build.gradle

@@ -11,7 +11,6 @@ repositories {
 
 dependencies {
     implementation 'org.ejml:ejml-all:0.41'
-    implementation 'com.opencsv:opencsv:5.6'
     testImplementation 'org.junit.jupiter:junit-jupiter-api:5.8.2'
     testRuntimeOnly 'org.junit.jupiter:junit-jupiter-engine:5.8.2'
 }

src/main/java/BlankLayer.java

@@ -1,88 +0,0 @@
-import org.ejml.simple.SimpleMatrix;
-
-import java.util.Random;
-
-/**
- * Layer initialized with 1 neuron.
- * Assumes that each new neuron is fully connected to every previous neuron (this will be changed in the future).
- */
-public class BlankLayer extends Layer {
-    SimpleMatrix weights;
-    SimpleMatrix biases;
-
-    public BlankLayer(int inputSize) {
-        Random random = new Random();
-        this.weights = new SimpleMatrix(inputSize, 1, true,
-                random.doubles(inputSize, -1, 1).toArray());
-        this.biases = new SimpleMatrix(1, 1, true,
-                random.doubles(1, -1, 1).toArray());
-    }
-
-    /**
-     * Updates input size when previous layer has newly added neurons.
-     * @param n amount of new neurons in previous layer
-     */
-    public void updateInputSize(int n) {
-        Random random = new Random();
-
-        // add new weights
-        SimpleMatrix newWeights = new SimpleMatrix(this.weights.numRows() + n, this.weights.numCols());
-        for (int i = 0; i < this.weights.numRows(); i++) {
-            for (int j = 0; j < this.weights.numCols(); j++) {
-                newWeights.set(i, j, this.weights.get(i, j));
-            }
-        }
-        for (int i = 0; i < newWeights.getNumElements(); i++) {
-            if (newWeights.get(i) == 0) {
-                newWeights.set(i, random.nextDouble(-1, 1));
-            }
-        }
-        this.weights = newWeights;
-    }
-
-    /**
-     * Adds new neurons at the end of the layer
-     * @param n amount how many new neurons should be added
-     */
-    public void addNeuron(int n) {
-        Random random = new Random();
-
-        // add new weights
-        SimpleMatrix newWeights = new SimpleMatrix(this.weights.numRows(), this.weights.numCols() + n);
-        for (int i = 0; i < this.weights.numRows(); i++) {
-            for (int j = 0; j < this.weights.numCols(); j++) {
-                newWeights.set(i, j, this.weights.get(i, j));
-            }
-        }
-        for (int i = 0; i < newWeights.getNumElements(); i++) {
-            if (newWeights.get(i) == 0) {
-                newWeights.set(i, random.nextDouble(-1, 1));
-            }
-        }
-        this.weights = newWeights;
-
-        // add new biases
-        SimpleMatrix newBiases = new SimpleMatrix(1, this.biases.numCols() + n);
-        double[] newBiasValues = random.doubles(n, -1, 1).toArray();
-        System.arraycopy(this.biases.getDDRM().data, 0, newBiases.getDDRM().data, 0, this.biases.numCols());
-        System.arraycopy(newBiasValues, 0, newBiases.getDDRM().data, this.biases.numCols(), n);
-        this.biases = newBiases;
-    }
-
-    @Override
-    public SimpleMatrix forwardPropagation(SimpleMatrix inputs) {
-        this.input = inputs;
-        this.output = this.input.mult(this.weights).plus(this.biases);
-        return this.output;
-    }
-
-    @Override
-    public SimpleMatrix backwardPropagation(SimpleMatrix outputError, double learningRate) {
-        SimpleMatrix inputError = outputError.mult(this.weights.transpose());
-        SimpleMatrix weightsError = this.input.transpose().mult(outputError);
-
-        this.weights = this.weights.plus(learningRate, weightsError);
-        this.biases = this.biases.plus(learningRate, outputError);
-        return inputError;
-    }
-}

src/main/java/ExampleXOR.java

@@ -12,10 +12,10 @@ public class ExampleXOR {
                 new SimpleMatrix(new double[][]{{0}})};
 
         Network network = new Network();
-        network.addLayer(new FCLayer(2, 3));
+        network.add(new FCLayer(2, 3));
-        network.addLayer(new ActivationLayer(ActivationFunctions::tanh, ActivationFunctions::tanhPrime));
+        network.add(new ActivationLayer(ActivationFunctions::tanh, ActivationFunctions::tanhPrime));
-        network.addLayer(new FCLayer(3, 1));
+        network.add(new FCLayer(3, 1));
-        network.addLayer(new ActivationLayer(ActivationFunctions::tanh, ActivationFunctions::tanhPrime));
+        network.add(new ActivationLayer(ActivationFunctions::tanh, ActivationFunctions::tanhPrime));
 
         network.use(LossFunctions::MSE, LossFunctions::MSEPrime);
         network.fit(X_train, y_train, 1000, 0.1d);

