- init/connect the synapseBias from within the SynapseSlots

- TopicModel instantiation fixes

Author: lukas.molzberger

core/src/main/java/network/aika/elements/activations/ConjunctiveActivation.java

@@ -99,7 +99,7 @@ protected void connectWeightUpdate() {
     protected void initBiases() {
         neuron.getSynapseBiasSynapses()
                 .forEach(s ->
-                        s.initBiasInput(this)
+                        s.initSlots(this)
                 );
 
         super.initBiases();

core/src/main/java/network/aika/elements/links/ConjunctiveLink.java

@@ -106,13 +106,13 @@ protected void initWeightInput() {
             );
 
         if(synapse.isOptional())
-            synapse.initBiasInput(output);
+            synapse.initSlots(output);
     }
 
     public void initFromTemplate(Link template) {
         super.initFromTemplate(template);
 
-        synapse.initBiasInput(output);
+        synapse.initSlots(output);
     }
 
     @Override

core/src/main/java/network/aika/elements/synapses/ConjunctiveCategoryInputSynapse.java

@@ -42,11 +42,4 @@ public abstract class ConjunctiveCategoryInputSynapse<
     public SynapseSlot createOutputSlot(OA oAct) {
         return new AnnealingSynapseOutputSlot(oAct, this, AnnealingType.CATEGORY_INPUT);
     }
-
-    @Override
-    public void initBiasInput(OA act) {
-        super.initBiasInput(act);
-
-        act.registerInputSlot(this);
-    }
 }

core/src/main/java/network/aika/elements/synapses/ConjunctiveSynapse.java

@@ -21,7 +21,6 @@
 import network.aika.elements.synapses.slots.SynapseInputSlot;
 import network.aika.elements.synapses.slots.SynapseOutputSlot;
 import network.aika.elements.synapses.slots.SynapseSlot;
-import network.aika.enums.direction.Direction;
 import network.aika.elements.neurons.Neuron;
 import network.aika.elements.neurons.ConjunctiveNeuron;
 import network.aika.elements.activations.Activation;
@@ -35,7 +34,6 @@
 import java.io.DataOutput;
 import java.io.IOException;
 
-import static network.aika.enums.direction.Direction.OUTPUT;
 import static network.aika.fields.link.FieldLink.linkAndConnect;
 import static network.aika.queue.Phase.TRAINING;
 import static network.aika.utils.Utils.TOLERANCE;
@@ -88,7 +86,11 @@ public SynapseSlot createAndInitOutputSlot(OA oAct) {
         return slot;
     }
 
-    public void initBiasInput(OA act) {
+    public void initSlots(OA act) {
+        act.registerInputSlot(this);
+    }
+
+    public void initSynapseBias(ConjunctiveActivation act) {
         linkAndConnect(synapseBias, act.getNet(synapseType.outputState()))
                 .setPropagateUpdates(false);
     }

core/src/main/java/network/aika/elements/synapses/slots/SynapseOutputSlot.java

@@ -17,6 +17,7 @@
 package network.aika.elements.synapses.slots;
 
 import network.aika.elements.activations.Activation;
+import network.aika.elements.activations.ConjunctiveActivation;
 import network.aika.elements.links.ConjunctiveLink;
 import network.aika.elements.synapses.ConjunctiveSynapse;
 import network.aika.enums.direction.Direction;
@@ -34,7 +35,7 @@ public class SynapseOutputSlot<S extends ConjunctiveSynapse, L extends Conjuncti
 
     protected Field outputNet;
 
-    public SynapseOutputSlot(Activation act, S synapse) {
+    public SynapseOutputSlot(ConjunctiveActivation act, S synapse) {
         super(
                 act,
                 synapse,
@@ -56,6 +57,8 @@ public void init() {
                 ),
                 synapse.getWeight()
         );
+
+        synapse.initSynapseBias((ConjunctiveActivation) act);
     }
 
     public Field getOutputNet() {

core/src/main/java/network/aika/elements/synapses/types/NegativeFeedbackSynapse.java

@@ -17,6 +17,7 @@
 package network.aika.elements.synapses.types;
 
 import network.aika.Model;
+import network.aika.elements.activations.ConjunctiveActivation;
 import network.aika.elements.activations.types.BindingActivation;
 import network.aika.elements.activations.types.InhibitoryActivation;
 import network.aika.elements.links.types.NegativeFeedbackLink;
@@ -83,11 +84,9 @@ public Field getWeightForAnnealing() {
     }
 
     @Override
-    public void initBiasInput(BindingActivation act) {
+    public void initSynapseBias(ConjunctiveActivation act) {
         linkAndConnect(weight, act.getNet(synapseType.outputState()))
                 .setPropagateUpdates(false);
-
-        act.registerInputSlot(this);
     }
 
     @Override

core/src/main/java/network/aika/elements/synapses/types/OuterPositiveFeedbackSynapse.java

@@ -61,13 +61,6 @@ public OuterPositiveFeedbackLink createLink(PatternActivation input, BindingActi
         return new OuterPositiveFeedbackLink(this, input, output);
     }
 
