SynEvalResult

Author: Lukas Molzberger

src/main/java/org/aika/corpus/Document.java

@@ -710,7 +710,18 @@ public interface SynapseEvaluation {
          * @param oAct
          * @return
          */
-        Synapse.Key evaluate(Activation iAct, Activation oAct);
+        SynEvalResult evaluate(Activation iAct, Activation oAct);
+    }
+
+
+    public static class SynEvalResult {
+        public SynEvalResult(Synapse.Key synapseKey, double significance) {
+            this.synapseKey = synapseKey;
+            this.significance = significance;
+        }
+
+        public Synapse.Key synapseKey;
+        public double significance;
     }
 
 

src/main/java/org/aika/neuron/INeuron.java

@@ -342,9 +342,9 @@ public void train(Document doc, Activation targetAct, double learnRate, Document
         biasDelta += x;
         for (INeuron n : doc.finallyActivatedNeurons) {
             for(Activation iAct: n.getFinalActivations(doc)) {
-                Synapse.Key sk = se.evaluate(iAct, targetAct);
-                if(sk != null) {
-                    trainSynapse(doc, iAct, sk, x, v);
+                Document.SynEvalResult ser = se.evaluate(iAct, targetAct);
+                if(ser != null) {
+                    trainSynapse(doc, iAct, ser, x, v);
                 }
             }
         }
@@ -353,29 +353,29 @@ public void train(Document doc, Activation targetAct, double learnRate, Document
     }
 
 
-    private void trainSynapse(Document doc, Activation iAct, Synapse.Key sk, double x, long v) {
+    private void trainSynapse(Document doc, Activation iAct, Document.SynEvalResult ser, double x, long v) {
         if (iAct.visitedNeuronTrain == v) return;
         iAct.visitedNeuronTrain = v;
 
         INeuron inputNeuron = iAct.key.node.neuron.get(doc);
         if(inputNeuron == this) {
             return;
         }
-        double deltaW = x * iAct.finalState.value;
+        double deltaW = x * ser.significance * iAct.finalState.value;
 
-        Provider<InputNode> inp = inputNeuron.outputNodes.get(sk.createInputNodeKey());
+        Provider<InputNode> inp = inputNeuron.outputNodes.get(ser.synapseKey.createInputNodeKey());
         Synapse synapse = null;
         InputNode in = null;
         if(inp != null) {
             in = inp.get(doc);
-            synapse = in.getSynapse(sk.relativeRid, provider);
+            synapse = in.getSynapse(ser.synapseKey.relativeRid, provider);
         }
 
         if(synapse == null) {
-            synapse = new Synapse(provider.model, inputNeuron.provider, provider, sk);
+            synapse = new Synapse(provider.model, inputNeuron.provider, provider, ser.synapseKey);
 
             if(in == null) {
-                in = InputNode.add(provider.model, sk.createInputNodeKey(), synapse.input.get(doc));
+                in = InputNode.add(provider.model, ser.synapseKey.createInputNodeKey(), synapse.input.get(doc));
             }
             in.setSynapse(synapse);
             synapse.link();

src/test/java/org/aika/network/TrainingTest.java

@@ -48,16 +48,19 @@ public void testTraining() {
         targetAct.errorSignal = 1.0 - targetAct.finalState.value;
 
         out.get().train(doc, targetAct, 0.01,
-                (iAct, oAct) -> new Synapse.Key(
-                        false,
-                        0,
-                        null,
-                        Range.Operator.EQUALS,
-                        Range.Mapping.START,
-                        true,
-                        Range.Operator.EQUALS,
-                        Range.Mapping.END,
-                        true
+                (iAct, oAct) -> new Document.SynEvalResult(
+                        new Synapse.Key(
+                                false,
+                                0,
+                                null,
+                                Range.Operator.EQUALS,
+                                Range.Mapping.START,
+                                true,
+                                Range.Operator.EQUALS,
+                                Range.Mapping.END,
+                                true
+                        )
+                        , 1.0
                 )
         );
 
@@ -91,20 +94,24 @@ public void testTraining1() {
                     new Document.TrainConfig()
                             .setLearnRate(2.0)
                             .setPerformBackpropagation(false)
-                            .setSynapseEvaluation((iAct, oAct) -> new Synapse.Key(
-                                    false,
-                                    0,
-                                    null,
-                                    Range.Operator.EQUALS,
-                                    Range.Mapping.START,
-                                    true,
-                                    Range.Operator.EQUALS,
-                                    Range.Mapping.END,
-                                    true
-                            ))
+                            .setSynapseEvaluation((iAct, oAct) ->
+                                    new Document.SynEvalResult(new Synapse.Key(
+                                            false,
+                                            0,
+                                            null,
+                                            Range.Operator.EQUALS,
+                                            Range.Mapping.START,
+                                            true,
+                                            Range.Operator.EQUALS,
+                                            Range.Mapping.END,
+                                            true
+                                    ),
+                                            1.0
+                                    )
+                            )
             );
 
-            doc.adjust();
+            doc.commit();
 
             doc.clearActivations();
         }