Ejemplo n.º 1
0
  public void backpropDerivative(
      Tree tree,
      List<String> words,
      IdentityHashMap<Tree, SimpleMatrix> nodeVectors,
      TwoDimensionalMap<String, String, SimpleMatrix> binaryW_dfs,
      Map<String, SimpleMatrix> unaryW_dfs,
      TwoDimensionalMap<String, String, SimpleMatrix> binaryScoreDerivatives,
      Map<String, SimpleMatrix> unaryScoreDerivatives,
      Map<String, SimpleMatrix> wordVectorDerivatives,
      SimpleMatrix deltaUp) {
    if (tree.isLeaf()) {
      return;
    }
    if (tree.isPreTerminal()) {
      if (op.trainOptions.trainWordVectors) {
        String word = tree.children()[0].label().value();
        word = dvModel.getVocabWord(word);
        //        SimpleMatrix currentVector = nodeVectors.get(tree);
        //        SimpleMatrix currentVectorDerivative =
        // nonlinearityVectorToDerivative(currentVector);
        //        SimpleMatrix derivative = deltaUp.elementMult(currentVectorDerivative);
        SimpleMatrix derivative = deltaUp;
        wordVectorDerivatives.put(word, wordVectorDerivatives.get(word).plus(derivative));
      }
      return;
    }
    SimpleMatrix currentVector = nodeVectors.get(tree);
    SimpleMatrix currentVectorDerivative =
        NeuralUtils.elementwiseApplyTanhDerivative(currentVector);

    SimpleMatrix scoreW = dvModel.getScoreWForNode(tree);
    currentVectorDerivative = currentVectorDerivative.elementMult(scoreW.transpose());

    // the delta that is used at the current nodes
    SimpleMatrix deltaCurrent = deltaUp.plus(currentVectorDerivative);
    SimpleMatrix W = dvModel.getWForNode(tree);
    SimpleMatrix WTdelta = W.transpose().mult(deltaCurrent);

    if (tree.children().length == 2) {
      // TODO: RS: Change to the nice "getWForNode" setup?
      String leftLabel = dvModel.basicCategory(tree.children()[0].label().value());
      String rightLabel = dvModel.basicCategory(tree.children()[1].label().value());

      binaryScoreDerivatives.put(
          leftLabel,
          rightLabel,
          binaryScoreDerivatives.get(leftLabel, rightLabel).plus(currentVector.transpose()));

      SimpleMatrix leftVector = nodeVectors.get(tree.children()[0]);
      SimpleMatrix rightVector = nodeVectors.get(tree.children()[1]);
      SimpleMatrix childrenVector = NeuralUtils.concatenateWithBias(leftVector, rightVector);
      if (op.trainOptions.useContextWords) {
        childrenVector = concatenateContextWords(childrenVector, tree.getSpan(), words);
      }
      SimpleMatrix W_df = deltaCurrent.mult(childrenVector.transpose());
      binaryW_dfs.put(leftLabel, rightLabel, binaryW_dfs.get(leftLabel, rightLabel).plus(W_df));

      // and then recurse
      SimpleMatrix leftDerivative = NeuralUtils.elementwiseApplyTanhDerivative(leftVector);
      SimpleMatrix rightDerivative = NeuralUtils.elementwiseApplyTanhDerivative(rightVector);
      SimpleMatrix leftWTDelta = WTdelta.extractMatrix(0, deltaCurrent.numRows(), 0, 1);
      SimpleMatrix rightWTDelta =
          WTdelta.extractMatrix(deltaCurrent.numRows(), deltaCurrent.numRows() * 2, 0, 1);
      backpropDerivative(
          tree.children()[0],
          words,
          nodeVectors,
          binaryW_dfs,
          unaryW_dfs,
          binaryScoreDerivatives,
          unaryScoreDerivatives,
          wordVectorDerivatives,
          leftDerivative.elementMult(leftWTDelta));
      backpropDerivative(
          tree.children()[1],
          words,
          nodeVectors,
          binaryW_dfs,
          unaryW_dfs,
          binaryScoreDerivatives,
          unaryScoreDerivatives,
          wordVectorDerivatives,
          rightDerivative.elementMult(rightWTDelta));
    } else if (tree.children().length == 1) {
      String childLabel = dvModel.basicCategory(tree.children()[0].label().value());

      unaryScoreDerivatives.put(
          childLabel, unaryScoreDerivatives.get(childLabel).plus(currentVector.transpose()));

