Exemple #1
0
  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;
 }
Exemple #3
0
 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;
  }
Exemple #5
0
  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;
  }