#5351 Fix EvaluativeListener workspace issue

deeplearning4j · May 28, 2018 · df681f0 · df681f0
1 parent a589d2c
commit df681f0
Show file tree

Hide file tree

Showing 2 changed files with 37 additions and 22 deletions.
diff --git a/deeplearning4j/deeplearning4j-core/src/test/java/org/deeplearning4j/eval/EvalTest.java b/deeplearning4j/deeplearning4j-core/src/test/java/org/deeplearning4j/eval/EvalTest.java
@@ -43,6 +43,7 @@
 import org.deeplearning4j.nn.graph.ComputationGraph;
 import org.deeplearning4j.nn.multilayer.MultiLayerNetwork;
 import org.deeplearning4j.nn.weights.WeightInit;
+import org.deeplearning4j.optimize.listeners.EvaluativeListener;
 import org.deeplearning4j.optimize.listeners.ScoreIterationListener;
 import org.junit.Test;
 import org.nd4j.linalg.activations.Activation;
@@ -1249,6 +1250,40 @@ public void testConfusionMatrixString(){
 
         System.out.println("\n\n\n\n");
         System.out.println(e.stats(false, true));
+    }
+
+
+
+    @Test
+    public void testEvaluativeListenerSimple(){
+        //Sanity check: https://github.com/deeplearning4j/deeplearning4j/issues/5351
+
+        // Network config
+        MultiLayerConfiguration conf = new NeuralNetConfiguration.Builder()
+
+                .optimizationAlgo(OptimizationAlgorithm.LINE_GRADIENT_DESCENT).seed(42)
+                .updater(new Sgd(1e-6)).list()
+                .layer(0, new DenseLayer.Builder().nIn(4).nOut(2).activation(Activation.TANH)
+                        .weightInit(WeightInit.XAVIER).build())
+                .layer(1, new org.deeplearning4j.nn.conf.layers.OutputLayer.Builder(
+                        LossFunctions.LossFunction.MCXENT).nIn(2).nOut(3).weightInit(WeightInit.XAVIER)
+                        .activation(Activation.SOFTMAX).build())
+                .build();
+
+        // Instantiate model
+        MultiLayerNetwork net = new MultiLayerNetwork(conf);
+        net.init();
+
+        // Train-test split
+        DataSetIterator iter = new IrisDataSetIterator(30, 150);
+        DataSetIterator iterTest = new IrisDataSetIterator(30, 150);
+
+        net.setListeners(new EvaluativeListener(iterTest, 3));
+
+        for( int i=0; i<10; i++ ){
+            net.fit(iter);
+        }
+
 
     }
 }
diff --git a/...learning4j-nn/src/main/java/org/deeplearning4j/optimize/listeners/EvaluativeListener.java b/...learning4j-nn/src/main/java/org/deeplearning4j/optimize/listeners/EvaluativeListener.java
@@ -152,7 +152,8 @@ public EvaluativeListener(@NonNull MultiDataSet multiDataSet, int frequency, @No
      */
     @Override
     public void iterationDone(Model model, int iteration, int epoch) {
-        // no-op
+        if (invocationType == InvocationType.ITERATION_END)
+            invokeListener(model);
     }
 
     @Override
@@ -167,27 +168,6 @@ public void onEpochEnd(Model model) {
             invokeListener(model);
     }
 
-    @Override
-    public void onForwardPass(Model model, List<INDArray> activations) {
-        // no-op
-    }
-
-    @Override
-    public void onForwardPass(Model model, Map<String, INDArray> activations) {
-        // no-op
-    }
-
-    @Override
-    public void onGradientCalculation(Model model) {
-        // no-op
-    }
-
-    @Override
-    public void onBackwardPass(Model model) {
-        if (invocationType == InvocationType.ITERATION_END)
-            invokeListener(model);
-    }
-
     protected void invokeListener(Model model) {
         if (iterationCount.get() == null)
             iterationCount.set(new AtomicLong(0));