/** @brief Print the property information for this node. Recursively look at
* children also, but only the primary child - siblings are not looked at. */
void SGFNode::printInfo() const
{
LogWriter log;
set<SGFProperty, less<SGFProperty> >::const_iterator citer;
citer = properties.begin();
while(citer!=properties.end()) {
log.print(citer->getName());
log.print(" = ");
vector<string> v = citer->getValues();
for(int i=0;i<v.size();i++) {
log.print(v.at(i));
log.print(" ");
}
log.print("\n");
citer++;
}
/** @todo Print info on siblings also... */
if(child!=NULL)
child->printInfo();
}
void BPN::completenessTest(NNDatabase& db)
{
LogWriter log;
string message;
char buffer[50];
log.println("Running training completeness test.");
vector<Matrix<float> >& input = db.getTrainingInput();
vector<Matrix<float> >& output = db.getTrainingOutput();
int correct = 0;
for(int i=0;i<input.size();i++)
{
getAnswer(input[i]);
if(outputs[outputs.size()-1]==output[i])
correct++;
//delete bpnOutput;
if(i%100==0)
{
log.print(".");
}
}
log.print("\n");
sprintf(buffer, "%d", correct);
message+=buffer;
message+= " correct out of ";
sprintf(buffer, "%d", input.size());
message+=buffer;
log.println(message);
int percent = ((float)correct/(float)input.size())*(float)100;
sprintf(buffer, "%d", percent);
message+=buffer;
message+="% correct";
log.println(message);
log.println("Completeness test finished.");
}
/** Extract all training pairs from an SGF file using the given
SGFReader object and add them to the given NNDatabase.
@params sgf An SGFReader object to use to read the file it
represents.
@params database The NNDatabase to store the training pairs in. */
void Urgency3BPNGoTrainer::extractTrainingPairs(SGFReader* sgf, NNDatabase* database,
int movesFrom /*=0*/, int movesTo /*=0*/, int lookahead /*=0*/, bool quiet /*=false*/) const
{
// at each step in sgf file, score current move with current urgency net
// then score next move of same colour
// if first move doesn't score at least 0.2 higher than second
// add as training pairs:
// 1. First move output should be second move score +0.2 (0.9 max)
// 2. Second move output should be first move score -0.2 (0.1 min)
LogWriter log;
string message;
//char buffer[50];
if(movesFrom > movesTo)
{
log.println("Start move is greater than end move.");
return;
}
vector<Move> moves;
sgf->getTree().getAllPrimaryMoves(moves);
if(moves.size()==0)
return;
// if file already in the database skip it
if(!database->addSignature(sgf->getSignature()))
{
log.println("File already in database.");
return;
}
int size = 19;
string v;
if(sgf->getBoardSize(v))
size = atoi(v.c_str());
if(size>19 || size<5)
{
message = "Boardsize too small or large to handle: ";
message+=v;
log.println(message);
return;
}
BoardStruct board(size);
vector<Move> futureMoves;
int moveNumber=0;
string c;
Matrix<float> currentInput(1, goAdapter->getBPN().getWeights()[0].getHeight());
Matrix<float> nextInput(1, goAdapter->getBPN().getWeights()[0].getHeight());
Matrix<float> output(1, 1);
Matrix<float>* answers;
// vector<Matrix<float> > temp(goAdapter->getBPN().getNumberOfLayers());
vector<vector<float> > tv;
tv.resize(1);
tv[0].resize(1);
// colour of new move
int colour;
//sgf->initBoard(board);
board.clear();
// add setup points
setupBoardFromSGF(*sgf, board);
/* vector<Move> props;
if(sgf->getRootNode()->getEmptySetup(props))
{
for(int i=0;i<props.size();i++)
board.setPoint(props[i].getX(), props[i].getY(), EMPTY, false);
}
if(sgf->getRootNode()->getBlackSetup(props))
{
for(int i=0;i<props.size();i++)
board.setPoint(props[i].getX(), props[i].getY(), BLACK, false);
}
if(sgf->getRootNode()->getWhiteSetup(props))
{
for(int i=0;i<props.size();i++)
board.setPoint(props[i].getX(), props[i].getY(), WHITE, false);
} */
SGFNode* nextNode = &(sgf->getRootNode());
bool useThisMove = true;
bool currentIsMoveA = true;
bool getInputSuccess = false;
// continue until a null node forces the loop to break
while (true) {
// check bounds if necessary
if(movesTo!=0) {
// use ply instead of individual moves
if(moveNumber/2 >= movesTo) break;
else if(moveNumber/2 < movesFrom) useThisMove = false;
else useThisMove = true;
}
if(movesTo==0 || useThisMove) {
nextNode = nextNode->getChild();
if(nextNode==NULL)
break;
// determine colour of move
vector<string> vs;
if (nextNode->getProperty(SGFProperty::blackMoveTag, vs)) {
colour = BLACK;
c = vs[0];
}
