
network.addLayer(new FCLayer(1)); network.addLayer(new FCLayer(1));
// configure loss function for the network
network.use(LossFunctions::MSE, LossFunctions::MSEPrime); network.use(LossFunctions::MSE, LossFunctions::MSEPrime);
// train network on X_train and y_train
network.fit(X_train, y_train, 100, 0.05d); network.fit(X_train, y_train, 100, 0.05d);
// predict X_test and output results to console
SimpleMatrix[] output = network.predict(X_test); SimpleMatrix[] output = network.predict(X_test);
for (int i = 0; i < output.length; i++) { for (int i = 0; i < output.length; i++) {
y_test_pred[i] = output[i].get(0); y_test_pred[i] = output[i].get(0);
@ -58,6 +69,7 @@ public class ExampleSine {
System.out.println(); System.out.println();
} }
// create and display chart
XYChart chart = new XYChartBuilder().title("sin(x) predictions").xAxisTitle("x").yAxisTitle("y").build(); 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) true", X_test_linspace, y_test_true);
chart.addSeries("sin(x) predictions", X_test_linspace, y_test_pred); chart.addSeries("sin(x) predictions", X_test_linspace, y_test_pred);