double scaleAndRegularize(
MultiDimensionalMap<String, String, INDArray> derivatives,
MultiDimensionalMap<String, String, INDArray> currentMatrices,
double scale,
double regCost) {
double cost = 0.0f; // the regularization cost
for (MultiDimensionalMap.Entry<String, String, INDArray> entry : currentMatrices.entrySet()) {
INDArray D = derivatives.get(entry.getFirstKey(), entry.getSecondKey());
if (D.data().dataType() == DataBuffer.Type.DOUBLE)
D =
Nd4j.getBlasWrapper()
.scal(scale, D)
.addi(Nd4j.getBlasWrapper().scal(regCost, entry.getValue()));
else
D =
Nd4j.getBlasWrapper()
.scal((float) scale, D)
.addi(Nd4j.getBlasWrapper().scal((float) regCost, entry.getValue()));
derivatives.put(entry.getFirstKey(), entry.getSecondKey(), D);
cost +=
entry.getValue().mul(entry.getValue()).sum(Integer.MAX_VALUE).getDouble(0)
* regCost
/ 2.0;
}
return cost;
}
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;
}
double scaleAndRegularizeINDArray(
MultiDimensionalMap<String, String, INDArray> derivatives,
MultiDimensionalMap<String, String, INDArray> currentMatrices,
double scale,
double regCost) {
double cost = 0.0f; // the regularization cost
for (MultiDimensionalMap.Entry<String, String, INDArray> entry : currentMatrices.entrySet()) {
INDArray D = derivatives.get(entry.getFirstKey(), entry.getSecondKey());
D = D.muli(scale).add(entry.getValue().muli(regCost));
derivatives.put(entry.getFirstKey(), entry.getSecondKey(), D);
cost +=
entry.getValue().mul(entry.getValue()).sum(Integer.MAX_VALUE).getDouble(0)
* regCost
/ 2.0f;
}
return cost;
}
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;
}
private void backpropDerivativesAndError(
Tree tree,
MultiDimensionalMap<String, String, FloatMatrix> binaryTD,
MultiDimensionalMap<String, String, FloatMatrix> binaryCD,
MultiDimensionalMap<String, String, FloatTensor> binaryFloatTensorTD,
Map<String, FloatMatrix> unaryCD,
Map<String, FloatMatrix> wordVectorD,
FloatMatrix deltaUp) {
if (tree.isLeaf()) {
return;
}
FloatMatrix currentVector = tree.vector();
String category = tree.label();
category = basicCategory(category);
// Build a vector that looks like 0,0,1,0,0 with an indicator for the correct class
FloatMatrix goldLabel = new FloatMatrix(numOuts, 1);
int goldClass = tree.goldLabel();
if (goldClass >= 0) {
goldLabel.put(goldClass, 1.0f);
}
Float nodeWeight = classWeights.get(goldClass);
if (nodeWeight == null) nodeWeight = 1.0f;
FloatMatrix predictions = tree.prediction();
// If this is an unlabeled class, set deltaClass to 0. We could
// make this more efficient by eliminating various of the below
// calculations, but this would be the easiest way to handle the
// unlabeled class
FloatMatrix deltaClass =
goldClass >= 0
? SimpleBlas.scal(nodeWeight, predictions.sub(goldLabel))
: new FloatMatrix(predictions.rows, predictions.columns);
FloatMatrix localCD = deltaClass.mmul(appendBias(currentVector).transpose());
float error = -(MatrixFunctions.log(predictions).muli(goldLabel).sum());
error = error * nodeWeight;
tree.setError(error);
if (tree.isPreTerminal()) { // below us is a word vector
unaryCD.put(category, unaryCD.get(category).add(localCD));
String word = tree.children().get(0).label();
word = getVocabWord(word);
FloatMatrix currentVectorDerivative = activationFunction.apply(currentVector);
FloatMatrix deltaFromClass = getUnaryClassification(category).transpose().mmul(deltaClass);
deltaFromClass =
deltaFromClass.get(interval(0, numHidden), interval(0, 1)).mul(currentVectorDerivative);
FloatMatrix deltaFull = deltaFromClass.add(deltaUp);
wordVectorD.put(word, wordVectorD.get(word).add(deltaFull));
} else {
// Otherwise, this must be a binary node
