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

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 FCLayer(2, 1));
+        network.addLayer(new FCLayer(1));
         network.addLayer(new ActivationLayer(ActivationFunctions::tanh, ActivationFunctions::tanhPrime));
-        network.addLayer(new FCLayer(1, 1));
+        network.addLayer(new FCLayer(1));
         network.addLayer(new ActivationLayer(ActivationFunctions::tanh, ActivationFunctions::tanhPrime));
         network.addNeuron(0, 2);
 

src/main/java/FCLayer.java

@@ -3,15 +3,23 @@ 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;
     }
 
     /**
@@ -19,6 +27,7 @@ public class FCLayer extends Layer {
      * @param n amount of new neurons in previous layer
      */
     public void updateInputSize(int n) {
+        if (isInitialized) {
             Random random = new Random();
 
             // add new weights
@@ -35,6 +44,7 @@ public class FCLayer extends Layer {
             }
             this.weights = newWeights;
         }
+    }
 
     /**
      * Adds new neurons at the end of the layer
@@ -43,6 +53,10 @@ public class FCLayer extends Layer {
     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++) {
@@ -64,9 +78,14 @@ public class FCLayer extends Layer {
             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;