예제 #1
0
  /**
   * output the prediction probabilities for each tree
   *
   * @param trees the trees to predict
   * @return the prediction probabilities for each tree
   */
  public List<FloatMatrix> output(List<Tree> trees) {
    List<FloatMatrix> ret = new ArrayList<>();
    for (Tree t : trees) {
      forwardPropagateTree(t);
      ret.add(t.prediction());
    }

    return ret;
  }
예제 #2
0
  /**
   * output the top level labels for each tree
   *
   * @param trees the trees to predict
   * @return the prediction labels for each tree
   */
  public List<Integer> predict(List<Tree> trees) {
    List<Integer> ret = new ArrayList<>();
    for (Tree t : trees) {
      forwardPropagateTree(t);
      ret.add(SimpleBlas.iamax(t.prediction()));
    }

    return ret;
  }
예제 #3
0
  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));
    }
  }