Examples of SimpleTensor

cc.redberry.core.tensor.SimpleTensor
Implementation of simple tensor. @author Dmitry Bolotin @author Stanislav Poslavsky @since 1.0
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

Examples of edu.stanford.nlp.neural.SimpleTensor

      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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Examples of edu.stanford.nlp.neural.SimpleTensor

    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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Examples of edu.stanford.nlp.neural.SimpleTensor


      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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