private WeightChangeTreeElement(bool isRoot, SlimRnnNeuronWithWeight neuronWithWeight, uint weightWithPropabilityTableIndex, WeightChangeTreeElement parent)
{
this.isRoot = isRoot;
this.neuronWithWeight = neuronWithWeight;
this.weightWithPropabilityTableIndex = weightWithPropabilityTableIndex;
this.parent = parent;
}
// calculates the timebound
// see chapter 3.2 (Incremental Adaptive) Universal Search for SLIM NNs
// algorithm "Univeral SLIM NN Search"
double calcTimebound(uint levinSearchIteration, WeightChangeTreeElement treeElement)
{
Debug.Assert(treeElement != null);
Debug.Assert(!treeElement.isRoot); // root node doesn't have any changed weights, so is invalid
double propabilityProduct = 1.0;
// walk down the tree and multiply the propabilities of the selected connections
WeightChangeTreeElement currentTreeElement = treeElement;
for (;;)
{
if (/*unnecessary currentTreeElement == null ||*/ currentTreeElement.isRoot)
{
break;
}
propabilityProduct *= weightWithPropabilityTable[(int)currentTreeElement.weightWithPropabilityTableIndex].propability;
currentTreeElement = currentTreeElement.parent;
}
Debug.Assert(propabilityProduct <= 1.0 && propabilityProduct >= 0.0);
return(((double)(calcTimeboundFactor(levinSearchIteration))) * propabilityProduct);
}
IList <WeightChangeTreeElement> createWeightChangeTreeElementsForConnectionAndAddToParent(SlimRnnNeuronWithWeight connection, WeightChangeTreeElement parent)
{
IList <WeightChangeTreeElement> createdWeightChangeElements = new List <WeightChangeTreeElement>(weightWithPropabilityTable.Count);
for (
uint weightWithPropabilityTableIndex = 0;
weightWithPropabilityTableIndex < weightWithPropabilityTable.Count;
weightWithPropabilityTableIndex++
)
{
WeightChangeTreeElement createdWeightChangeTreeElement = WeightChangeTreeElement.make(connection, weightWithPropabilityTableIndex, parent);
parent.children.Add(createdWeightChangeTreeElement);
parent.childrenConnectionNeuronIndices.Add(new Tuple <uint, uint>(connection.source.neuronIndex, connection.target.neuronIndex));
createdWeightChangeElements.Add(createdWeightChangeTreeElement);
}
return(createdWeightChangeElements);
}
// depth first search of the changes of the weights of the SLIM-RNN
//
// this version doesn't timeshare the computations (to save RAM)
// /param mustHalt has the SLIM-RNN to halt to be a valid solution?
void depthFirstSearch(uint levinSearchIteration, bool mustHalt, out bool wasSolved, out SlimRnn solutionRnn)
{
solutionRnn = null;
wasSolved = false;
WeightChangeTreeElement weightChangeTreeRoot = WeightChangeTreeElement.makeRoot();
currentWeightChangeTreeElement = null; // to ignore calls to ISlimRnnLearningAlgorithm.opportunityToAdjustWeight()
// scan all weights for eligable weights
List <SlimRnnNeuronWithWeight> eligibleWeights = new List <SlimRnnNeuronWithWeight>();
foreach (SlimRnnNeuron iNeuron in slimRnn.neurons)
{
eligibleWeights.AddRange(iNeuron.outNeuronsWithWeights.Where(v => v.isEligable));
}
// build tree of the possible weight changes
//
// by iterating over all elements in the trace and adding all possible weight values to the weightChangeTree
{
List <WeightChangeTreeElement>
weightChangeTreeLeafElements = new List <WeightChangeTreeElement> {
weightChangeTreeRoot
},
nextWeightChangeTreeLeafElements = new List <WeightChangeTreeElement>();
foreach (SlimRnnNeuronWithWeight iTrace in eligibleWeights)
{
foreach (WeightChangeTreeElement iWeightChangeTreeElement in weightChangeTreeLeafElements)
{
/*
* for (
* uint weightWithPropabilityTableIndex = 0;
* weightWithPropabilityTableIndex < weightWithPropabilityTable.Count;
* weightWithPropabilityTableIndex++
* ) {
*
* WeightChangeTreeElement createdWeightChangeTreeElement = WeightChangeTreeElement.make(iTrace, weightWithPropabilityTableIndex, *parent**iWeightChangeTreeElement);
* iWeightChangeTreeElement.children.Add(createdWeightChangeTreeElement);
* iWeightChangeTreeElement.childrenConnectionNeuronIndices.Add(new Tuple<uint, uint>(iTrace.source.neuronIndex, iTrace.target.neuronIndex));
*
* nextWeightChangeTreeLeafElements.Add(createdWeightChangeTreeElement); // keep track of new leaf elements of the weight change tree
* }*/
nextWeightChangeTreeLeafElements.AddRange(createWeightChangeTreeElementsForConnectionAndAddToParent(iTrace, iWeightChangeTreeElement));
}
weightChangeTreeLeafElements = nextWeightChangeTreeLeafElements;
nextWeightChangeTreeLeafElements = new List <WeightChangeTreeElement>();
}
}
// set all weights of the trace to zero
