コード例 #1
0
ファイル: RNTN.java プロジェクト: jpquiroga/java-deeplearning
  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;
  }
コード例 #2
0
ファイル: RNTN.java プロジェクト: jpquiroga/java-deeplearning
  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));
    }
  }