Examples of edu.stanford.nlp.neural.SimpleTensor

edu.stanford.nlp.neural.SimpleTensor
This class defines a block tensor, somewhat like a three dimensional matrix. This can be created in various ways, such as by providing an array of SimpleMatrix slices, by providing the initial size to create a 0-initialized tensor, or by creating a random matrix. @author John Bauer @author Richard Socher

    // System.err.println(unaryClassification);
  }


  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);
  }

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    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);
    System.err.println("W tensor size: " + tensor.numRows() + "x" + tensor.numCols() + "x" + tensor.numSlices());


    SimpleMatrix W = loadMatrix(basePath + "bin/W.bin", basePath + "W.txt");
    System.err.println("W matrix size: " + W.numRows() + "x" + W.numCols());


    SimpleMatrix Wcat = loadMatrix(basePath + "bin/Wcat.bin", basePath + "Wcat.txt");

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      for (TwoDimensionalMap.Entry<String, String, SimpleTensor> entry : model.binaryTensors) {
        int numRows = entry.getValue().numRows();
        int numCols = entry.getValue().numCols();
        int numSlices = entry.getValue().numSlices();


        binaryTensorTD.put(entry.getFirstKey(), entry.getSecondKey(), new SimpleTensor(numRows, numCols, numSlices));
      }
    }


    for (Map.Entry<String, SimpleMatrix> entry : model.unaryClassification.entrySet()) {
      int numRows = entry.getValue().numRows();

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                                  TwoDimensionalMap<String, String, SimpleTensor> currentMatrices,
                                  double scale,
                                  double regCost) {
    double cost = 0.0; // the regularization cost
    for (TwoDimensionalMap.Entry<String, String, SimpleTensor> entry : 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;
  }

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      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);
      }

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    return deltaTensor.plus(WTDeltaNoBias);
  }


  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;
  }

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      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 {

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