The inputSize of each layer does not need to be specified anymore

src/main/java/BlankLayer.java

@@ -3,6 +3,7 @@ import org.ejml.simple.SimpleMatrix;
 import java.util.Random;
 
 /**
+ * Goal: initialize layer without any neurons. Not yet implemented.
  * Layer initialized with 1 neuron.
  * Assumes that each new neuron is fully connected to every previous neuron (this will be changed in the future).
  */
@@ -18,57 +19,6 @@ public class BlankLayer extends Layer {
                 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;

src/main/java/ExampleXOR.java

@@ -12,9 +12,9 @@ public class ExampleXOR {
                 new SimpleMatrix(new double[][]{{0}})};
 
         Network network = new Network();
-        network.addLayer(new FCLayer(2, 3));
+        network.addLayer(new FCLayer(3));
         network.addLayer(new ActivationLayer(ActivationFunctions::tanh, ActivationFunctions::tanhPrime));
-        network.addLayer(new FCLayer(3, 1));
+        network.addLayer(new FCLayer(1));
         network.addLayer(new ActivationLayer(ActivationFunctions::tanh, ActivationFunctions::tanhPrime));
 
         network.use(LossFunctions::MSE, LossFunctions::MSEPrime);

src/main/java/ExampleXORBlankLayers.java

@@ -12,9 +12,9 @@ public class ExampleXORBlankLayers {
                 new SimpleMatrix(new double[][]{{0}})};
 
         Network network = new Network();
-        network.addLayer(new BlankLayer(2));
+        network.addLayer(new FCLayer(1));
         network.addLayer(new ActivationLayer(ActivationFunctions::tanh, ActivationFunctions::tanhPrime));
-        network.addLayer(new BlankLayer(1));
+        network.addLayer(new FCLayer(1));
         network.addLayer(new ActivationLayer(ActivationFunctions::tanh, ActivationFunctions::tanhPrime));
         network.addNeuron(0, 2);
 

src/main/java/FCLayer.java

@@ -3,19 +3,89 @@ import org.ejml.simple.SimpleMatrix;
 import java.util.Random;
 
 public class FCLayer extends Layer {
-    SimpleMatrix weights;
+    private SimpleMatrix weights;
-    SimpleMatrix biases;
+    private SimpleMatrix biases;
+    private int numNeurons;
+    private boolean isInitialized;
 
-    public FCLayer(int inputSize, int outputSize) {
+    public FCLayer(int numNeurons) {
+        this.numNeurons = numNeurons;
+        isInitialized = false;
+    }
+
+    private void initialize(int inputSize) {
         Random random = new Random();
-        weights = new SimpleMatrix(inputSize, outputSize, true,
+        this.weights = new SimpleMatrix(inputSize, numNeurons, true,
-                random.doubles((long) inputSize*outputSize, -1, 1).toArray());
+                random.doubles((long) inputSize*numNeurons, -1, 1).toArray());
-        biases = new SimpleMatrix(1, outputSize, true,
+        this.biases = new SimpleMatrix(1, numNeurons, true,
-                random.doubles(outputSize, -1, 1).toArray());
+                random.doubles(numNeurons, -1, 1).toArray());
+        this.isInitialized = true;
+    }
+
+    /**
+     * Updates input size when previous layer has newly added neurons.
+     * @param n amount of new neurons in previous layer
+     */
+    public void updateInputSize(int n) {
+        if (isInitialized) {
+            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();
+
+        // update neuron count
+        this.numNeurons += n;
+
+        if (isInitialized) {
+            // 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) {
+        if (!isInitialized) {
+            initialize(inputs.numCols());
+        }
+
         this.input = inputs;
         this.output = this.input.mult(this.weights).plus(this.biases);
         return this.output;

src/main/java/Network.java

@@ -23,13 +23,13 @@ public class Network {
      * @param n amount how many new neurons should be added
      */
     public void addNeuron(int layer, int n) {
-        if (!(this.layers.get(layer) instanceof BlankLayer)) {
+        if (!(this.layers.get(layer) instanceof FCLayer)) {
             System.out.println("This layer is not a BlankLayer");
-        } else if (!(this.layers.get(layer + 2) instanceof BlankLayer)) {
+        } else if (!(this.layers.get(layer + 2) instanceof FCLayer)) {
             System.out.println("The next layer is not a BlankLayer");
         }
-        ((BlankLayer) this.layers.get(layer)).addNeuron(n);
+        ((FCLayer) this.layers.get(layer)).addNeuron(n);
-        ((BlankLayer) this.layers.get(layer + 2)).updateInputSize(n);
+        ((FCLayer) this.layers.get(layer + 2)).updateInputSize(n);
     }
 
     public void use(BiFunction<SimpleMatrix, SimpleMatrix, Double> loss, BiFunction<SimpleMatrix, SimpleMatrix, SimpleMatrix> lossPrime) {