-    @Override
-    public void initBiasInput(BindingActivation act) {
-        super.initBiasInput(act);
-
-        act.registerInputSlot(this);
-    }
-
     @Override
     public SynapseSlot createOutputSlot(BindingActivation oAct) {
         return new AnnealingSynapseOutputSlot(oAct, this, AnnealingType.OUTER_FEEDBACK);

text/src/main/java/network/aika/meta/NetworkMotifs.java

@@ -18,7 +18,6 @@
 
 import network.aika.Model;
 import network.aika.elements.Type;
-import network.aika.elements.neurons.RefType;
 import network.aika.elements.neurons.types.BindingNeuron;
 import network.aika.elements.neurons.types.InhibitoryNeuron;
 import network.aika.elements.neurons.types.LatentRelationNeuron;
@@ -37,7 +36,7 @@
  * @author Lukas Molzberger
  */
 public class NetworkMotifs {
-    private static final Logger log = LoggerFactory.getLogger(NetworkMotifs.class);
+    private static final Logger LOG = LoggerFactory.getLogger(NetworkMotifs.class);
 
     public static double DEFAULT_BINDING_INPUT_CATEGORY_SYNAPSE_WEIGHT = 5.0;
     public static double DEFAULT_PATTERN_INPUT_CATEGORY_SYNAPSE_WEIGHT = 5.0;
@@ -79,8 +78,8 @@ public static BindingNeuron addBindingNeuron(Model m, String label, double netTa
         return bn;
     }
 
-    public static void addInputObjectSynapse(PatternNeuron input, BindingNeuron bn, double weight, boolean propagable) {
-        new InputObjectSynapse()
+    public static InputObjectSynapse addInputObjectSynapse(PatternNeuron input, BindingNeuron bn, double weight, boolean propagable) {
+        return new InputObjectSynapse()
                 .setWeight(weight)
                 .link(input, bn)
                 .setPropagable(propagable)
@@ -156,7 +155,7 @@ public static InnerPositiveFeedbackSynapse addPositiveFeedbackLoop(
                 .setPropagable(true)
                 .adjustBias(bn.getTargetValue() + weakInputMargin);
 
-        log.info("  " + pSyn + " targetNetContr:" + -pSyn.getSynapseBias().getValue());
+        LOG.info("  " + pSyn + " targetNetContr:" + -pSyn.getSynapseBias().getValue());
 
         InnerPositiveFeedbackSynapse posFeedSyn = new InnerPositiveFeedbackSynapse()
                 .setWeight(getPositiveFeedbackWeight(bn.getTargetNet(), pn.getTargetValue()))
@@ -169,7 +168,7 @@ public static InnerPositiveFeedbackSynapse addPositiveFeedbackLoop(
                 .setInstantiable(instantiable, instantiable)
                 .adjustBias();
 
-        log.info("  " + posFeedSyn + " targetNetContr:" + -posFeedSyn.getSynapseBias().getValue());
+        LOG.info("  " + posFeedSyn + " targetNetContr:" + -posFeedSyn.getSynapseBias().getValue());
 
         return posFeedSyn;
     }
@@ -211,6 +210,6 @@ public static void addRelation(
 
         rel.linkRelation(spSyn, relSyn);
 
-        log.info("  " + spSyn + " targetNetContr:" + -spSyn.getSynapseBias().getValue());
+        LOG.info("  " + spSyn + " targetNetContr:" + -spSyn.getSynapseBias().getValue());
     }
 }

text/src/main/java/network/aika/meta/TemplateModel.java

@@ -25,13 +25,18 @@
 import network.aika.meta.exceptions.FailedInstantiationException;
 import network.aika.queue.steps.InstantiationTrigger;
 import network.aika.utils.Writable;
+import org.slf4j.Logger;
+import org.slf4j.LoggerFactory;
 
 /**
  *
  * @author Lukas Molzberger
  */
 public abstract class TemplateModel<T extends TemplateModel> implements InstantiationCallback, Writable {
 
+    private static final Logger LOG = LoggerFactory.getLogger(TemplateModel.class);
+
+
     protected String label;
 
     protected T parent;
@@ -122,11 +127,17 @@ protected void generateLabel(Activation tAct, Activation iAct, String l) {
     protected abstract String getLabelPostfix();
 
     protected static <N extends Neuron<?, ?>> N lookupInstance(Document doc, N templateN) {
-        return (N) templateN.getActivations(doc)
+        Activation templateInstance = templateN.getActivations(doc)
                 .stream()
                 .findFirst()
                 .orElse(null)
-                .getActiveTemplateInstance()
-                .getNeuron();
+                .getActiveTemplateInstance();
+
+        if(templateInstance == null) {
+            LOG.warn("Unable to find instance for template Neuron: " + templateN);
+            return null;
+        }
+
+        return (N) templateInstance.getNeuron();
     }
 }

text/src/main/java/network/aika/meta/entities/EntityModel.java

@@ -142,7 +142,8 @@ public void initTemplateNeurons() {
                 topicModel.getTopicBindingNeuron(),
                 10.0,
                 true
-        );
+        )
+                .setInstantiable(false, false);
 
         entityBN = addBindingNeuron(
                 phraseModel.getPatternNeuron(),
@@ -256,7 +257,8 @@ public void mapResults(Document doc) {
                 .setInstantiable(false, true);
 
         topicBN = lookupInstance(doc, parent.topicBN);
-        topicBN.setPersistent(true);
+        if(topicBN != null)
+            topicBN.setPersistent(true);
 
         inhibitoryN = lookupInstance(doc, parent.inhibitoryN);
         inhibitoryN.setPersistent(true);