src/main/java/ExampleXORBlankLayers.java

@@ -1,32 +0,0 @@
-import org.ejml.simple.SimpleMatrix;
-
-public class ExampleXORBlankLayers {
-    public static void main(String[] args) {
-        SimpleMatrix[] X_train = {new SimpleMatrix(new double[][]{{0, 0}}),
-                new SimpleMatrix(new double[][]{{0, 1}}),
-                new SimpleMatrix(new double[][]{{1, 0}}),
-                new SimpleMatrix(new double[][]{{1, 1}})};
-        SimpleMatrix[] y_train = {new SimpleMatrix(new double[][]{{0}}),
-                new SimpleMatrix(new double[][]{{1}}),
-                new SimpleMatrix(new double[][]{{1}}),
-                new SimpleMatrix(new double[][]{{0}})};
-
-        Network network = new Network();
-        network.addLayer(new BlankLayer(2));
-        network.addLayer(new ActivationLayer(ActivationFunctions::tanh, ActivationFunctions::tanhPrime));
-        network.addLayer(new BlankLayer(1));
-        network.addLayer(new ActivationLayer(ActivationFunctions::tanh, ActivationFunctions::tanhPrime));
-        network.addNeuron(0, 2);
-
-        network.use(LossFunctions::MSE, LossFunctions::MSEPrime);
-        network.fit(X_train, y_train, 1000, 0.1d);
-
-        SimpleMatrix[] output = network.predict(X_train);
-        for (SimpleMatrix entry : output) {
-            System.out.println("Prediction:");
-            for (int i = 0; i < entry.getNumElements(); i++) {
-                System.out.println(Math.round(entry.get(i)));
-            }
-        }
-    }
-}

src/main/java/Network.java

@@ -4,34 +4,19 @@ import java.util.ArrayList;
 import java.util.function.BiFunction;
 
 public class Network {
-    private ArrayList<Layer> layers;
+
-    private BiFunction<SimpleMatrix, SimpleMatrix, Double> loss;
+    ArrayList<Layer> layers;
-    private BiFunction<SimpleMatrix, SimpleMatrix, SimpleMatrix> lossPrime;
+    BiFunction<SimpleMatrix, SimpleMatrix, Double> loss;
+    BiFunction<SimpleMatrix, SimpleMatrix, SimpleMatrix> lossPrime;
 
     public Network() {
         layers = new ArrayList<>();
     }
 
-    public void addLayer(Layer layer) {
+    public void add(Layer layer) {
         layers.add(layer);
     }
 
-    /**
-     * Adds n neurons to a specific layer and also updates this and the next layer's weights and biases.
-     * Only works if there are two successive BlankLayers.
-     * @param layer index of layer in the ArrayList layers
-     * @param n amount how many new neurons should be added
-     */
-    public void addNeuron(int layer, int n) {
-        if (!(this.layers.get(layer) instanceof BlankLayer)) {
-            System.out.println("This layer is not a BlankLayer");
-        } else if (!(this.layers.get(layer + 2) instanceof BlankLayer)) {
-            System.out.println("The next layer is not a BlankLayer");
-        }
-        ((BlankLayer) this.layers.get(layer)).addNeuron(n);
-        ((BlankLayer) this.layers.get(layer + 2)).updateInputSize(n);
-    }
-
     public void use(BiFunction<SimpleMatrix, SimpleMatrix, Double> loss, BiFunction<SimpleMatrix, SimpleMatrix, SimpleMatrix> lossPrime) {
         this.loss = loss;
         this.lossPrime = lossPrime;
@@ -80,28 +65,4 @@ public class Network {
             System.out.println("epoch " + (i+1) + "/" + epochs + "  error=" + err);
         }
     }
-
-    public ArrayList<Layer> getLayers() {
-        return layers;
-    }
-
-    public void setLayers(ArrayList<Layer> layers) {
-        this.layers = layers;
-    }
-
-    public BiFunction<SimpleMatrix, SimpleMatrix, Double> getLoss() {
-        return loss;
-    }
-
-    public void setLoss(BiFunction<SimpleMatrix, SimpleMatrix, Double> loss) {
-        this.loss = loss;
-    }
-
-    public BiFunction<SimpleMatrix, SimpleMatrix, SimpleMatrix> getLossPrime() {
-        return lossPrime;
-    }
-
-    public void setLossPrime(BiFunction<SimpleMatrix, SimpleMatrix, SimpleMatrix> lossPrime) {
-        this.lossPrime = lossPrime;
-    }
 }

src/main/java/Utilities.java

@@ -1,33 +0,0 @@
-import com.opencsv.CSVReader;
-import com.opencsv.exceptions.CsvValidationException;
-import org.ejml.simple.SimpleMatrix;
-
-import java.io.FileReader;
-import java.io.IOException;
-import java.util.ArrayList;
-import java.util.Arrays;
-import java.util.List;
-
-public class Utilities {
-    public static SimpleMatrix ones(int rows, int columns) {
-        SimpleMatrix mat = new SimpleMatrix(rows, columns);
-        Arrays.fill(mat.getDDRM().data, 1);
-        return mat;
-    }
-
-    public static List<List<String>> readCSV(String filename) {
-        List<List<String>> entries = new ArrayList<>();
-        try (CSVReader csvReader = new CSVReader(new FileReader(filename))) {
-            String[] values;
-            while ((values = csvReader.readNext()) != null) {
-                entries.add(Arrays.asList(values));
-            }
-            return entries;
-        } catch (IOException e) {
-            System.out.println(filename + " does not exist");
-        } catch (CsvValidationException e) {
-            System.out.println("Invalid line in " + filename);
-        }
-        return null;
-    }
-}