public void vectorToParams(double[] theta) {
NeuralUtils.vectorToParams(
theta,
binaryTransform.valueIterator(),
binaryClassification.valueIterator(),
SimpleTensor.iteratorSimpleMatrix(binaryTensors.valueIterator()),
unaryClassification.values().iterator(),
wordVectors.values().iterator());
}
public double[] paramsToVector() {
int totalSize = totalParamSize();
return NeuralUtils.paramsToVector(
totalSize,
binaryTransform.valueIterator(),
binaryClassification.valueIterator(),
SimpleTensor.iteratorSimpleMatrix(binaryTensors.valueIterator()),
unaryClassification.values().iterator(),
wordVectors.values().iterator());
}
private SimpleMatrix concatenateContextWords(
SimpleMatrix childVec, IntPair span, List<String> words) {
// TODO: factor out getting the words
SimpleMatrix left =
(span.getSource() < 0)
? dvModel.getStartWordVector()
: dvModel.getWordVector(words.get(span.getSource()));
SimpleMatrix right =
(span.getTarget() >= words.size())
? dvModel.getEndWordVector()
: dvModel.getWordVector(words.get(span.getTarget()));
return NeuralUtils.concatenate(childVec, left, right);
}
public void backpropDerivative(
Tree tree,
List<String> words,
IdentityHashMap<Tree, SimpleMatrix> nodeVectors,
TwoDimensionalMap<String, String, SimpleMatrix> binaryW_dfs,
Map<String, SimpleMatrix> unaryW_dfs,
TwoDimensionalMap<String, String, SimpleMatrix> binaryScoreDerivatives,
Map<String, SimpleMatrix> unaryScoreDerivatives,
Map<String, SimpleMatrix> wordVectorDerivatives,
SimpleMatrix deltaUp) {
if (tree.isLeaf()) {
return;
}
if (tree.isPreTerminal()) {
if (op.trainOptions.trainWordVectors) {
String word = tree.children()[0].label().value();
word = dvModel.getVocabWord(word);
// SimpleMatrix currentVector = nodeVectors.get(tree);
// SimpleMatrix currentVectorDerivative =
// nonlinearityVectorToDerivative(currentVector);
// SimpleMatrix derivative = deltaUp.elementMult(currentVectorDerivative);
SimpleMatrix derivative = deltaUp;
wordVectorDerivatives.put(word, wordVectorDerivatives.get(word).plus(derivative));
}
return;
}
SimpleMatrix currentVector = nodeVectors.get(tree);
SimpleMatrix currentVectorDerivative =
NeuralUtils.elementwiseApplyTanhDerivative(currentVector);
SimpleMatrix scoreW = dvModel.getScoreWForNode(tree);
currentVectorDerivative = currentVectorDerivative.elementMult(scoreW.transpose());
// the delta that is used at the current nodes
SimpleMatrix deltaCurrent = deltaUp.plus(currentVectorDerivative);
SimpleMatrix W = dvModel.getWForNode(tree);
SimpleMatrix WTdelta = W.transpose().mult(deltaCurrent);
if (tree.children().length == 2) {
// TODO: RS: Change to the nice "getWForNode" setup?
