internal virtual SimpleTensor RandomBinaryTensor()
{
double range = 1.0 / (4.0 * numHid);
SimpleTensor tensor = SimpleTensor.Random(numHid * 2, numHid * 2, numHid, -range, range, rand);
return(tensor.Scale(op.trainOptions.scalingForInit));
}
private static SimpleTensor GetTensorGradient(SimpleMatrix deltaFull, SimpleMatrix leftVector, SimpleMatrix rightVector)
{
int size = deltaFull.GetNumElements();
SimpleTensor Wt_df = new SimpleTensor(size * 2, size * 2, size);
// TODO: combine this concatenation with computeTensorDeltaDown?
SimpleMatrix fullVector = NeuralUtils.Concatenate(leftVector, rightVector);
for (int slice = 0; slice < size; ++slice)
{
Wt_df.SetSlice(slice, fullVector.Scale(deltaFull.Get(slice)).Mult(fullVector.Transpose()));
}
return(Wt_df);
}
private static double ScaleAndRegularizeTensor(TwoDimensionalMap <string, string, SimpleTensor> derivatives, TwoDimensionalMap <string, string, SimpleTensor> currentMatrices, double scale, double regCost)
{
double cost = 0.0;
// the regularization cost
foreach (TwoDimensionalMap.Entry <string, string, SimpleTensor> entry in currentMatrices)
{
SimpleTensor D = derivatives.Get(entry.GetFirstKey(), entry.GetSecondKey());
D = D.Scale(scale).Plus(entry.GetValue().Scale(regCost));
derivatives.Put(entry.GetFirstKey(), entry.GetSecondKey(), D);
cost += entry.GetValue().ElementMult(entry.GetValue()).ElementSum() * regCost / 2.0;
}
return(cost);
}
public virtual void VectorToParams(double[] theta)
{
NeuralUtils.VectorToParams(theta, binaryTransform.ValueIterator(), binaryClassification.ValueIterator(), SimpleTensor.IteratorSimpleMatrix(binaryTensors.ValueIterator()), unaryClassification.Values.GetEnumerator(), wordVectors.Values.GetEnumerator
());
}
public virtual double[] ParamsToVector()
{
int totalSize = TotalParamSize();
return(NeuralUtils.ParamsToVector(totalSize, binaryTransform.ValueIterator(), binaryClassification.ValueIterator(), SimpleTensor.IteratorSimpleMatrix(binaryTensors.ValueIterator()), unaryClassification.Values.GetEnumerator(), wordVectors.Values
.GetEnumerator()));
}
/*
* // An example of how you could read in old models with readObject to fix the serialization
* // You would first read in the old model, then reserialize it
* private void readObject(ObjectInputStream in)
* throws IOException, ClassNotFoundException
* {
* ObjectInputStream.GetField fields = in.readFields();
* binaryTransform = ErasureUtils.uncheckedCast(fields.get("binaryTransform", null));
*
* // transform binaryTensors
* binaryTensors = TwoDimensionalMap.treeMap();
* TwoDimensionalMap<String, String, edu.stanford.nlp.rnn.SimpleTensor> oldTensors = ErasureUtils.uncheckedCast(fields.get("binaryTensors", null));
* for (String first : oldTensors.firstKeySet()) {
* for (String second : oldTensors.get(first).keySet()) {
* binaryTensors.put(first, second, new SimpleTensor(oldTensors.get(first, second).slices));
* }
* }
*
* binaryClassification = ErasureUtils.uncheckedCast(fields.get("binaryClassification", null));
* unaryClassification = ErasureUtils.uncheckedCast(fields.get("unaryClassification", null));
* wordVectors = ErasureUtils.uncheckedCast(fields.get("wordVectors", null));
*
* if (fields.defaulted("numClasses")) {
* throw new RuntimeException();
* }
* numClasses = fields.get("numClasses", 0);
*
* if (fields.defaulted("numHid")) {
* throw new RuntimeException();
* }
* numHid = fields.get("numHid", 0);
*
* if (fields.defaulted("numBinaryMatrices")) {
* throw new RuntimeException();
* }
* numBinaryMatrices = fields.get("numBinaryMatrices", 0);
*
* if (fields.defaulted("binaryTransformSize")) {
* throw new RuntimeException();
* }
* binaryTransformSize = fields.get("binaryTransformSize", 0);
*
* if (fields.defaulted("binaryTensorSize")) {
* throw new RuntimeException();
* }
* binaryTensorSize = fields.get("binaryTensorSize", 0);
*
* if (fields.defaulted("binaryClassificationSize")) {
* throw new RuntimeException();
* }
* binaryClassificationSize = fields.get("binaryClassificationSize", 0);
*
* if (fields.defaulted("numUnaryMatrices")) {
* throw new RuntimeException();
* }
* numUnaryMatrices = fields.get("numUnaryMatrices", 0);
*
* if (fields.defaulted("unaryClassificationSize")) {
* throw new RuntimeException();
* }
* unaryClassificationSize = fields.get("unaryClassificationSize", 0);
*
* rand = ErasureUtils.uncheckedCast(fields.get("rand", null));
* op = ErasureUtils.uncheckedCast(fields.get("op", null));
* op.classNames = op.DEFAULT_CLASS_NAMES;
* op.equivalenceClasses = op.APPROXIMATE_EQUIVALENCE_CLASSES;
* op.equivalenceClassNames = op.DEFAULT_EQUIVALENCE_CLASS_NAMES;
* }
*/
/// <summary>
/// Given single matrices and sets of options, create the
/// corresponding SentimentModel.
