Tests zum Laden und SPeichern

src/test/java/schule/ngb/zm/ml/NeuralNetworkTest.java

@@ -1,22 +1,103 @@
 package schule.ngb.zm.ml;
 
+import org.junit.jupiter.api.BeforeAll;
 import org.junit.jupiter.api.Test;
 import schule.ngb.zm.util.Log;
 
+import java.io.File;
 import java.util.ArrayList;
 import java.util.List;
 import java.util.Random;
 
+import static org.junit.jupiter.api.Assertions.*;
+
 class NeuralNetworkTest {
 
+	@BeforeAll
+	static void enableDebugging() {
+		Log.enableGlobalDebugging();
+	}
+
+	@Test
+	void readWrite() {
+		// XOR Dataset
+		NeuralNetwork net = new NeuralNetwork(2, 4, 1);
+		double[][] inputs = new double[][]{
+			{0, 0}, {0, 1}, {1, 0}, {1, 1}
+		};
+		double[][] outputs = new double[][]{
+			{0}, {1}, {1}, {0}
+		};
+
+		System.out.println("Training the neural net to learn XOR...");
+		net.train(inputs, outputs, 10000);
+		System.out.println("    finished training");
+
+		NeuralNetwork.saveToFile("./ml-test.txt", net);
+		assertTrue(new File("./ml-test.txt").isFile());
+
+		NeuralNetwork net2 = NeuralNetwork.loadFromFile("./ml-test.txt");
+		assertEquals(net.getLayerCount(), net2.getLayerCount());
+		for( int l = 0; l < net2.getLayerCount(); l++ ) {
+			NeuronLayer layer = net.getLayer(l+1);
+			NeuronLayer layer2 = net2.getLayer(l+1);
+
+			for( int i = 0; i < layer.getInputCount(); i++ ) {
+				for( int j = 0; j < layer.getNeuronCount(); j++ ) {
+					assertEquals(layer.weights.coefficients[i][j], layer2.weights.coefficients[i][j]);
+				}
+			}
+			for( int j = 0; j < layer.getNeuronCount(); j++ ) {
+				assertEquals(layer.biases[j], layer2.biases[j]);
+			}
+		}
+
+		assertArrayEquals(net.predict(inputs), net2.predict(inputs));
+	}
+
+	@Test
+	void learnXor() {
+		int TRAINING_CYCLES = 40000;
+
+		NeuralNetwork net = new NeuralNetwork(2, 4, 1);
+
+		double[][] inputs = new double[][]{
+			{0, 0}, {0, 1}, {1, 0}, {1, 1}
+		};
+		double[][] outputs = new double[][]{
+			{0}, {1}, {1}, {0}
+		};
+
+		System.out.println("Training the neural net to learn XOR...");
+		net.train(inputs, outputs, TRAINING_CYCLES);
+		System.out.println("    finished training");
+
+		for( int i = 1; i <= net.getLayerCount(); i++ ) {
+			System.out.println("Layer " +i + " weights");
+			System.out.println(net.getLayer(i));
+		}
+
+		// calculate predictions
+		double[][] predictions = net.predict(inputs);
+		for( int i = 0; i < 4; i++ ) {
+			int parsed_pred = predictions[i][0] < 0.5 ? 0 : 1;
+
+			System.out.printf(
+				"{%.0f, %.0f} = %.4f (%d) -> %s\n",
+				inputs[i][0], inputs[i][1],
+				predictions[i][0],
+				parsed_pred,
+				parsed_pred == outputs[i][0] ? "correct" : "miss"
+			);
+		}
+	}
+
 	@Test
 	void learnCalc() {
-		Log.enableGlobalDebugging();
-
 		int INPUT_SIZE = 50;
 		int PREDICT_SIZE = 4;
 		int TRAINING_CYCLES = 40000;
-		CalcType OPERATION = CalcType.SUB;
+		CalcType OPERATION = CalcType.ADD;
 
 		// Create neural network with layer1: 4 neurones, layer2: 1 neuron
 		NeuralNetwork net = new NeuralNetwork(2, 8, 4, 1);
@@ -37,7 +118,7 @@ class NeuralNetworkTest {
 
 		for( int i = 1; i <= net.getLayerCount(); i++ ) {
 			System.out.println("Layer " +i + " weights");
-			System.out.println(net.getLayer(i).weights);
+			System.out.println(net.getLayer(i));
 		}
 
 		// calculate the predictions on unknown data