private void init() {
if (rng == null) rng = new MersenneTwister(123);
MultiDimensionalSet<String, String> binaryProductions = MultiDimensionalSet.hashSet();
if (simplifiedModel) {
binaryProductions.add("", "");
} else {
// TODO
// figure out what binary productions we have in these trees
// Note: the current sentiment training data does not actually
// have any constituent labels
throw new UnsupportedOperationException("Not yet implemented");
}
Set<String> unaryProductions = new HashSet<>();
if (simplifiedModel) {
unaryProductions.add("");
} else {
// TODO
// figure out what unary productions we have in these trees (preterminals only, after the
// collapsing)
throw new UnsupportedOperationException("Not yet implemented");
}
identity = FloatMatrix.eye(numHidden);
binaryTransform = MultiDimensionalMap.newTreeBackedMap();
binaryFloatTensors = MultiDimensionalMap.newTreeBackedMap();
binaryClassification = MultiDimensionalMap.newTreeBackedMap();
// When making a flat model (no semantic untying) the
// basicCategory function will return the same basic category for
// all labels, so all entries will map to the same matrix
for (Pair<String, String> binary : binaryProductions) {
String left = basicCategory(binary.getFirst());
String right = basicCategory(binary.getSecond());
if (binaryTransform.contains(left, right)) {
continue;
}
binaryTransform.put(left, right, randomTransformMatrix());
if (useFloatTensors) {
binaryFloatTensors.put(left, right, randomBinaryFloatTensor());
}
if (!combineClassification) {
binaryClassification.put(left, right, randomClassificationMatrix());
}
}
numBinaryMatrices = binaryTransform.size();
binaryTransformSize = numHidden * (2 * numHidden + 1);
if (useFloatTensors) {
binaryFloatTensorSize = numHidden * numHidden * numHidden * 4;
} else {
binaryFloatTensorSize = 0;
}
binaryClassificationSize = (combineClassification) ? 0 : numOuts * (numHidden + 1);
unaryClassification = new TreeMap<>();
// When making a flat model (no semantic untying) the
// basicCategory function will return the same basic category for
// all labels, so all entries will map to the same matrix
for (String unary : unaryProductions) {
unary = basicCategory(unary);
if (unaryClassification.containsKey(unary)) {
continue;
}
unaryClassification.put(unary, randomClassificationMatrix());
}
binaryClassificationSize = (combineClassification) ? 0 : numOuts * (numHidden + 1);
numUnaryMatrices = unaryClassification.size();
unaryClassificationSize = numOuts * (numHidden + 1);
featureVectors.put(UNKNOWN_FEATURE, randomWordVector());
numUnaryMatrices = unaryClassification.size();
unaryClassificationSize = numOuts * (numHidden + 1);
classWeights = new HashMap<>();
}
public FloatMatrix getValueGradient(int iterations) {
// We use TreeMap for each of these so that they stay in a
// canonical sorted order
// TODO: factor out the initialization routines
// binaryTD stands for Transform Derivatives
final MultiDimensionalMap<String, String, FloatMatrix> binaryTD =
MultiDimensionalMap.newTreeBackedMap();
// the derivatives of the FloatTensors for the binary nodes
final MultiDimensionalMap<String, String, FloatTensor> binaryFloatTensorTD =
MultiDimensionalMap.newTreeBackedMap();
// binaryCD stands for Classification Derivatives
final MultiDimensionalMap<String, String, FloatMatrix> binaryCD =
MultiDimensionalMap.newTreeBackedMap();
// unaryCD stands for Classification Derivatives
final Map<String, FloatMatrix> unaryCD = new TreeMap<>();
// word vector derivatives
final Map<String, FloatMatrix> wordVectorD = new TreeMap<>();
for (MultiDimensionalMap.Entry<String, String, FloatMatrix> entry :
binaryTransform.entrySet()) {
int numRows = entry.getValue().rows;
int numCols = entry.getValue().columns;
binaryTD.put(entry.getFirstKey(), entry.getSecondKey(), new FloatMatrix(numRows, numCols));
}
if (!combineClassification) {
for (MultiDimensionalMap.Entry<String, String, FloatMatrix> entry :