/// </summary>
/// <remarks>
/// Given single matrices and sets of options, create the
/// corresponding SentimentModel. Useful for creating a Java version
/// of a model trained in some other manner, such as using the
/// original Matlab code.
/// </remarks>
internal static Edu.Stanford.Nlp.Sentiment.SentimentModel ModelFromMatrices(SimpleMatrix W, SimpleMatrix Wcat, SimpleTensor Wt, IDictionary <string, SimpleMatrix> wordVectors, RNNOptions op)
{
if (!op.combineClassification || !op.simplifiedModel)
{
throw new ArgumentException("Can only create a model using this method if combineClassification and simplifiedModel are turned on");
}
TwoDimensionalMap <string, string, SimpleMatrix> binaryTransform = TwoDimensionalMap.TreeMap();
binaryTransform.Put(string.Empty, string.Empty, W);
TwoDimensionalMap <string, string, SimpleTensor> binaryTensors = TwoDimensionalMap.TreeMap();
binaryTensors.Put(string.Empty, string.Empty, Wt);
TwoDimensionalMap <string, string, SimpleMatrix> binaryClassification = TwoDimensionalMap.TreeMap();
IDictionary <string, SimpleMatrix> unaryClassification = Generics.NewTreeMap();
unaryClassification[string.Empty] = Wcat;
return(new Edu.Stanford.Nlp.Sentiment.SentimentModel(binaryTransform, binaryTensors, binaryClassification, unaryClassification, wordVectors, op));
}
/// <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 static SimpleMatrix ComputeTensorDeltaDown(SimpleMatrix deltaFull, SimpleMatrix leftVector, SimpleMatrix rightVector, SimpleMatrix W, SimpleTensor Wt)
{
SimpleMatrix WTDelta = W.Transpose().Mult(deltaFull);
SimpleMatrix WTDeltaNoBias = WTDelta.ExtractMatrix(0, deltaFull.NumRows() * 2, 0, 1);
int size = deltaFull.GetNumElements();
SimpleMatrix deltaTensor = new SimpleMatrix(size * 2, 1);
SimpleMatrix fullVector = NeuralUtils.Concatenate(leftVector, rightVector);
for (int slice = 0; slice < size; ++slice)
{
SimpleMatrix scaledFullVector = fullVector.Scale(deltaFull.Get(slice));
deltaTensor = deltaTensor.Plus(Wt.GetSlice(slice).Plus(Wt.GetSlice(slice).Transpose()).Mult(scaledFullVector));
}
return(deltaTensor.Plus(WTDeltaNoBias));
}
private void BackpropDerivativesAndError(Tree tree, TwoDimensionalMap <string, string, SimpleMatrix> binaryTD, TwoDimensionalMap <string, string, SimpleMatrix> binaryCD, TwoDimensionalMap <string, string, SimpleTensor> binaryTensorTD, IDictionary
<string, SimpleMatrix> unaryCD, IDictionary <string, SimpleMatrix> wordVectorD, SimpleMatrix deltaUp)
{
if (tree.IsLeaf())
{
return;
}
SimpleMatrix currentVector = RNNCoreAnnotations.GetNodeVector(tree);
string category = tree.Label().Value();
category = model.BasicCategory(category);
// Build a vector that looks like 0,0,1,0,0 with an indicator for the correct class
SimpleMatrix goldLabel = new SimpleMatrix(model.numClasses, 1);
int goldClass = RNNCoreAnnotations.GetGoldClass(tree);
if (goldClass >= 0)
{
goldLabel.Set(goldClass, 1.0);
}
double nodeWeight = model.op.trainOptions.GetClassWeight(goldClass);
SimpleMatrix predictions = RNNCoreAnnotations.GetPredictions(tree);
// 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
SimpleMatrix deltaClass = goldClass >= 0 ? predictions.Minus(goldLabel).Scale(nodeWeight) : new SimpleMatrix(predictions.NumRows(), predictions.NumCols());
SimpleMatrix localCD = deltaClass.Mult(NeuralUtils.ConcatenateWithBias(currentVector).Transpose());
double error = -(NeuralUtils.ElementwiseApplyLog(predictions).ElementMult(goldLabel).ElementSum());
error = error * nodeWeight;
RNNCoreAnnotations.SetPredictionError(tree, error);
if (tree.IsPreTerminal())
{
// below us is a word vector
unaryCD[category] = unaryCD[category].Plus(localCD);
string word = tree.Children()[0].Label().Value();
word = model.GetVocabWord(word);
//SimpleMatrix currentVectorDerivative = NeuralUtils.elementwiseApplyTanhDerivative(currentVector);
