Added evaluation method for better model performance

README.md

@@ -44,11 +44,14 @@ DataSet training = new DataSet(first, second, third, fourth);
 
 We have everything setup, we can call the fit method inside a loop and wait for the network to finish.
 
+Tip: When training always split the data in two, one set for training and one for testing/evaluation.
+
 ```java
 double error;
 
 do {
-    error = network.fit(training);
+    network.fit(training);
+    error = network.evaluate(training);
 } while (error > 0.01);
 ```
 

build.gradle

@@ -4,7 +4,7 @@ plugins {
 }
 
 group = 'net.echo'
-version = '2.0.12'
+version = '2.0.2'
 
 repositories {
     mavenCentral()

src/main/java/net/echo/brain4j/model/Model.java

@@ -27,9 +27,16 @@ public interface Model {
      * Trains the model for one epoch.
      *
      * @param set dataset for training
-     * @return model error
      */
-    double fit(DataSet set);
+    void fit(DataSet set);
+
+    /**
+     * Evaluates the model on the given dataset.
+     *
+     * @param set dataset for testing
+     * @return the error of the model
+     */
+    double evaluate(DataSet set);
 
     /**
      * Predicts output for given input.

src/main/java/net/echo/brain4j/model/impl/FeedForwardModel.java

@@ -16,6 +16,7 @@
 import net.echo.brain4j.structure.Neuron;
 import net.echo.brain4j.structure.Synapse;
 import net.echo.brain4j.training.BackPropagation;
+import net.echo.brain4j.training.data.DataRow;
 import net.echo.brain4j.training.data.DataSet;
 import net.echo.brain4j.training.optimizers.Optimizer;
 import net.echo.brain4j.training.optimizers.impl.Adam;
@@ -84,8 +85,24 @@ public void compile(InitializationType type, LossFunctions function, Optimizer o
     }
 
     @Override
-    public double fit(DataSet set) {
-        return propagation.iterate(set, optimizer.getLearningRate());
+    public void fit(DataSet set) {
+        propagation.iterate(set, optimizer.getLearningRate());
+    }
+
+    @Override
+    public double evaluate(DataSet set) {
+        double totalError = 0.0;
+
+        for (DataRow row : set.getDataRows()) {
+            double[] inputs = row.inputs();
+            double[] targets = row.outputs();
+
+            double[] outputs = predict(inputs);
+
+            totalError += function.getFunction().calculate(targets, outputs);
+        }
+
+        return totalError;
     }
 
     @Override

src/main/java/net/echo/brain4j/training/BackPropagation.java

@@ -15,32 +15,24 @@
 public class BackPropagation {
 
     private final Model model;
-    private final LossFunction lossFunction;
     private final Optimizer optimizer;
 
     private int timestep = 0;
 
     public BackPropagation(Model model, Optimizer optimizer) {
         this.model = model;
-        this.lossFunction = model.getLossFunction();
         this.optimizer = optimizer;
     }
 
-    public double iterate(DataSet dataSet, double learningRate) {
-        double totalError = 0.0;
-
+    public void iterate(DataSet dataSet, double learningRate) {
         for (DataRow row : dataSet.getDataRows()) {
             double[] inputs = row.inputs();
             double[] targets = row.outputs();
 
             double[] outputs = model.predict(inputs);
 
-            totalError += lossFunction.calculate(targets, outputs);
-
             backpropagate(targets, outputs, learningRate);
         }
-
-        return totalError / dataSet.getDataRows().size();
     }
 
     private void backpropagate(double[] targets, double[] outputs, double learningRate) {