private void computeDir(double[] dir, double[] fg) throws SQNMinimizer.SurpriseConvergence {
System.arraycopy(fg, 0, dir, 0, fg.length);
int mmm = sList.size();
double[] as = new double[mmm];
double[] factors = new double[dir.length];
for (int i = mmm - 1; i >= 0; i--) {
as[i] = roList.get(i) * ArrayMath.innerProduct(sList.get(i), dir);
plusAndConstMult(dir, yList.get(i), -as[i], dir);
}
// multiply by hessian approximation
if (mmm != 0) {
double[] y = yList.get(mmm - 1);
double yDotY = ArrayMath.innerProduct(y, y);
if (yDotY == 0) {
throw new SQNMinimizer.SurpriseConvergence("Y is 0!!");
}
double gamma = ArrayMath.innerProduct(sList.get(mmm - 1), y) / yDotY;
ArrayMath.multiplyInPlace(dir, gamma);
} else if (mmm == 0) {
// This is a safety feature preventing too large of an initial step (see Yu Schraudolph
// Gunter)
ArrayMath.multiplyInPlace(dir, epsilon);
}
for (int i = 0; i < mmm; i++) {
double b = roList.get(i) * ArrayMath.innerProduct(yList.get(i), dir);
plusAndConstMult(dir, sList.get(i), cPosDef * as[i] - b, dir);
plusAndConstMult(ArrayMath.pairwiseMultiply(yList.get(i), sList.get(i)), factors, 1, factors);
}
ArrayMath.multiplyInPlace(dir, -1);
}
@Override
public double[] derivativeAt(double[] flatCoefs) {
double[] g = new double[model.flatIDsize()];
model.setCoefsFromFlat(flatCoefs);
for (ModelSentence s : mSentences) {
model.computeGradient(s, g);
}
ArrayMath.multiplyInPlace(g, -1);
addL2regularizerGradient(g, flatCoefs);
return g;
}
// fill value & derivative
public void calculate(double[] theta) {
dvModel.vectorToParams(theta);
double localValue = 0.0;
double[] localDerivative = new double[theta.length];
TwoDimensionalMap<String, String, SimpleMatrix> binaryW_dfsG, binaryW_dfsB;
binaryW_dfsG = TwoDimensionalMap.treeMap();
binaryW_dfsB = TwoDimensionalMap.treeMap();
TwoDimensionalMap<String, String, SimpleMatrix> binaryScoreDerivativesG,
binaryScoreDerivativesB;
binaryScoreDerivativesG = TwoDimensionalMap.treeMap();
binaryScoreDerivativesB = TwoDimensionalMap.treeMap();
Map<String, SimpleMatrix> unaryW_dfsG, unaryW_dfsB;
unaryW_dfsG = new TreeMap<>();
unaryW_dfsB = new TreeMap<>();
Map<String, SimpleMatrix> unaryScoreDerivativesG, unaryScoreDerivativesB;
unaryScoreDerivativesG = new TreeMap<>();
unaryScoreDerivativesB = new TreeMap<>();
Map<String, SimpleMatrix> wordVectorDerivativesG = new TreeMap<>();
Map<String, SimpleMatrix> wordVectorDerivativesB = new TreeMap<>();
for (TwoDimensionalMap.Entry<String, String, SimpleMatrix> entry : dvModel.binaryTransform) {
int numRows = entry.getValue().numRows();
int numCols = entry.getValue().numCols();
binaryW_dfsG.put(
entry.getFirstKey(), entry.getSecondKey(), new SimpleMatrix(numRows, numCols));
binaryW_dfsB.put(
entry.getFirstKey(), entry.getSecondKey(), new SimpleMatrix(numRows, numCols));
binaryScoreDerivativesG.put(
entry.getFirstKey(), entry.getSecondKey(), new SimpleMatrix(1, numRows));
binaryScoreDerivativesB.put(
entry.getFirstKey(), entry.getSecondKey(), new SimpleMatrix(1, numRows));
}
for (Map.Entry<String, SimpleMatrix> entry : dvModel.unaryTransform.entrySet()) {
int numRows = entry.getValue().numRows();
int numCols = entry.getValue().numCols();
unaryW_dfsG.put(entry.getKey(), new SimpleMatrix(numRows, numCols));
unaryW_dfsB.put(entry.getKey(), new SimpleMatrix(numRows, numCols));
unaryScoreDerivativesG.put(entry.getKey(), new SimpleMatrix(1, numRows));
unaryScoreDerivativesB.put(entry.getKey(), new SimpleMatrix(1, numRows));
}
if (op.trainOptions.trainWordVectors) {
for (Map.Entry<String, SimpleMatrix> entry : dvModel.wordVectors.entrySet()) {
int numRows = entry.getValue().numRows();
int numCols = entry.getValue().numCols();
wordVectorDerivativesG.put(entry.getKey(), new SimpleMatrix(numRows, numCols));
wordVectorDerivativesB.put(entry.getKey(), new SimpleMatrix(numRows, numCols));
}
}
// Some optimization methods prints out a line without an end, so our