//SimpleMatrix deltaFromClass = model.getUnaryClassification(category).transpose().mult(deltaClass);
//SimpleMatrix deltaFull = deltaFromClass.extractMatrix(0, model.op.numHid, 0, 1).plus(deltaUp);
//SimpleMatrix wordDerivative = deltaFull.elementMult(currentVectorDerivative);
//wordVectorD.put(word, wordVectorD.get(word).plus(wordDerivative));
SimpleMatrix currentVectorDerivative = NeuralUtils.ElementwiseApplyTanhDerivative(currentVector);
SimpleMatrix deltaFromClass = model.GetUnaryClassification(category).Transpose().Mult(deltaClass);
deltaFromClass = deltaFromClass.ExtractMatrix(0, model.op.numHid, 0, 1).ElementMult(currentVectorDerivative);
SimpleMatrix deltaFull = deltaFromClass.Plus(deltaUp);
SimpleMatrix oldWordVectorD = wordVectorD[word];
if (oldWordVectorD == null)
{
wordVectorD[word] = deltaFull;
}
else
{
wordVectorD[word] = oldWordVectorD.Plus(deltaFull);
}
}
else
{
// Otherwise, this must be a binary node
string leftCategory = model.BasicCategory(tree.Children()[0].Label().Value());
string rightCategory = model.BasicCategory(tree.Children()[1].Label().Value());
if (model.op.combineClassification)
{
unaryCD[string.Empty] = unaryCD[string.Empty].Plus(localCD);
}
else
{
binaryCD.Put(leftCategory, rightCategory, binaryCD.Get(leftCategory, rightCategory).Plus(localCD));
}
SimpleMatrix currentVectorDerivative = NeuralUtils.ElementwiseApplyTanhDerivative(currentVector);
SimpleMatrix deltaFromClass = model.GetBinaryClassification(leftCategory, rightCategory).Transpose().Mult(deltaClass);
deltaFromClass = deltaFromClass.ExtractMatrix(0, model.op.numHid, 0, 1).ElementMult(currentVectorDerivative);
SimpleMatrix deltaFull = deltaFromClass.Plus(deltaUp);
SimpleMatrix leftVector = RNNCoreAnnotations.GetNodeVector(tree.Children()[0]);
SimpleMatrix rightVector = RNNCoreAnnotations.GetNodeVector(tree.Children()[1]);
SimpleMatrix childrenVector = NeuralUtils.ConcatenateWithBias(leftVector, rightVector);
SimpleMatrix W_df = deltaFull.Mult(childrenVector.Transpose());
binaryTD.Put(leftCategory, rightCategory, binaryTD.Get(leftCategory, rightCategory).Plus(W_df));
SimpleMatrix deltaDown;
if (model.op.useTensors)
{
SimpleTensor Wt_df = GetTensorGradient(deltaFull, leftVector, rightVector);
binaryTensorTD.Put(leftCategory, rightCategory, binaryTensorTD.Get(leftCategory, rightCategory).Plus(Wt_df));
deltaDown = ComputeTensorDeltaDown(deltaFull, leftVector, rightVector, model.GetBinaryTransform(leftCategory, rightCategory), model.GetBinaryTensor(leftCategory, rightCategory));
}
else
{
deltaDown = model.GetBinaryTransform(leftCategory, rightCategory).Transpose().Mult(deltaFull);
}
SimpleMatrix leftDerivative = NeuralUtils.ElementwiseApplyTanhDerivative(leftVector);
SimpleMatrix rightDerivative = NeuralUtils.ElementwiseApplyTanhDerivative(rightVector);
SimpleMatrix leftDeltaDown = deltaDown.ExtractMatrix(0, deltaFull.NumRows(), 0, 1);
SimpleMatrix rightDeltaDown = deltaDown.ExtractMatrix(deltaFull.NumRows(), deltaFull.NumRows() * 2, 0, 1);
BackpropDerivativesAndError(tree.Children()[0], binaryTD, binaryCD, binaryTensorTD, unaryCD, wordVectorD, leftDerivative.ElementMult(leftDeltaDown));
BackpropDerivativesAndError(tree.Children()[1], binaryTD, binaryCD, binaryTensorTD, unaryCD, wordVectorD, rightDerivative.ElementMult(rightDeltaDown));
}
}
protected internal override void Calculate(double[] theta)
{
model.VectorToParams(theta);
SentimentCostAndGradient.ModelDerivatives derivatives;
if (model.op.trainOptions.nThreads == 1)
{
derivatives = ScoreDerivatives(trainingBatch);
}
else
{
// TODO: because some addition operations happen in different
// orders now, this results in slightly different values, which
// over time add up to significantly different models even when
// given the same random seed. Probably not a big deal.
