/// <summary>
/// This is the method to call for assigning labels and node vectors
/// to the Tree.
/// </summary>
/// <remarks>
/// This is the method to call for assigning labels and node vectors
/// to the Tree. After calling this, each of the non-leaf nodes will
/// have the node vector and the predictions of their classes
/// assigned to that subtree's node. The annotations filled in are
/// the RNNCoreAnnotations.NodeVector, Predictions, and
/// PredictedClass. In general, PredictedClass will be the most
/// useful annotation except when training.
/// </remarks>
public virtual void ForwardPropagateTree(Tree tree)
{
SimpleMatrix nodeVector;
// initialized below or Exception thrown // = null;
SimpleMatrix classification;
// initialized below or Exception thrown // = null;
if (tree.IsLeaf())
{
// We do nothing for the leaves. The preterminals will
// calculate the classification for this word/tag. In fact, the
// recursion should not have gotten here (unless there are
// degenerate trees of just one leaf)
log.Info("SentimentCostAndGradient: warning: We reached leaves in forwardPropagate: " + tree);
throw new AssertionError("We should not have reached leaves in forwardPropagate");
}
else
{
if (tree.IsPreTerminal())
{
classification = model.GetUnaryClassification(tree.Label().Value());
string word = tree.Children()[0].Label().Value();
SimpleMatrix wordVector = model.GetWordVector(word);
nodeVector = NeuralUtils.ElementwiseApplyTanh(wordVector);
}
else
{
if (tree.Children().Length == 1)
{
log.Info("SentimentCostAndGradient: warning: Non-preterminal nodes of size 1: " + tree);
throw new AssertionError("Non-preterminal nodes of size 1 should have already been collapsed");
}
else
{
if (tree.Children().Length == 2)
{
ForwardPropagateTree(tree.Children()[0]);
ForwardPropagateTree(tree.Children()[1]);
string leftCategory = tree.Children()[0].Label().Value();
string rightCategory = tree.Children()[1].Label().Value();
SimpleMatrix W = model.GetBinaryTransform(leftCategory, rightCategory);
classification = model.GetBinaryClassification(leftCategory, rightCategory);
SimpleMatrix leftVector = RNNCoreAnnotations.GetNodeVector(tree.Children()[0]);
SimpleMatrix rightVector = RNNCoreAnnotations.GetNodeVector(tree.Children()[1]);
SimpleMatrix childrenVector = NeuralUtils.ConcatenateWithBias(leftVector, rightVector);
if (model.op.useTensors)
{
SimpleTensor tensor = model.GetBinaryTensor(leftCategory, rightCategory);
SimpleMatrix tensorIn = NeuralUtils.Concatenate(leftVector, rightVector);
SimpleMatrix tensorOut = tensor.BilinearProducts(tensorIn);
nodeVector = NeuralUtils.ElementwiseApplyTanh(W.Mult(childrenVector).Plus(tensorOut));
}
else
{
nodeVector = NeuralUtils.ElementwiseApplyTanh(W.Mult(childrenVector));
}
}
else
{
log.Info("SentimentCostAndGradient: warning: Tree not correctly binarized: " + tree);
throw new AssertionError("Tree not correctly binarized");
}
}
}
}
SimpleMatrix predictions = NeuralUtils.Softmax(classification.Mult(NeuralUtils.ConcatenateWithBias(nodeVector)));
int index = GetPredictedClass(predictions);
if (!(tree.Label() is CoreLabel))
{
log.Info("SentimentCostAndGradient: warning: No CoreLabels in nodes: " + tree);
throw new AssertionError("Expected CoreLabels in the nodes");
}
CoreLabel label = (CoreLabel)tree.Label();
label.Set(typeof(RNNCoreAnnotations.Predictions), predictions);
label.Set(typeof(RNNCoreAnnotations.PredictedClass), index);
label.Set(typeof(RNNCoreAnnotations.NodeVector), nodeVector);
}
private void ForwardPropagateTree(Tree tree, IList <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[tree] = wordVector;
return;
}
foreach (Tree child in 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[tree.Children()[0]], nodeVectors[tree.Children()[1]]);
}
else
{
childVec = NeuralUtils.ConcatenateWithBias(nodeVectors[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)
{
log.Info(error);
}
throw new NoSuchParseException(error);
}
SimpleMatrix currentVector = W.Mult(childVec);
currentVector = NeuralUtils.ElementwiseApplyTanh(currentVector);
nodeVectors[tree] = currentVector;
SimpleMatrix scoreW = dvModel.GetScoreWForNode(tree);
if (scoreW == null)
{
string error = "Could not find scoreW for tree " + tree;
if (op.testOptions.verbose)
{
log.Info(error);
}
throw new NoSuchParseException(error);
}
double score = scoreW.Dot(currentVector);
//score = NeuralUtils.sigmoid(score);
scores[tree] = score;
}