else if (nextNode->getProperty(SGFProperty::whiteMoveTag, vs)) {
colour = WHITE;
c = vs[0];
}
else break;
// find our next move to compare with
// check
futureMoves.clear();
// NOTE: The first move returned by getLookaheadMoves() is
// always the current one, so we need 2 lookahead moves
nextNode->getLookaheadMoves(2, colour, futureMoves);
if(futureMoves.size()==2 && Move::SGFToX(c)!=-1 && Move::SGFToY(c)!=-1
&& futureMoves[1].getX()!=-1 && futureMoves[1].getY()!=-1) {
// At each step in sgf file, score current move with current urgency net
// then score next move of same colour
// if first move doesn't score at least 0.2 higher than second
// add as training pairs:
// 1. First move output should be second move score +0.2 (0.9 max)
// 2. Second move output should be first move score -0.2 (0.1 min)
// get current move score
goAdapter->getInput(Move::SGFToX(c), Move::SGFToY(c), board, currentInput, colour);
goAdapter->getBPN().getAnswer(currentInput);
answers = &(goAdapter->getBPN().getOutputs()[goAdapter->getBPN().getNumberOfLayers()-1]);
float currentMoveScore = answers->getValue(0, 0);
// get next move score
goAdapter->getInput(futureMoves[1].getX(), futureMoves[1].getY(), board, nextInput, colour);
goAdapter->getBPN().getAnswer(nextInput);
answers = &(goAdapter->getBPN().getOutputs()[goAdapter->getBPN().getNumberOfLayers()-1]);
float nextMoveScore = answers->getValue(0, 0);
// if current scores higher than next move by 0.2
// don't train otherwise do train
if(currentMoveScore<=(nextMoveScore+0.2)) {
if((nextMoveScore+0.2)>0.9)
tv[0][0] = 0.9f;
else
tv[0][0] = nextMoveScore+0.2;
output.setValues(tv);
database->addTrainingPair(¤tInput, &output);
if((currentMoveScore-0.2)<0.1)
tv[0][0] = 0.1f;
else
tv[0][0] = currentMoveScore-0.2;
output.setValues(tv);
database->addTrainingPair(&nextInput, &output);
}
}
} // end if(movesTo==0 || useThisMove)
board.setPoint(Move::SGFToX(c), Move::SGFToY(c), colour);
if(!quiet)
log.print(".");
moveNumber++;
// save after every board position has been
// analysed and moves extracted
// database.save();
} // end while(true)
if(!quiet)
log.print("\n");
//database.save();
} // end extractTrainingPairs
/** Print useful information about this object. In this case,
output all the weights for each layer. */
void BPN::printInfo(bool printWeights /* = true */) const
{
LogWriter log;
char buffer[50];
sprintf(buffer, "Filename: %s", filename.c_str());
log.println(buffer);
sprintf(buffer, "Save version: %d", saveVersion);
log.println(buffer);
sprintf(buffer, "Type: %s", typeToString(id));
log.println(buffer);
sprintf(buffer, "Learning rate: %*g", 7, learningRate);
log.println(buffer);
sprintf(buffer, "Momentum: %*g", 7, momentum);
log.println(buffer);
sprintf(buffer, "Epochs completed: %d", epochsCompleted);
log.println(buffer);
sprintf(buffer, "Patterns completed: %g", patternsCompleted);
log.println(buffer);
sprintf(buffer, "Last pattern test: %g", lastPatternTest);
log.println(buffer);
if(dynamicLearningRate)
sprintf(buffer, "Dynamic learning rate: True");
else
sprintf(buffer, "Dynamic learning rate: False");
log.println(buffer);
if(dynamicMomentum)
sprintf(buffer, "Dynamic momentum: True");
else
sprintf(buffer, "Dynamic momentum: False");
log.println(buffer);
if(inputFieldShape==IFS_SQUARE)
log.println("Input field shape: IFS_SQUARE");
else if(inputFieldShape==IFS_DIAMOND)
log.println("Input field shape: IFS_DIAMOND");
else
log.println("Input field shape: UNKNOWN");
// output neurons info
sprintf(buffer, "Input neurons: %d", weights[0].getHeight());
log.println(buffer);
for(int i=1;i<weights.size();i++)
{
sprintf(buffer, "Hidden neurons(%d): %d", i-1, weights[i].getHeight());
log.println(buffer);
}
sprintf(buffer, "Output neurons: %d", weights[weights.size()-1].getWidth());
log.println(buffer);
if(printWeights)
{
for(int i=0;i<weights.size();i++)
{
sprintf(buffer, "Weights for layer %d:", i);
log.println(buffer);
getWeights()[i].printInfo();
}
for(i=0;i<biasWeights.size();i++)
{
sprintf(buffer, "Bias weights for layer %d:", i);
log.println(buffer);
biasWeights[i].printInfo();
}
}
// print rank test values
// if(printTestResults)
// {
// log.print("Rank test values: ");
// for(i=0;i<rankTestValue.size();i++)
// {
// sprintf(buffer, "%d", rankTestEpoch[i]);
// message+=buffer;
// message+="[";
// sprintf(buffer, "%*g", 9, rankTestValue[i]);
// message+=buffer;
// message+="] ";
// log.print(message);
// }
// }
log.print("\n");
}
