本文整理了Java中org.nd4j.linalg.factory.Nd4j.toFlattened()方法的一些代码示例，展示了Nd4j.toFlattened()的具体用法。这些代码示例主要来源于Github/Stackoverflow/Maven等平台，是从一些精选项目中提取出来的代码，具有较强的参考意义，能在一定程度帮忙到你。Nd4j.toFlattened()方法的具体详情如下：
包路径：org.nd4j.linalg.factory.Nd4j
类名称：Nd4j
方法名：toFlattened

Nd4j.toFlattened介绍

[英]Create a long row vector of all of the given ndarrays
[中]创建所有给定nArray的长行向量

代码示例

代码示例来源：origin: deeplearning4j/dl4j-examples

static INDArray append(INDArray arr1, INDArray values, int dimension) {
  if(dimension == -1) {
    return Nd4j.toFlattened(arr1, values);
  } else {
    return Nd4j.concat(dimension, arr1, values);
  }
}

代码示例来源：origin: deeplearning4j/dl4j-examples

static INDArray insert(INDArray arr1, int index, INDArray values, int dimension) {
  if(dimension == -1) {
    INDArray flat1 = Nd4j.toFlattened(arr1);
    INDArray flatValues = Nd4j.toFlattened(values);
    INDArray firstSlice = flat1.get(NDArrayIndex.interval(0, index));
    INDArray secondSlice = flat1.get(NDArrayIndex.interval(index, flat1.length()));
    return Nd4j.toFlattened(firstSlice, flatValues, secondSlice);
  } else {
    INDArray firstSlice = arr1.get(createIntervalOnDimension(dimension, false,
      0, index));
    INDArray secondSlice = arr1.get(createIntervalOnDimension(dimension, false,
      index, arr1.shape()[dimension]));
    return Nd4j.concat(dimension, firstSlice, values, secondSlice);
  }
}

代码示例来源：origin: deeplearning4j/nd4j

protected INDArray handleParamsView(INDArray outputArray, INDArray paramView) {
  //minor optimization when the views are the same, just return
  if(paramView == null || paramView == outputArray)
    return outputArray;
  INDArray flat = Nd4j.toFlattened(order(), outputArray);
  if (flat.length() != paramView.length())
    throw new RuntimeException("ParamView length does not match initialized weights length (view length: "
        + paramView.length() + ", view shape: " + Arrays.toString(paramView.shape())
        + "; flattened length: " + flat.length());
  paramView.assign(flat);
  return paramView.reshape(order(), outputArray.shape());
}

代码示例来源：origin: deeplearning4j/nd4j

public static INDArray tailor4d2d(@NonNull INDArray data) {
  long instances = data.size(0);
  long channels = data.size(1);
  long height = data.size(2);
  long width = data.size(3);
  INDArray in2d = Nd4j.create(channels, height * width * instances);
  long tads = data.tensorssAlongDimension(3, 2, 0);
  for (int i = 0; i < tads; i++) {
    INDArray thisTAD = data.tensorAlongDimension(i, 3, 2, 0);
    in2d.putRow(i, Nd4j.toFlattened(thisTAD));
  }
  return in2d.transposei();
}

代码示例来源：origin: deeplearning4j/dl4j-examples

static INDArray delete(int dimension, INDArray arr1, int... interval) {
  int length = interval.length;
  int lastIntervalValue = interval[length - 1];
  if(dimension == -1) {
    INDArray array1 = arr1.get(NDArrayIndex.interval(0, interval[0]));
    if(lastIntervalValue == arr1.length() - 1) {
      return Nd4j.toFlattened(array1);
    } else {
      INDArray array2 = arr1.get(NDArrayIndex.interval(lastIntervalValue + 1,
        arr1.length()));
      return Nd4j.toFlattened(array1, array2);
    }
  } else {
    INDArray array1 = arr1.get(createIntervalOnDimension(dimension, false, 0, interval[0]));
    if(lastIntervalValue == arr1.shape()[dimension] - 1) {
      return array1;
    } else {
      INDArray array2 = arr1.get(createIntervalOnDimension(dimension, false,
        lastIntervalValue + 1,
        arr1.shape()[dimension]));
      return Nd4j.concat(dimension, array1, array2);
    }
  }
}

代码示例来源：origin: deeplearning4j/dl4j-examples

print("Ascended sorted array on zero axis: ", axisSortedArray);
INDArray flattened = Nd4j.toFlattened(fourByFiveRandomZeroToOne);
print("Flattened array", flattened);

代码示例来源：origin: org.deeplearning4j/deeplearning4j-nn

@Override
public INDArray params() {
  //C order flattening, to match the gradient flattening order
  return Nd4j.toFlattened('c', params.values());
}

代码示例来源：origin: org.deeplearning4j/deeplearning4j-nn

public INDArray params() {
  List<INDArray> list = new ArrayList<>(2);
  for (Map.Entry<String, INDArray> entry : params.entrySet()) {
    list.add(entry.getValue());
  }
  return Nd4j.toFlattened('f', list);
}