binaryClassification.entrySet()) {
int numRows = entry.getValue().rows;
int numCols = entry.getValue().columns;
binaryCD.put(entry.getFirstKey(), entry.getSecondKey(), new FloatMatrix(numRows, numCols));
}
}
if (useFloatTensors) {
for (MultiDimensionalMap.Entry<String, String, FloatTensor> entry :
binaryFloatTensors.entrySet()) {
int numRows = entry.getValue().rows();
int numCols = entry.getValue().columns;
int numSlices = entry.getValue().slices();
binaryFloatTensorTD.put(
entry.getFirstKey(),
entry.getSecondKey(),
new FloatTensor(numRows, numCols, numSlices));
}
}
for (Map.Entry<String, FloatMatrix> entry : unaryClassification.entrySet()) {
int numRows = entry.getValue().rows;
int numCols = entry.getValue().columns;
unaryCD.put(entry.getKey(), new FloatMatrix(numRows, numCols));
}
for (Map.Entry<String, FloatMatrix> entry : featureVectors.entrySet()) {
int numRows = entry.getValue().rows;
int numCols = entry.getValue().columns;
wordVectorD.put(entry.getKey(), new FloatMatrix(numRows, numCols));
}
final List<Tree> forwardPropTrees = new CopyOnWriteArrayList<>();
Parallelization.iterateInParallel(
trainingTrees,
new Parallelization.RunnableWithParams<Tree>() {
public void run(Tree currentItem, Object[] args) {
Tree trainingTree = new Tree(currentItem);
trainingTree.connect(new ArrayList<>(currentItem.children()));
// this will attach the error vectors and the node vectors
// to each node in the tree
forwardPropagateTree(trainingTree);
forwardPropTrees.add(trainingTree);
}
},
rnTnActorSystem);
// TODO: we may find a big speedup by separating the derivatives and then summing
final AtomicDouble error = new AtomicDouble(0);
Parallelization.iterateInParallel(
forwardPropTrees,
new Parallelization.RunnableWithParams<Tree>() {
public void run(Tree currentItem, Object[] args) {
backpropDerivativesAndError(
currentItem, binaryTD, binaryCD, binaryFloatTensorTD, unaryCD, wordVectorD);
error.addAndGet(currentItem.errorSum());
}
},
new Parallelization.RunnableWithParams<Tree>() {
public void run(Tree currentItem, Object[] args) {}
},
rnTnActorSystem,
new Object[] {binaryTD, binaryCD, binaryFloatTensorTD, unaryCD, wordVectorD});
// scale the error by the number of sentences so that the
// regularization isn't drowned out for large training batchs
float scale = (1.0f / trainingTrees.size());
value = error.floatValue() * scale;
value += scaleAndRegularize(binaryTD, binaryTransform, scale, regTransformMatrix);
value += scaleAndRegularize(binaryCD, binaryClassification, scale, regClassification);
value +=
scaleAndRegularizeFloatTensor(
binaryFloatTensorTD, binaryFloatTensors, scale, regTransformFloatTensor);
value += scaleAndRegularize(unaryCD, unaryClassification, scale, regClassification);
value += scaleAndRegularize(wordVectorD, featureVectors, scale, regWordVector);
FloatMatrix derivative =
MatrixUtil.toFlattenedFloat(
getNumParameters(),
binaryTD.values().iterator(),
binaryCD.values().iterator(),
binaryFloatTensorTD.values().iterator(),
unaryCD.values().iterator(),
wordVectorD.values().iterator());
if (paramAdaGrad == null) paramAdaGrad = new AdaGradFloat(1, derivative.columns);
derivative.muli(paramAdaGrad.getLearningRates(derivative));
return derivative;
}
public INDArray getValueGradient(final List<Tree> trainingBatch) {
// We use TreeMap for each of these so that they stay in a
// canonical sorted order
// TODO: factor out the initialization routines
// binaryTD stands for Transform Derivatives
final MultiDimensionalMap<String, String, INDArray> binaryTD =
MultiDimensionalMap.newTreeBackedMap();
// the derivatives of the INd4j for the binary nodes
final MultiDimensionalMap<String, String, INDArray> binaryINDArrayTD =
MultiDimensionalMap.newTreeBackedMap();
// binaryCD stands for Classification Derivatives
final MultiDimensionalMap<String, String, INDArray> binaryCD =
MultiDimensionalMap.newTreeBackedMap();
// unaryCD stands for Classification Derivatives