// To be more specific, for trees T1, T2, T3, ... Tn,
// when using one thread, we sum the derivatives T1 + T2 ...
// When using multiple threads, we first sum T1 + ... + Tk,
// then sum Tk+1 + ... + T2k, etc, for split size k.
// The splits are then summed in order.
// This different sum order results in slightly different numbers.
MulticoreWrapper <IList <Tree>, SentimentCostAndGradient.ModelDerivatives> wrapper = new MulticoreWrapper <IList <Tree>, SentimentCostAndGradient.ModelDerivatives>(model.op.trainOptions.nThreads, new SentimentCostAndGradient.ScoringProcessor(this
));
// use wrapper.nThreads in case the number of threads was automatically changed
foreach (IList <Tree> chunk in CollectionUtils.PartitionIntoFolds(trainingBatch, wrapper.NThreads()))
{
wrapper.Put(chunk);
}
wrapper.Join();
derivatives = new SentimentCostAndGradient.ModelDerivatives(model);
while (wrapper.Peek())
{
SentimentCostAndGradient.ModelDerivatives batchDerivatives = wrapper.Poll();
derivatives.Add(batchDerivatives);
}
}
// scale the error by the number of sentences so that the
// regularization isn't drowned out for large training batchs
double scale = (1.0 / trainingBatch.Count);
value = derivatives.error * scale;
value += ScaleAndRegularize(derivatives.binaryTD, model.binaryTransform, scale, model.op.trainOptions.regTransformMatrix, false);
value += ScaleAndRegularize(derivatives.binaryCD, model.binaryClassification, scale, model.op.trainOptions.regClassification, true);
value += ScaleAndRegularizeTensor(derivatives.binaryTensorTD, model.binaryTensors, scale, model.op.trainOptions.regTransformTensor);
value += ScaleAndRegularize(derivatives.unaryCD, model.unaryClassification, scale, model.op.trainOptions.regClassification, false, true);
value += ScaleAndRegularize(derivatives.wordVectorD, model.wordVectors, scale, model.op.trainOptions.regWordVector, true, false);
derivative = NeuralUtils.ParamsToVector(theta.Length, derivatives.binaryTD.ValueIterator(), derivatives.binaryCD.ValueIterator(), SimpleTensor.IteratorSimpleMatrix(derivatives.binaryTensorTD.ValueIterator()), derivatives.unaryCD.Values.GetEnumerator
(), derivatives.wordVectorD.Values.GetEnumerator());
}
/// <exception cref="System.IO.IOException"/>
public static void Main(string[] args)
{
string basePath = "/user/socherr/scr/projects/semComp/RNTN/src/params/";
int numSlices = 25;
bool useEscapedParens = false;
for (int argIndex = 0; argIndex < args.Length;)
{
if (Sharpen.Runtime.EqualsIgnoreCase(args[argIndex], "-slices"))
{
numSlices = System.Convert.ToInt32(args[argIndex + 1]);
argIndex += 2;
}
else
{
if (Sharpen.Runtime.EqualsIgnoreCase(args[argIndex], "-path"))
{
basePath = args[argIndex + 1];
argIndex += 2;
}
else
{
if (Sharpen.Runtime.EqualsIgnoreCase(args[argIndex], "-useEscapedParens"))
{
useEscapedParens = true;
argIndex += 1;
}
else
{
log.Info("Unknown argument " + args[argIndex]);
System.Environment.Exit(2);
}
}
}
}
SimpleMatrix[] slices = new SimpleMatrix[numSlices];
for (int i = 0; i < numSlices; ++i)
{
slices[i] = LoadMatrix(basePath + "bin/Wt_" + (i + 1) + ".bin", basePath + "Wt_" + (i + 1) + ".txt");
}
SimpleTensor tensor = new SimpleTensor(slices);