代码示例来源：origin: org.deeplearning4j/deeplearning4j-nn

private void flattenGradient() {
  if (flatteningOrders != null) {
    //Arrays with non-default order get flattened to row vector first, then everything is flattened to f order
    //TODO revisit this, and make more efficient
    List<INDArray> toFlatten = new ArrayList<>();
    for (Map.Entry<String, INDArray> entry : gradients.entrySet()) {
      if (flatteningOrders.containsKey(entry.getKey())
              && flatteningOrders.get(entry.getKey()) != DEFAULT_FLATTENING_ORDER) {
        //Specific flattening order for this array, that isn't the default
        toFlatten.add(Nd4j.toFlattened(flatteningOrders.get(entry.getKey()), entry.getValue()));
      } else {
        //default flattening order for this array
        toFlatten.add(entry.getValue());
      }
    }
    flattenedGradient = Nd4j.toFlattened(DEFAULT_FLATTENING_ORDER, toFlatten);
  } else {
    //Standard case: flatten all to f order
    flattenedGradient = Nd4j.toFlattened(DEFAULT_FLATTENING_ORDER, gradients.values());
  }
}

代码示例来源：origin: org.deeplearning4j/deeplearning4j-nn

@Override
public INDArray gradient(List<String> order) {
  List<INDArray> toFlatten = new ArrayList<>();
  if (flatteningOrders == null) {
    for (String s : order) {
      if (!gradients.containsKey(s))
        continue;
      toFlatten.add(gradients.get(s));
    }
  } else {
    for (String s : order) {
      if (!gradients.containsKey(s))
        continue;
      if (flatteningOrders.containsKey(s) && flatteningOrders.get(s) != DEFAULT_FLATTENING_ORDER) {
        //Arrays with non-default order get flattened to row vector first, then everything is flattened to f order
        //TODO revisit this, and make more efficient
        toFlatten.add(Nd4j.toFlattened(flatteningOrders.get(s), gradients.get(s)));
      } else {
        toFlatten.add(gradients.get(s));
      }
    }
  }
  return Nd4j.toFlattened(DEFAULT_FLATTENING_ORDER, toFlatten);
}

代码示例来源：origin: org.deeplearning4j/deeplearning4j-nn

public INDArray scoreExamples(DataSetIterator iter, boolean addRegularizationTerms) {
  List<INDArray> out = new ArrayList<>();
  while (iter.hasNext()) {
    out.add(scoreExamples(iter.next(), addRegularizationTerms));
  }
  return Nd4j.toFlattened('f', out);
}

代码示例来源：origin: org.deeplearning4j/deeplearning4j-nn

/**
 * Returns a 1 x m vector where the vector is composed of
 * a flattened vector of all of the weights for the
 * various neuralNets(w,hbias NOT VBIAS) and output layer
 *
 * @return the params for this neural net
 */
public INDArray params(boolean backwardOnly) {
  if (backwardOnly)
    return params();
  List<INDArray> params = new ArrayList<>();
  for (Layer layer : getLayers()) {
    INDArray layerParams = layer.params();
    if (layerParams != null)
      params.add(layerParams); //may be null: subsampling etc layers
  }
  return Nd4j.toFlattened('f', params);
}

代码示例来源：origin: org.deeplearning4j/deeplearning4j-nn

/**
 * Get the parameters for the ComputationGraph
 *
 * @param backwardOnly If true: backprop parameters only (i.e., no visible layer biases used in layerwise pretraining layers)
 */
public INDArray params(boolean backwardOnly) {
  if (backwardOnly)
    return flattenedParams;
  List<INDArray> list = new ArrayList<>(layers.length);
  for (int i = 0; i < topologicalOrder.length; i++) {
    if (!vertices[topologicalOrder[i]].hasLayer())
      continue;
    Layer l = vertices[topologicalOrder[i]].getLayer();
    INDArray layerParams = l.params();
    if (layerParams != null)
      list.add(layerParams); //may be null: subsampling etc layers
  }
  return Nd4j.toFlattened('f', list);
}

代码示例来源：origin: org.nd4j/nd4j-api

public static INDArray tailor4d2d(@NonNull INDArray data) {
  int instances = data.size(0);
  int channels = data.size(1);
  int height = data.size(2);
  int width = data.size(3);
  INDArray in2d = Nd4j.create(channels, height * width * instances);
  int tads = data.tensorssAlongDimension(3, 2, 0);
  for (int i = 0; i < tads; i++) {
    INDArray thisTAD = data.tensorAlongDimension(i, 3, 2, 0);
    in2d.putRow(i, Nd4j.toFlattened(thisTAD));
  }
  return in2d.transposei();
}

代码示例来源：origin: Waikato/wekaDeeplearning4j

for (int i = 0; i < batchsize; i++) {
 INDArray row = ndArray.getRow(i);
 INDArray flattenedRow = Nd4j.toFlattened(row);
 Instance inst = new DenseInstance(atts.size());
 for (int j = 0; j < flattenedRow.size(1); j++) {

代码示例来源：origin: org.deeplearning4j/deeplearning4j-nn

INDArray flat = Nd4j.toFlattened(order, ret);
if (flat.length() != paramView.length())
  throw new RuntimeException("ParamView length does not match initialized weights length (view length: "