String leftCategory = basicCategory(tree.children().get(0).label());
String rightCategory = basicCategory(tree.children().get(1).label());
if (combineClassification) {
unaryCD.put("", unaryCD.get("").add(localCD));
} else {
binaryCD.put(
leftCategory, rightCategory, binaryCD.get(leftCategory, rightCategory).add(localCD));
}
FloatMatrix currentVectorDerivative = activationFunction.applyDerivative(currentVector);
FloatMatrix deltaFromClass =
getBinaryClassification(leftCategory, rightCategory).transpose().mmul(deltaClass);
FloatMatrix mult = deltaFromClass.get(interval(0, numHidden), interval(0, 1));
deltaFromClass = mult.muli(currentVectorDerivative);
FloatMatrix deltaFull = deltaFromClass.add(deltaUp);
FloatMatrix leftVector = tree.children().get(0).vector();
FloatMatrix rightVector = tree.children().get(1).vector();
FloatMatrix childrenVector = appendBias(leftVector, rightVector);
// deltaFull 50 x 1, childrenVector: 50 x 2
FloatMatrix add = binaryTD.get(leftCategory, rightCategory);
FloatMatrix W_df = deltaFromClass.mmul(childrenVector.transpose());
binaryTD.put(leftCategory, rightCategory, add.add(W_df));
FloatMatrix deltaDown;
if (useFloatTensors) {
FloatTensor Wt_df = getFloatTensorGradient(deltaFull, leftVector, rightVector);
binaryFloatTensorTD.put(
leftCategory,
rightCategory,
binaryFloatTensorTD.get(leftCategory, rightCategory).add(Wt_df));
deltaDown =
computeFloatTensorDeltaDown(
deltaFull,
leftVector,
rightVector,
getBinaryTransform(leftCategory, rightCategory),
getBinaryFloatTensor(leftCategory, rightCategory));
} else {
deltaDown = getBinaryTransform(leftCategory, rightCategory).transpose().mmul(deltaFull);
}
FloatMatrix leftDerivative = activationFunction.apply(leftVector);
FloatMatrix rightDerivative = activationFunction.apply(rightVector);
FloatMatrix leftDeltaDown = deltaDown.get(interval(0, deltaFull.rows), interval(0, 1));
FloatMatrix rightDeltaDown =
deltaDown.get(interval(deltaFull.rows, deltaFull.rows * 2), interval(0, 1));
backpropDerivativesAndError(
tree.children().get(0),
binaryTD,
binaryCD,
binaryFloatTensorTD,
unaryCD,
wordVectorD,
leftDerivative.mul(leftDeltaDown));
backpropDerivativesAndError(
tree.children().get(1),
binaryTD,
binaryCD,
binaryFloatTensorTD,
unaryCD,
wordVectorD,
rightDerivative.mul(rightDeltaDown));
}
}
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<>();
}
private void backpropDerivativesAndError(
Tree tree,
MultiDimensionalMap<String, String, INDArray> binaryTD,
MultiDimensionalMap<String, String, INDArray> binaryCD,
MultiDimensionalMap<String, String, INDArray> binaryINDArrayTD,
Map<String, INDArray> unaryCD,
Map<String, INDArray> wordVectorD,
INDArray deltaUp) {
if (tree.isLeaf()) {
return;
}
INDArray currentVector = tree.vector();
String category = tree.label();
category = basicCategory(category);
// Build a vector that looks like 0,0,1,0,0 with an indicator for the correct class
INDArray goldLabel = Nd4j.create(numOuts, 1);
int goldClass = tree.goldLabel();
if (goldClass >= 0) {
assert goldClass <= numOuts
: "Tried adding a label that was >= to the number of configured outputs "
+ numOuts
+ " with label "
+ goldClass;
goldLabel.putScalar(goldClass, 1.0f);
}
Double nodeWeight = classWeights.get(goldClass);
if (nodeWeight == null) nodeWeight = 1.0;
INDArray predictions = tree.prediction();
// If this is an unlabeled class, transform deltaClass to 0. We could
// make this more efficient by eliminating various of the below
// calculations, but this would be the easiest way to handle the
// unlabeled class
INDArray deltaClass = null;
if (predictions.data().dataType() == DataBuffer.Type.DOUBLE) {
deltaClass =
goldClass >= 0
? Nd4j.getBlasWrapper().scal(nodeWeight, predictions.sub(goldLabel))