log.Info("W tensor size: " + tensor.NumRows() + "x" + tensor.NumCols() + "x" + tensor.NumSlices());
SimpleMatrix W = LoadMatrix(basePath + "bin/W.bin", basePath + "W.txt");
log.Info("W matrix size: " + W.NumRows() + "x" + W.NumCols());
SimpleMatrix Wcat = LoadMatrix(basePath + "bin/Wcat.bin", basePath + "Wcat.txt");
log.Info("W cat size: " + Wcat.NumRows() + "x" + Wcat.NumCols());
SimpleMatrix combinedWV = LoadMatrix(basePath + "bin/Wv.bin", basePath + "Wv.txt");
log.Info("Word matrix size: " + combinedWV.NumRows() + "x" + combinedWV.NumCols());
File vocabFile = new File(basePath + "vocab_1.txt");
if (!vocabFile.Exists())
{
vocabFile = new File(basePath + "words.txt");
}
IList <string> lines = Generics.NewArrayList();
foreach (string line in IOUtils.ReadLines(vocabFile))
{
lines.Add(line.Trim());
}
log.Info("Lines in vocab file: " + lines.Count);
IDictionary <string, SimpleMatrix> wordVectors = Generics.NewTreeMap();
for (int i_1 = 0; i_1 < lines.Count && i_1 < combinedWV.NumCols(); ++i_1)
{
string[] pieces = lines[i_1].Split(" +");
if (pieces.Length == 0 || pieces.Length > 1)
{
continue;
}
wordVectors[pieces[0]] = combinedWV.ExtractMatrix(0, numSlices, i_1, i_1 + 1);
if (pieces[0].Equals("UNK"))
{
wordVectors[SentimentModel.UnknownWord] = wordVectors["UNK"];
}
}
// If there is no ",", we first try to look for an HTML escaping,
// then fall back to "." as better than just a random word vector.
// Same for "``" and ";"
CopyWordVector(wordVectors, ",", ",");
CopyWordVector(wordVectors, ".", ",");
CopyWordVector(wordVectors, ";", ";");
CopyWordVector(wordVectors, ".", ";");
CopyWordVector(wordVectors, "``", "``");
CopyWordVector(wordVectors, "''", "``");
if (useEscapedParens)
{
ReplaceWordVector(wordVectors, "(", "-LRB-");
ReplaceWordVector(wordVectors, ")", "-RRB-");
}
RNNOptions op = new RNNOptions();
op.numHid = numSlices;
op.lowercaseWordVectors = false;
if (Wcat.NumRows() == 2)
{
op.classNames = new string[] { "Negative", "Positive" };
op.equivalenceClasses = new int[][] { new int[] { 0 }, new int[] { 1 } };
// TODO: set to null once old models are updated
op.numClasses = 2;
}
if (!wordVectors.Contains(SentimentModel.UnknownWord))
{
wordVectors[SentimentModel.UnknownWord] = SimpleMatrix.Random(numSlices, 1, -0.00001, 0.00001, new Random());
}
SentimentModel model = SentimentModel.ModelFromMatrices(W, Wcat, tensor, wordVectors, op);
model.SaveSerialized("matlab.ser.gz");
}
public bool isMetric(SimpleTensor t)
{
return(nameManager.isKroneckerOrMetric(t.GetName()) &&
IndicesUtils.haveEqualStates(t.GetIndices()[0], t.GetIndices()[1]));
}
/**
* Returns {@code true} if specified tensor is a metric or a Kronecker tensor
*
* @param t tensor
* @return {@code true} if specified tensor is a metric or a Kronecker tensor
*/
public bool IsKroneckerOrMetric(SimpleTensor t)
{
return(nameManager.IsKroneckerOrMetric(t.Name));
}
/**
* Returns {@code true} if specified tensor is a metric tensor
*
* @param t tensor
* @return {@code true} if specified tensor is a metric tensor
*/
public bool IsMetric(SimpleTensor t)
{
return(nameManager.IsKroneckerOrMetric(t.Name) &&
IndicesUtils.haveEqualStates(t.Indices[0], t.Indices[1]));
}