Пример #1
0
  // fill value & derivative
  public void calculate(double[] theta) {
    dvModel.vectorToParams(theta);

    double localValue = 0.0;
    double[] localDerivative = new double[theta.length];

    TwoDimensionalMap<String, String, SimpleMatrix> binaryW_dfsG, binaryW_dfsB;
    binaryW_dfsG = TwoDimensionalMap.treeMap();
    binaryW_dfsB = TwoDimensionalMap.treeMap();
    TwoDimensionalMap<String, String, SimpleMatrix> binaryScoreDerivativesG,
        binaryScoreDerivativesB;
    binaryScoreDerivativesG = TwoDimensionalMap.treeMap();
    binaryScoreDerivativesB = TwoDimensionalMap.treeMap();
    Map<String, SimpleMatrix> unaryW_dfsG, unaryW_dfsB;
    unaryW_dfsG = new TreeMap<>();
    unaryW_dfsB = new TreeMap<>();
    Map<String, SimpleMatrix> unaryScoreDerivativesG, unaryScoreDerivativesB;
    unaryScoreDerivativesG = new TreeMap<>();
    unaryScoreDerivativesB = new TreeMap<>();

    Map<String, SimpleMatrix> wordVectorDerivativesG = new TreeMap<>();
    Map<String, SimpleMatrix> wordVectorDerivativesB = new TreeMap<>();

    for (TwoDimensionalMap.Entry<String, String, SimpleMatrix> entry : dvModel.binaryTransform) {
      int numRows = entry.getValue().numRows();
      int numCols = entry.getValue().numCols();
      binaryW_dfsG.put(
          entry.getFirstKey(), entry.getSecondKey(), new SimpleMatrix(numRows, numCols));
      binaryW_dfsB.put(
          entry.getFirstKey(), entry.getSecondKey(), new SimpleMatrix(numRows, numCols));
      binaryScoreDerivativesG.put(
          entry.getFirstKey(), entry.getSecondKey(), new SimpleMatrix(1, numRows));
      binaryScoreDerivativesB.put(
          entry.getFirstKey(), entry.getSecondKey(), new SimpleMatrix(1, numRows));
    }
    for (Map.Entry<String, SimpleMatrix> entry : dvModel.unaryTransform.entrySet()) {
      int numRows = entry.getValue().numRows();
      int numCols = entry.getValue().numCols();
      unaryW_dfsG.put(entry.getKey(), new SimpleMatrix(numRows, numCols));
      unaryW_dfsB.put(entry.getKey(), new SimpleMatrix(numRows, numCols));
      unaryScoreDerivativesG.put(entry.getKey(), new SimpleMatrix(1, numRows));
      unaryScoreDerivativesB.put(entry.getKey(), new SimpleMatrix(1, numRows));
    }
    if (op.trainOptions.trainWordVectors) {
      for (Map.Entry<String, SimpleMatrix> entry : dvModel.wordVectors.entrySet()) {
        int numRows = entry.getValue().numRows();
        int numCols = entry.getValue().numCols();
        wordVectorDerivativesG.put(entry.getKey(), new SimpleMatrix(numRows, numCols));
        wordVectorDerivativesB.put(entry.getKey(), new SimpleMatrix(numRows, numCols));
      }
    }

    // Some optimization methods prints out a line without an end, so our
    // debugging statements are misaligned
    Timing scoreTiming = new Timing();
    scoreTiming.doing("Scoring trees");
    int treeNum = 0;
    MulticoreWrapper<Tree, Pair<DeepTree, DeepTree>> wrapper =
        new MulticoreWrapper<>(op.trainOptions.trainingThreads, new ScoringProcessor());
    for (Tree tree : trainingBatch) {
      wrapper.put(tree);
    }
    wrapper.join();
    scoreTiming.done();
    while (wrapper.peek()) {
      Pair<DeepTree, DeepTree> result = wrapper.poll();
      DeepTree goldTree = result.first;
      DeepTree bestTree = result.second;

      StringBuilder treeDebugLine = new StringBuilder();
      Formatter formatter = new Formatter(treeDebugLine);
      boolean isDone =
          (Math.abs(bestTree.getScore() - goldTree.getScore()) <= 0.00001
              || goldTree.getScore() > bestTree.getScore());
      String done = isDone ? "done" : "";
      formatter.format(
          "Tree %6d Highest tree: %12.4f Correct tree: %12.4f %s",
          treeNum, bestTree.getScore(), goldTree.getScore(), done);
      System.err.println(treeDebugLine.toString());
      if (!isDone) {
        // if the gold tree is better than the best hypothesis tree by
        // a large enough margin, then the score difference will be 0
        // and we ignore the tree

        double valueDelta = bestTree.getScore() - goldTree.getScore();
        // double valueDelta = Math.max(0.0, - scoreGold + bestScore);
        localValue += valueDelta;

        // get the context words for this tree - should be the same
        // for either goldTree or bestTree
        List<String> words = getContextWords(goldTree.getTree());