: Nd4j.create(predictions.rows(), predictions.columns());
} else {
deltaClass =
goldClass >= 0
? Nd4j.getBlasWrapper()
.scal((float) nodeWeight.doubleValue(), predictions.sub(goldLabel))
: Nd4j.create(predictions.rows(), predictions.columns());
}
INDArray localCD = deltaClass.mmul(Nd4j.appendBias(currentVector).transpose());
double error =
-(Transforms.log(predictions).muli(goldLabel).sum(Integer.MAX_VALUE).getDouble(0));
error = error * nodeWeight;
tree.setError(error);
if (tree.isPreTerminal()) { // below us is a word vector
unaryCD.put(category, unaryCD.get(category).add(localCD));
String word = tree.children().get(0).label();
word = getVocabWord(word);
INDArray currentVectorDerivative =
Nd4j.getExecutioner()
.execAndReturn(
Nd4j.getOpFactory().createTransform(activationFunction, currentVector));
INDArray deltaFromClass = getUnaryClassification(category).transpose().mmul(deltaClass);
deltaFromClass =
deltaFromClass.get(interval(0, numHidden), interval(0, 1)).mul(currentVectorDerivative);
INDArray deltaFull = deltaFromClass.add(deltaUp);
INDArray wordVector = wordVectorD.get(word);
wordVectorD.put(word, wordVector.add(deltaFull));
} else {
// Otherwise, this must be a binary node
String leftCategory = basicCategory(tree.children().get(0).label());
String rightCategory = basicCategory(tree.children().get(1).label());
if (combineClassification) {
unaryCD.put("", unaryCD.get("").add(localCD));
} else {
binaryCD.put(
leftCategory, rightCategory, binaryCD.get(leftCategory, rightCategory).add(localCD));
}
INDArray currentVectorDerivative =
Nd4j.getExecutioner()
.execAndReturn(
Nd4j.getOpFactory().createTransform(activationFunction, currentVector));
INDArray deltaFromClass =
getBinaryClassification(leftCategory, rightCategory).transpose().mmul(deltaClass);
INDArray mult = deltaFromClass.get(interval(0, numHidden), interval(0, 1));
deltaFromClass = mult.muli(currentVectorDerivative);
INDArray deltaFull = deltaFromClass.add(deltaUp);
INDArray leftVector = tree.children().get(0).vector();
INDArray rightVector = tree.children().get(1).vector();
INDArray childrenVector = Nd4j.appendBias(leftVector, rightVector);
// deltaFull 50 x 1, childrenVector: 50 x 2
INDArray add = binaryTD.get(leftCategory, rightCategory);
INDArray W_df = deltaFromClass.mmul(childrenVector.transpose());
binaryTD.put(leftCategory, rightCategory, add.add(W_df));
INDArray deltaDown;
if (useDoubleTensors) {
INDArray Wt_df = getINDArrayGradient(deltaFull, leftVector, rightVector);
binaryINDArrayTD.put(
leftCategory,
rightCategory,
binaryINDArrayTD.get(leftCategory, rightCategory).add(Wt_df));
deltaDown =
computeINDArrayDeltaDown(
deltaFull,
leftVector,
rightVector,
getBinaryTransform(leftCategory, rightCategory),
getBinaryINDArray(leftCategory, rightCategory));
} else {
deltaDown = getBinaryTransform(leftCategory, rightCategory).transpose().mmul(deltaFull);
}
INDArray leftDerivative =
Nd4j.getExecutioner()
.execAndReturn(Nd4j.getOpFactory().createTransform(activationFunction, leftVector));
INDArray rightDerivative =
Nd4j.getExecutioner()
.execAndReturn(Nd4j.getOpFactory().createTransform(activationFunction, rightVector));
INDArray leftDeltaDown = deltaDown.get(interval(0, deltaFull.rows()), interval(0, 1));
INDArray rightDeltaDown =
deltaDown.get(interval(deltaFull.rows(), deltaFull.rows() * 2), interval(0, 1));
backpropDerivativesAndError(
tree.children().get(0),
binaryTD,
binaryCD,
binaryINDArrayTD,
unaryCD,
wordVectorD,
leftDerivative.mul(leftDeltaDown));
backpropDerivativesAndError(
tree.children().get(1),
binaryTD,
binaryCD,
binaryINDArrayTD,
unaryCD,
wordVectorD,
rightDerivative.mul(rightDeltaDown));
}
}
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;
}