/**
* This is the method to call for assigning labels and node vectors to the Tree. After calling
* this, each of the non-leaf nodes will have the node vector and the predictions of their classes
* assigned to that subtree's node.
*/
public void forwardPropagateTree(Tree tree) {
FloatMatrix nodeVector;
FloatMatrix classification;
if (tree.isLeaf()) {
// We do nothing for the leaves. The preterminals will
// calculate the classification for this word/tag. In fact, the
// recursion should not have gotten here (unless there are
// degenerate trees of just one leaf)
throw new AssertionError("We should not have reached leaves in forwardPropagate");
} else if (tree.isPreTerminal()) {
classification = getUnaryClassification(tree.label());
String word = tree.children().get(0).value();
FloatMatrix wordVector = getFeatureVector(word);
if (wordVector == null) {
wordVector = featureVectors.get(UNKNOWN_FEATURE);
}
nodeVector = activationFunction.apply(wordVector);
} else if (tree.children().size() == 1) {
throw new AssertionError(
"Non-preterminal nodes of size 1 should have already been collapsed");
} else if (tree.children().size() == 2) {
Tree left = tree.firstChild(), right = tree.lastChild();
forwardPropagateTree(left);
forwardPropagateTree(right);
String leftCategory = tree.children().get(0).label();
String rightCategory = tree.children().get(1).label();
FloatMatrix W = getBinaryTransform(leftCategory, rightCategory);
classification = getBinaryClassification(leftCategory, rightCategory);
FloatMatrix leftVector = tree.children().get(0).vector();
FloatMatrix rightVector = tree.children().get(1).vector();
FloatMatrix childrenVector = appendBias(leftVector, rightVector);
if (useFloatTensors) {
FloatTensor floatT = getBinaryFloatTensor(leftCategory, rightCategory);
FloatMatrix floatTensorIn = FloatMatrix.concatHorizontally(leftVector, rightVector);
FloatMatrix floatTensorOut = floatT.bilinearProducts(floatTensorIn);
nodeVector = activationFunction.apply(W.mmul(childrenVector).add(floatTensorOut));
} else nodeVector = activationFunction.apply(W.mmul(childrenVector));
} else {
throw new AssertionError("Tree not correctly binarized");
}
FloatMatrix inputWithBias = appendBias(nodeVector);
FloatMatrix preAct = classification.mmul(inputWithBias);
FloatMatrix predictions = outputActivation.apply(preAct);
tree.setPrediction(predictions);
tree.setVector(nodeVector);
}
private FloatTensor getFloatTensorGradient(
FloatMatrix deltaFull, FloatMatrix leftVector, FloatMatrix rightVector) {
int size = deltaFull.length;
FloatTensor Wt_df = new FloatTensor(size * 2, size * 2, size);
FloatMatrix fullVector = FloatMatrix.concatHorizontally(leftVector, rightVector);
for (int slice = 0; slice < size; ++slice) {
Wt_df.setSlice(
slice, SimpleBlas.scal(deltaFull.get(slice), fullVector).mmul(fullVector.transpose()));
}
return Wt_df;
}
float scaleAndRegularizeFloatTensor(
MultiDimensionalMap<String, String, FloatTensor> derivatives,
MultiDimensionalMap<String, String, FloatTensor> currentMatrices,
float scale,
float regCost) {
float cost = 0.0f; // the regularization cost
for (MultiDimensionalMap.Entry<String, String, FloatTensor> entry :
currentMatrices.entrySet()) {
FloatTensor D = derivatives.get(entry.getFirstKey(), entry.getSecondKey());
D = D.scale(scale).add(entry.getValue().scale(regCost));
derivatives.put(entry.getFirstKey(), entry.getSecondKey(), D);
cost += entry.getValue().mul(entry.getValue()).sum() * regCost / 2.0;
}
return cost;
}
private FloatMatrix computeFloatTensorDeltaDown(
FloatMatrix deltaFull,
FloatMatrix leftVector,
FloatMatrix rightVector,
FloatMatrix W,
FloatTensor Wt) {
FloatMatrix WTDelta = W.transpose().mmul(deltaFull);
FloatMatrix WTDeltaNoBias = WTDelta.get(interval(0, 1), interval(0, deltaFull.rows * 2));
int size = deltaFull.length;
FloatMatrix deltaFloatTensor = new FloatMatrix(size * 2, 1);
FloatMatrix fullVector = FloatMatrix.concatHorizontally(leftVector, rightVector);
for (int slice = 0; slice < size; ++slice) {
FloatMatrix scaledFullVector = SimpleBlas.scal(deltaFull.get(slice), fullVector);
deltaFloatTensor =
deltaFloatTensor.add(
Wt.getSlice(slice).add(Wt.getSlice(slice).transpose()).mmul(scaledFullVector));
}
return deltaFloatTensor.add(WTDeltaNoBias);
}
FloatTensor randomBinaryFloatTensor() {
float range = 1.0f / (4.0f * numHidden);
FloatTensor floatTensor =
FloatTensor.rand(numHidden * 2, numHidden * 2, numHidden, -range, range, rng);
return floatTensor.scale(scalingForInit);
}