        // The derivatives affected by this tree are only based on the
        // nodes present in this tree, eg not all matrix derivatives
        // will be affected by this tree
        backpropDerivative(
            goldTree.getTree(),
            words,
            goldTree.getVectors(),
            binaryW_dfsG,
            unaryW_dfsG,
            binaryScoreDerivativesG,
            unaryScoreDerivativesG,
            wordVectorDerivativesG);

        backpropDerivative(
            bestTree.getTree(),
            words,
            bestTree.getVectors(),
            binaryW_dfsB,
            unaryW_dfsB,
            binaryScoreDerivativesB,
            unaryScoreDerivativesB,
            wordVectorDerivativesB);
      }
      ++treeNum;
    }

    double[] localDerivativeGood;
    double[] localDerivativeB;
    if (op.trainOptions.trainWordVectors) {
      localDerivativeGood =
          NeuralUtils.paramsToVector(
              theta.length,
              binaryW_dfsG.valueIterator(),
              unaryW_dfsG.values().iterator(),
              binaryScoreDerivativesG.valueIterator(),
              unaryScoreDerivativesG.values().iterator(),
              wordVectorDerivativesG.values().iterator());

      localDerivativeB =
          NeuralUtils.paramsToVector(
              theta.length,
              binaryW_dfsB.valueIterator(),
              unaryW_dfsB.values().iterator(),
              binaryScoreDerivativesB.valueIterator(),
              unaryScoreDerivativesB.values().iterator(),
              wordVectorDerivativesB.values().iterator());
    } else {
      localDerivativeGood =
          NeuralUtils.paramsToVector(
              theta.length,
              binaryW_dfsG.valueIterator(),
              unaryW_dfsG.values().iterator(),
              binaryScoreDerivativesG.valueIterator(),
              unaryScoreDerivativesG.values().iterator());

      localDerivativeB =
          NeuralUtils.paramsToVector(
              theta.length,
              binaryW_dfsB.valueIterator(),
              unaryW_dfsB.values().iterator(),
              binaryScoreDerivativesB.valueIterator(),
              unaryScoreDerivativesB.values().iterator());
    }

    // correct - highest
    for (int i = 0; i < localDerivativeGood.length; i++) {
      localDerivative[i] = localDerivativeB[i] - localDerivativeGood[i];
    }

    // TODO: this is where we would combine multiple costs if we had parallelized the calculation
    value = localValue;
    derivative = localDerivative;

    // normalizing by training batch size
    value = (1.0 / trainingBatch.size()) * value;
    ArrayMath.multiplyInPlace(derivative, (1.0 / trainingBatch.size()));

    // add regularization to cost:
    double[] currentParams = dvModel.paramsToVector();
    double regCost = 0;
    for (double currentParam : currentParams) {
      regCost += currentParam * currentParam;
    }
    regCost = op.trainOptions.regCost * 0.5 * regCost;
    value += regCost;
    // add regularization to gradient
    ArrayMath.multiplyInPlace(currentParams, op.trainOptions.regCost);
    ArrayMath.pairwiseAddInPlace(derivative, currentParams);
  }
Пример #2
0
  public void printParamInformation(int index) {
    int curIndex = 0;
    for (TwoDimensionalMap.Entry<String, String, SimpleMatrix> entry : binaryTransform) {
      if (curIndex <= index && curIndex + entry.getValue().getNumElements() > index) {
        System.err.println(
            "Index "
                + index
                + " is element "
                + (index - curIndex)
                + " of binaryTransform \""
                + entry.getFirstKey()
                + ","
                + entry.getSecondKey()
                + "\"");
        return;
      } else {
        curIndex += entry.getValue().getNumElements();
      }
    }

    for (TwoDimensionalMap.Entry<String, String, SimpleMatrix> entry : binaryClassification) {
      if (curIndex <= index && curIndex + entry.getValue().getNumElements() > index) {
        System.err.println(
            "Index "
                + index
                + " is element "
                + (index - curIndex)
                + " of binaryClassification \""
                + entry.getFirstKey()
                + ","
                + entry.getSecondKey()
                + "\"");
        return;
      } else {
        curIndex += entry.getValue().getNumElements();
      }
    }

    for (TwoDimensionalMap.Entry<String, String, SimpleTensor> entry : binaryTensors) {
      if (curIndex <= index && curIndex + entry.getValue().getNumElements() > index) {
        System.err.println(
            "Index "
                + index
                + " is element "
                + (index - curIndex)
                + " of binaryTensor \""
                + entry.getFirstKey()
                + ","
                + entry.getSecondKey()
                + "\"");
        return;
      } else {
        curIndex += entry.getValue().getNumElements();
      }
    }

    for (Map.Entry<String, SimpleMatrix> entry : unaryClassification.entrySet()) {
      if (curIndex <= index && curIndex + entry.getValue().getNumElements() > index) {
        System.err.println(
            "Index "
                + index
                + " is element "
                + (index - curIndex)
                + " of unaryClassification \""
                + entry.getKey()
                + "\"");
        return;
      } else {
        curIndex += entry.getValue().getNumElements();
      }
    }

    for (Map.Entry<String, SimpleMatrix> entry : wordVectors.entrySet()) {
      if (curIndex <= index && curIndex + entry.getValue().getNumElements() > index) {
        System.err.println(
            "Index "
                + index
                + " is element "
                + (index - curIndex)
                + " of wordVector \""
                + entry.getKey()
                + "\"");
        return;
      } else {
        curIndex += entry.getValue().getNumElements();
      }
    }

    System.err.println(
        "Index "
            + index
            + " is beyond the length of the parameters; total parameter space was "
            + totalParamSize());
  }