final Map<String, INDArray> unaryCD = new TreeMap<>();
// word vector derivatives
final Map<String, INDArray> wordVectorD = new TreeMap<>();
for (MultiDimensionalMap.Entry<String, String, INDArray> entry : binaryTransform.entrySet()) {
int numRows = entry.getValue().rows();
int numCols = entry.getValue().columns();
binaryTD.put(entry.getFirstKey(), entry.getSecondKey(), Nd4j.create(numRows, numCols));
}
if (!combineClassification) {
for (MultiDimensionalMap.Entry<String, String, INDArray> entry :
binaryClassification.entrySet()) {
int numRows = entry.getValue().rows();
int numCols = entry.getValue().columns();
binaryCD.put(entry.getFirstKey(), entry.getSecondKey(), Nd4j.create(numRows, numCols));
}
}
if (useDoubleTensors) {
for (MultiDimensionalMap.Entry<String, String, INDArray> entry : binaryTensors.entrySet()) {
int numRows = entry.getValue().size(1);
int numCols = entry.getValue().size(2);
int numSlices = entry.getValue().slices();
binaryINDArrayTD.put(
entry.getFirstKey(), entry.getSecondKey(), Nd4j.create(numRows, numCols, numSlices));
}
}
for (Map.Entry<String, INDArray> entry : unaryClassification.entrySet()) {
int numRows = entry.getValue().rows();
int numCols = entry.getValue().columns();
unaryCD.put(entry.getKey(), Nd4j.create(numRows, numCols));
}
for (String s : vocabCache.words()) {
INDArray vector = featureVectors.vector(s);
int numRows = vector.rows();
int numCols = vector.columns();
wordVectorD.put(s, Nd4j.create(numRows, numCols));
}
final List<Tree> forwardPropTrees = new CopyOnWriteArrayList<>();
// if(!forwardPropTrees.isEmpty())
Parallelization.iterateInParallel(
trainingBatch,
new Parallelization.RunnableWithParams<Tree>() {
public void run(Tree currentItem, Object[] args) {
Tree trainingTree = new Tree(currentItem);
trainingTree.connect(new ArrayList<>(currentItem.children()));
// this will attach the error vectors and the node vectors
// to each node in the tree
forwardPropagateTree(trainingTree);
forwardPropTrees.add(trainingTree);
}
},
rnTnActorSystem);
// TODO: we may find a big speedup by separating the derivatives and then summing
final AtomicDouble error = new AtomicDouble(0);
if (!forwardPropTrees.isEmpty())
Parallelization.iterateInParallel(
forwardPropTrees,
new Parallelization.RunnableWithParams<Tree>() {
public void run(Tree currentItem, Object[] args) {
backpropDerivativesAndError(
currentItem, binaryTD, binaryCD, binaryINDArrayTD, unaryCD, wordVectorD);
error.addAndGet(currentItem.errorSum());
}
},
new Parallelization.RunnableWithParams<Tree>() {
public void run(Tree currentItem, Object[] args) {}
},
rnTnActorSystem,
new Object[] {binaryTD, binaryCD, binaryINDArrayTD, unaryCD, wordVectorD});
// scale the error by the number of sentences so that the
// regularization isn't drowned out for large training batchs
double scale =
trainingBatch == null || trainingBatch.isEmpty() ? 1.0f : (1.0f / trainingBatch.size());
value = error.doubleValue() * scale;
value += scaleAndRegularize(binaryTD, binaryTransform, scale, regTransformMatrix);
value += scaleAndRegularize(binaryCD, binaryClassification, scale, regClassification);
value +=
scaleAndRegularizeINDArray(binaryINDArrayTD, binaryTensors, scale, regTransformINDArray);
value += scaleAndRegularize(unaryCD, unaryClassification, scale, regClassification);
value += scaleAndRegularize(wordVectorD, featureVectors, scale, regWordVector);
INDArray derivative =
Nd4j.toFlattened(
getNumParameters(),
binaryTD.values().iterator(),
binaryCD.values().iterator(),
binaryINDArrayTD.values().iterator(),
unaryCD.values().iterator(),
wordVectorD.values().iterator());
if (derivative.length() != numParameters)
throw new IllegalStateException(
"Gradient has wrong number of parameters "
+ derivative.length()
+ " should have been "
+ numParameters);
if (paramAdaGrad == null) paramAdaGrad = new AdaGrad(1, derivative.columns());
derivative = paramAdaGrad.getGradient(derivative, 0);
return derivative;
}