String leftLabel = dvModel.basicCategory(tree.children()[0].label().value());
String rightLabel = dvModel.basicCategory(tree.children()[1].label().value());
binaryScoreDerivatives.put(
leftLabel,
rightLabel,
binaryScoreDerivatives.get(leftLabel, rightLabel).plus(currentVector.transpose()));
SimpleMatrix leftVector = nodeVectors.get(tree.children()[0]);
SimpleMatrix rightVector = nodeVectors.get(tree.children()[1]);
SimpleMatrix childrenVector = NeuralUtils.concatenateWithBias(leftVector, rightVector);
if (op.trainOptions.useContextWords) {
childrenVector = concatenateContextWords(childrenVector, tree.getSpan(), words);
}
SimpleMatrix W_df = deltaCurrent.mult(childrenVector.transpose());
binaryW_dfs.put(leftLabel, rightLabel, binaryW_dfs.get(leftLabel, rightLabel).plus(W_df));
// and then recurse
SimpleMatrix leftDerivative = NeuralUtils.elementwiseApplyTanhDerivative(leftVector);
SimpleMatrix rightDerivative = NeuralUtils.elementwiseApplyTanhDerivative(rightVector);
SimpleMatrix leftWTDelta = WTdelta.extractMatrix(0, deltaCurrent.numRows(), 0, 1);
SimpleMatrix rightWTDelta =
WTdelta.extractMatrix(deltaCurrent.numRows(), deltaCurrent.numRows() * 2, 0, 1);
backpropDerivative(
tree.children()[0],
words,
nodeVectors,
binaryW_dfs,
unaryW_dfs,
binaryScoreDerivatives,
unaryScoreDerivatives,
wordVectorDerivatives,
leftDerivative.elementMult(leftWTDelta));
backpropDerivative(
tree.children()[1],
words,
nodeVectors,
binaryW_dfs,
unaryW_dfs,
binaryScoreDerivatives,
unaryScoreDerivatives,
wordVectorDerivatives,
rightDerivative.elementMult(rightWTDelta));
} else if (tree.children().length == 1) {
String childLabel = dvModel.basicCategory(tree.children()[0].label().value());
unaryScoreDerivatives.put(
childLabel, unaryScoreDerivatives.get(childLabel).plus(currentVector.transpose()));
SimpleMatrix childVector = nodeVectors.get(tree.children()[0]);
SimpleMatrix childVectorWithBias = NeuralUtils.concatenateWithBias(childVector);
if (op.trainOptions.useContextWords) {
childVectorWithBias = concatenateContextWords(childVectorWithBias, tree.getSpan(), words);
}
SimpleMatrix W_df = deltaCurrent.mult(childVectorWithBias.transpose());
// System.out.println("unary backprop derivative for " + childLabel);
// System.out.println("Old transform:");
// System.out.println(unaryW_dfs.get(childLabel));
// System.out.println(" Delta:");
// System.out.println(W_df.scale(scale));
unaryW_dfs.put(childLabel, unaryW_dfs.get(childLabel).plus(W_df));
// and then recurse
SimpleMatrix childDerivative = NeuralUtils.elementwiseApplyTanhDerivative(childVector);
// SimpleMatrix childDerivative = childVector;
SimpleMatrix childWTDelta = WTdelta.extractMatrix(0, deltaCurrent.numRows(), 0, 1);
backpropDerivative(
tree.children()[0],
words,
nodeVectors,
binaryW_dfs,
unaryW_dfs,
binaryScoreDerivatives,
unaryScoreDerivatives,
wordVectorDerivatives,
childDerivative.elementMult(childWTDelta));
}
}
// 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);
}
private void forwardPropagateTree(
Tree tree,
List<String> words,
IdentityHashMap<Tree, SimpleMatrix> nodeVectors,
IdentityHashMap<Tree, Double> scores) {
if (tree.isLeaf()) {
return;
}
if (tree.isPreTerminal()) {
Tree wordNode = tree.children()[0];
String word = wordNode.label().value();
SimpleMatrix wordVector = dvModel.getWordVector(word);
wordVector = NeuralUtils.elementwiseApplyTanh(wordVector);
nodeVectors.put(tree, wordVector);
return;
}
for (Tree child : tree.children()) {
forwardPropagateTree(child, words, nodeVectors, scores);
}
// at this point, nodeVectors contains the vectors for all of
// the children of tree
SimpleMatrix childVec;
if (tree.children().length == 2) {
childVec =
NeuralUtils.concatenateWithBias(
nodeVectors.get(tree.children()[0]), nodeVectors.get(tree.children()[1]));
} else {
childVec = NeuralUtils.concatenateWithBias(nodeVectors.get(tree.children()[0]));
}
if (op.trainOptions.useContextWords) {
childVec = concatenateContextWords(childVec, tree.getSpan(), words);
}
SimpleMatrix W = dvModel.getWForNode(tree);
if (W == null) {
String error = "Could not find W for tree " + tree;
if (op.testOptions.verbose) {
System.err.println(error);
}
throw new NoSuchParseException(error);
}
SimpleMatrix currentVector = W.mult(childVec);
currentVector = NeuralUtils.elementwiseApplyTanh(currentVector);
nodeVectors.put(tree, currentVector);
SimpleMatrix scoreW = dvModel.getScoreWForNode(tree);
if (scoreW == null) {
String error = "Could not find scoreW for tree " + tree;
if (op.testOptions.verbose) {
System.err.println(error);
}
throw new NoSuchParseException(error);
}
double score = scoreW.dot(currentVector);
// score = NeuralUtils.sigmoid(score);
scores.put(tree, score);
// System.err.print(Double.toString(score)+" ");
}
static SimpleMatrix randomWordVector(int size, Random rand) {
return NeuralUtils.randomGaussian(size, 1, rand);
}
