Added support for choosing weight and bias initializers

src/main/java/de/lluni/javann/Initializer.java

@@ -0,0 +1,8 @@
+package de.lluni.javann;
+
+public enum Initializer {
+    ZEROS,
+    ONES,
+    GAUSSIAN,
+    RANDOM
+}

src/main/java/de/lluni/javann/examples/ExampleSine.java

@@ -1,5 +1,6 @@
 package de.lluni.javann.examples;
 
+import de.lluni.javann.Initializer;
 import de.lluni.javann.Network;
 import de.lluni.javann.functions.ActivationFunctions;
 import de.lluni.javann.functions.LossFunctions;
@@ -46,11 +47,11 @@ public class ExampleSine {
 
         // create network and add layers
         Network network = new Network();
-        network.addLayer(new FCLayer(8));
+        network.addLayer(new FCLayer(8, Initializer.GAUSSIAN, Initializer.ONES));
         network.addLayer(new ActivationLayer(ActivationFunctions::LeakyReLu, ActivationFunctions::LeakyReLuPrime));
-        network.addLayer(new FCLayer(8));
+        network.addLayer(new FCLayer(8, Initializer.GAUSSIAN, Initializer.ONES));
         network.addLayer(new ActivationLayer(ActivationFunctions::LeakyReLu, ActivationFunctions::LeakyReLuPrime));
-        network.addLayer(new FCLayer(1));
+        network.addLayer(new FCLayer(1, Initializer.GAUSSIAN, Initializer.ONES));
 
         // configure loss function for the network
         network.use(LossFunctions::MSE, LossFunctions::MSEPrime);

src/main/java/de/lluni/javann/examples/ExampleXOR.java

@@ -1,5 +1,6 @@
 package de.lluni.javann.examples;
 
+import de.lluni.javann.Initializer;
 import de.lluni.javann.Network;
 import de.lluni.javann.functions.ActivationFunctions;
 import de.lluni.javann.functions.LossFunctions;
@@ -19,9 +20,9 @@ public class ExampleXOR {
                 new SimpleMatrix(new double[][]{{0}})};
 
         Network network = new Network();
-        network.addLayer(new FCLayer(3));
+        network.addLayer(new FCLayer(3, Initializer.RANDOM, Initializer.RANDOM));
         network.addLayer(new ActivationLayer(ActivationFunctions::tanh, ActivationFunctions::tanhPrime));
-        network.addLayer(new FCLayer(1));
+        network.addLayer(new FCLayer(1, Initializer.RANDOM, Initializer.RANDOM));
         network.addLayer(new ActivationLayer(ActivationFunctions::tanh, ActivationFunctions::tanhPrime));
 
         network.use(LossFunctions::MSE, LossFunctions::MSEPrime);

src/main/java/de/lluni/javann/examples/ExampleXORAddedNeurons.java

@@ -1,5 +1,6 @@
 package de.lluni.javann.examples;
 
+import de.lluni.javann.Initializer;
 import de.lluni.javann.Network;
 import de.lluni.javann.functions.ActivationFunctions;
 import de.lluni.javann.functions.LossFunctions;
@@ -19,9 +20,9 @@ public class ExampleXORAddedNeurons {
                 new SimpleMatrix(new double[][]{{0}})};
 
         Network network = new Network();
-        network.addLayer(new FCLayer(1));
+        network.addLayer(new FCLayer(1, Initializer.RANDOM, Initializer.RANDOM));
         network.addLayer(new ActivationLayer(ActivationFunctions::tanh, ActivationFunctions::tanhPrime));
-        network.addLayer(new FCLayer(1));
+        network.addLayer(new FCLayer(1, Initializer.RANDOM, Initializer.RANDOM));
         network.addLayer(new ActivationLayer(ActivationFunctions::tanh, ActivationFunctions::tanhPrime));
         network.addNeuron(0, 2);
 

src/main/java/de/lluni/javann/layers/FCLayer.java

@@ -1,5 +1,6 @@
 package de.lluni.javann.layers;
 
+import de.lluni.javann.Initializer;
 import de.lluni.javann.util.Utilities;
 import org.ejml.simple.SimpleMatrix;
 
@@ -9,6 +10,8 @@ import java.util.Random;
  * Fully connected layer with n Neurons
  */
 public class FCLayer extends Layer {
+    private final Initializer weightInit;
+    private final Initializer biasInit;
     private SimpleMatrix weights;
     private SimpleMatrix biases;
     private int numNeurons;
@@ -18,14 +21,26 @@ public class FCLayer extends Layer {
      * Creates a fully connected layer with numNeurons neurons
      * @param numNeurons amount of neurons in this layer
      */
-    public FCLayer(int numNeurons) {
+    public FCLayer(int numNeurons, Initializer weightInit, Initializer biasInit) {
         this.numNeurons = numNeurons;
+        this.weightInit = weightInit;
+        this.biasInit = biasInit;
         isInitialized = false;
     }
 
     private void initialize(int inputSize) {
-        this.weights = Utilities.gaussianMatrix(inputSize, numNeurons, 0, 1, 0.1d);
+        switch (weightInit) {
-        this.biases = Utilities.ones(1, numNeurons);
+            case ZEROS -> this.weights = new SimpleMatrix(inputSize, numNeurons);
+            case ONES -> this.weights = Utilities.ones(inputSize, numNeurons);
+            case GAUSSIAN -> this.weights = Utilities.gaussianMatrix(inputSize, numNeurons, 0, 1, 0.1d);
+            case RANDOM -> this.weights = Utilities.randomMatrix(inputSize, numNeurons);
+        }
+        switch (biasInit) {
+            case ZEROS -> this.biases = new SimpleMatrix(1, numNeurons);
+            case ONES -> this.biases = Utilities.ones(1, numNeurons);
+            case GAUSSIAN -> this.biases = Utilities.gaussianMatrix(1, numNeurons, 0, 1, 0.1d);
+            case RANDOM -> this.biases = Utilities.randomMatrix(1, numNeurons);
+        }
         this.isInitialized = true;
     }