      SimpleMatrix childVector = nodeVectors.get(tree.children()[0]);
      SimpleMatrix childVectorWithBias = NeuralUtils.concatenateWithBias(childVector);
      if (op.trainOptions.useContextWords) {
        childVectorWithBias = concatenateContextWords(childVectorWithBias, tree.getSpan(), words);
      }
      SimpleMatrix W_df = deltaCurrent.mult(childVectorWithBias.transpose());

      // System.out.println("unary backprop derivative for " + childLabel);
      // System.out.println("Old transform:");
      // System.out.println(unaryW_dfs.get(childLabel));
      // System.out.println(" Delta:");
      // System.out.println(W_df.scale(scale));
      unaryW_dfs.put(childLabel, unaryW_dfs.get(childLabel).plus(W_df));

      // and then recurse
      SimpleMatrix childDerivative = NeuralUtils.elementwiseApplyTanhDerivative(childVector);
      // SimpleMatrix childDerivative = childVector;
      SimpleMatrix childWTDelta = WTdelta.extractMatrix(0, deltaCurrent.numRows(), 0, 1);
      backpropDerivative(
          tree.children()[0],
          words,
          nodeVectors,
          binaryW_dfs,
          unaryW_dfs,
          binaryScoreDerivatives,
          unaryScoreDerivatives,
          wordVectorDerivatives,
          childDerivative.elementMult(childWTDelta));
    }
  }
Ejemplo n.º 2
0
  private void forwardPropagateTree(
      Tree tree,
      List<String> words,
      IdentityHashMap<Tree, SimpleMatrix> nodeVectors,
      IdentityHashMap<Tree, Double> scores) {
    if (tree.isLeaf()) {
      return;
    }

    if (tree.isPreTerminal()) {
      Tree wordNode = tree.children()[0];
      String word = wordNode.label().value();
      SimpleMatrix wordVector = dvModel.getWordVector(word);
      wordVector = NeuralUtils.elementwiseApplyTanh(wordVector);
      nodeVectors.put(tree, wordVector);
      return;
    }

    for (Tree child : tree.children()) {
      forwardPropagateTree(child, words, nodeVectors, scores);
    }

    // at this point, nodeVectors contains the vectors for all of
    // the children of tree

    SimpleMatrix childVec;
    if (tree.children().length == 2) {
      childVec =
          NeuralUtils.concatenateWithBias(
              nodeVectors.get(tree.children()[0]), nodeVectors.get(tree.children()[1]));
    } else {
      childVec = NeuralUtils.concatenateWithBias(nodeVectors.get(tree.children()[0]));
    }
    if (op.trainOptions.useContextWords) {
      childVec = concatenateContextWords(childVec, tree.getSpan(), words);
    }

    SimpleMatrix W = dvModel.getWForNode(tree);
    if (W == null) {
      String error = "Could not find W for tree " + tree;
      if (op.testOptions.verbose) {
        System.err.println(error);
      }
      throw new NoSuchParseException(error);
    }
    SimpleMatrix currentVector = W.mult(childVec);
    currentVector = NeuralUtils.elementwiseApplyTanh(currentVector);
    nodeVectors.put(tree, currentVector);

    SimpleMatrix scoreW = dvModel.getScoreWForNode(tree);
    if (scoreW == null) {
      String error = "Could not find scoreW for tree " + tree;
      if (op.testOptions.verbose) {
        System.err.println(error);
      }
      throw new NoSuchParseException(error);
    }
    double score = scoreW.dot(currentVector);
    // score = NeuralUtils.sigmoid(score);
    scores.put(tree, score);
    // System.err.print(Double.toString(score)+" ");
  }