// debugging statements are misaligned
Timing scoreTiming = new Timing();
scoreTiming.doing("Scoring trees");
int treeNum = 0;
MulticoreWrapper<Tree, Pair<DeepTree, DeepTree>> wrapper =
new MulticoreWrapper<>(op.trainOptions.trainingThreads, new ScoringProcessor());
for (Tree tree : trainingBatch) {
wrapper.put(tree);
}
wrapper.join();
scoreTiming.done();
while (wrapper.peek()) {
Pair<DeepTree, DeepTree> result = wrapper.poll();
DeepTree goldTree = result.first;
DeepTree bestTree = result.second;
StringBuilder treeDebugLine = new StringBuilder();
Formatter formatter = new Formatter(treeDebugLine);
boolean isDone =
(Math.abs(bestTree.getScore() - goldTree.getScore()) <= 0.00001
|| goldTree.getScore() > bestTree.getScore());
String done = isDone ? "done" : "";
formatter.format(
"Tree %6d Highest tree: %12.4f Correct tree: %12.4f %s",
treeNum, bestTree.getScore(), goldTree.getScore(), done);
System.err.println(treeDebugLine.toString());
if (!isDone) {
// if the gold tree is better than the best hypothesis tree by
// a large enough margin, then the score difference will be 0
// and we ignore the tree
double valueDelta = bestTree.getScore() - goldTree.getScore();
// double valueDelta = Math.max(0.0, - scoreGold + bestScore);
localValue += valueDelta;
// get the context words for this tree - should be the same
// for either goldTree or bestTree
List<String> words = getContextWords(goldTree.getTree());
// The derivatives affected by this tree are only based on the
// nodes present in this tree, eg not all matrix derivatives
// will be affected by this tree
backpropDerivative(
goldTree.getTree(),
words,
goldTree.getVectors(),
binaryW_dfsG,
unaryW_dfsG,
binaryScoreDerivativesG,
unaryScoreDerivativesG,
wordVectorDerivativesG);
backpropDerivative(
bestTree.getTree(),
words,
bestTree.getVectors(),
binaryW_dfsB,
unaryW_dfsB,
binaryScoreDerivativesB,
unaryScoreDerivativesB,
wordVectorDerivativesB);
}
++treeNum;
}
double[] localDerivativeGood;
double[] localDerivativeB;
if (op.trainOptions.trainWordVectors) {
localDerivativeGood =
NeuralUtils.paramsToVector(
theta.length,
binaryW_dfsG.valueIterator(),
unaryW_dfsG.values().iterator(),
binaryScoreDerivativesG.valueIterator(),
unaryScoreDerivativesG.values().iterator(),
wordVectorDerivativesG.values().iterator());
localDerivativeB =
NeuralUtils.paramsToVector(
theta.length,
binaryW_dfsB.valueIterator(),
unaryW_dfsB.values().iterator(),
binaryScoreDerivativesB.valueIterator(),
unaryScoreDerivativesB.values().iterator(),
wordVectorDerivativesB.values().iterator());
} else {
localDerivativeGood =
NeuralUtils.paramsToVector(
theta.length,
binaryW_dfsG.valueIterator(),
unaryW_dfsG.values().iterator(),
binaryScoreDerivativesG.valueIterator(),
unaryScoreDerivativesG.values().iterator());
localDerivativeB =
NeuralUtils.paramsToVector(
theta.length,
binaryW_dfsB.valueIterator(),
unaryW_dfsB.values().iterator(),
binaryScoreDerivativesB.valueIterator(),
unaryScoreDerivativesB.values().iterator());
}
// correct - highest
for (int i = 0; i < localDerivativeGood.length; i++) {
localDerivative[i] = localDerivativeB[i] - localDerivativeGood[i];
}
// TODO: this is where we would combine multiple costs if we had parallelized the calculation
value = localValue;
derivative = localDerivative;
// normalizing by training batch size
value = (1.0 / trainingBatch.size()) * value;
ArrayMath.multiplyInPlace(derivative, (1.0 / trainingBatch.size()));
// add regularization to cost:
double[] currentParams = dvModel.paramsToVector();
double regCost = 0;
for (double currentParam : currentParams) {
regCost += currentParam * currentParam;
}
regCost = op.trainOptions.regCost * 0.5 * regCost;
value += regCost;
// add regularization to gradient
ArrayMath.multiplyInPlace(currentParams, op.trainOptions.regCost);
ArrayMath.pairwiseAddInPlace(derivative, currentParams);
}