// we do this because the connections are this way inactive and this avoids any call to ISlimRnnLearningAlgorithm.opportunityToAdjustWeight() for
// connections which are already inside the weightChangeTree
foreach (var iNeuronWithWeight in eligibleWeights)
{
iNeuronWithWeight.weight = 0.0f;
}
// depth-first-search iterate and update the weightChange tree as necessary
List <DepthFirstSearchStackElement> stack = new List <DepthFirstSearchStackElement>();
stack.Clear();
stack.push(DepthFirstSearchStackElement.make(weightChangeTreeRoot));
while (!stack.isEmpty())
{
DepthFirstSearchStackElement topStackElement = stack.pop();
// calls to ISlimRnnLearningAlgorithm.opportunityToAdjustWeight() have to modify the tree
currentWeightChangeTreeElement = topStackElement.treeElement;
if (!currentWeightChangeTreeElement.isRoot)
{
// do modification of SLIM-RNN
// OPTIMIZATION TODO< check if we have to do this recursivly or if it leads to the right answer with the nonrecursive code, the recursive code is correct >
// nonrecursive code:
// currentWeightChangeTreeElement.neuronWithWeight.weight = weightWithPropabilityTable[(int)currentWeightChangeTreeElement.weightWithPropabilityTableIndex].weight;
// recursive code
WeightChangeTreeElement currentWeightUpdateElement = currentWeightChangeTreeElement;
for (;;)
{
if (/*unnecessary currentWeightUpdateElement == null ||*/ currentWeightUpdateElement.isRoot)
{
break;
}
currentWeightUpdateElement.neuronWithWeight.weight = weightWithPropabilityTable[(int)currentWeightUpdateElement.weightWithPropabilityTableIndex].weight;
currentWeightUpdateElement = currentWeightUpdateElement.parent;
}
// we need to label all connections which got already adapted
currentWeightUpdateElement = currentWeightChangeTreeElement;
for (;;)
{
if (/*unnecessary currentWeightUpdateElement == null ||*/ currentWeightUpdateElement.isRoot)
{
break;
}
var connectionTuple = new Tuple <uint, uint>(currentWeightUpdateElement.neuronWithWeight.source.neuronIndex, currentWeightUpdateElement.neuronWithWeight.target.neuronIndex);
Debug.Assert(!globalConnectionNeuronIndices.Contains(connectionTuple));
globalConnectionNeuronIndices.Add(connectionTuple);
currentWeightUpdateElement = currentWeightUpdateElement.parent;
}
// debug network
//SlimRnnDebug.debugConnections(slimRnn);
double tLim = calcTimebound(levinSearchIteration, topStackElement.treeElement);
bool slimRnnSolvedTask = tester.doesSlimRnnSolveTask(slimRnn, mustHalt, tLim);
if (slimRnnSolvedTask)
{
// the task has been solved with this network
wasSolved = true;
solutionRnn = slimRnn;
return;
}
// reset all touched connections to 0.0 to avoid any sideeffects
// OPTIMIZATION TODO< in the recursive version of depth-first-search we don't need to do this because we modify the network with each successive call,
// so in this version we don't have to reset the whole connections to null >
currentWeightUpdateElement = currentWeightChangeTreeElement;
for (;;)
{
if (/*unnecessary currentWeightUpdateElement == null ||*/ currentWeightUpdateElement.isRoot)
{
break;
}
currentWeightUpdateElement.neuronWithWeight.weight = 0.0f;
currentWeightUpdateElement = currentWeightUpdateElement.parent;
}
// we need to unlabel all connections which got already adapted
currentWeightUpdateElement = currentWeightChangeTreeElement;
for (;;)
{
if (/*unnecessary currentWeightUpdateElement == null ||*/ currentWeightUpdateElement.isRoot)
{
break;
}
var connectionTuple = new Tuple <uint, uint>(currentWeightUpdateElement.neuronWithWeight.source.neuronIndex, currentWeightUpdateElement.neuronWithWeight.target.neuronIndex);
Debug.Assert(globalConnectionNeuronIndices.Contains(connectionTuple));
globalConnectionNeuronIndices.Remove(connectionTuple);
currentWeightUpdateElement = currentWeightUpdateElement.parent;
}
}
// push all children for depth-first-search
foreach (var iTreeChildren in topStackElement.treeElement.children)
{
stack.push(DepthFirstSearchStackElement.make(iTreeChildren));
}
}
}
public static WeightChangeTreeElement make(SlimRnnNeuronWithWeight neuronWithWeight, uint weightWithPropabilityTableIndex, WeightChangeTreeElement parent = null)
{
return(new WeightChangeTreeElement(false, neuronWithWeight, weightWithPropabilityTableIndex, parent));
}
public static DepthFirstSearchStackElement make(WeightChangeTreeElement traceElement)
{
return(new DepthFirstSearchStackElement(traceElement));//, false);
}
public readonly WeightChangeTreeElement treeElement; // not complete trace
private DepthFirstSearchStackElement(WeightChangeTreeElement treeElement /*, bool isRoot*/)
{
this.treeElement = treeElement;
//this.isRoot = isRoot;
}
