org.nd4j.linalg.factory.Nd4j.valueArrayOf()方法的使用及代码示例

x33g5p2x  于2022-01-24 转载在 其他  
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本文整理了Java中org.nd4j.linalg.factory.Nd4j.valueArrayOf()方法的一些代码示例，展示了Nd4j.valueArrayOf()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.valueArrayOf()方法的具体详情如下：
包路径：org.nd4j.linalg.factory.Nd4j
类名称：Nd4j
方法名：valueArrayOf

Nd4j.valueArrayOf介绍

[英]Creates a row vector ndarray with the specified value as the only value in the ndarray Some people may know this as np.full
[中]创建一个行向量ndarray，其中指定的值是ndarray中的唯一值。有些人可能知道这是np。满的

代码示例

代码示例来源：origin: deeplearning4j/nd4j

@Override
public INDArray doCreate(long[] shape, INDArray paramsView) {
  return Nd4j.valueArrayOf(shape,constant);
}

代码示例来源：origin: deeplearning4j/nd4j

/**
 * Returns an ndarray with 1 if the element is epsilon equals
 *
 * @param other the number to compare
 * @return a ndarray with the given
 * binary conditions
 */
@Override
public INDArray epsi(Number other) {
  INDArray otherArr = Nd4j.valueArrayOf(shape(), other.doubleValue());
  return epsi(otherArr);
}

代码示例来源：origin: deeplearning4j/nd4j

/**
 * Prepare the boundaries for processing
 * @param bounds the bounds
 * @param x the input in to the approximation
 * @return the lower and upper bounds as an array of ndarrays
 * (in that order) of the same shape as x
 */
public static INDArray[] prepareBounds(INDArray bounds,INDArray x) {
  return new INDArray[]  {Nd4j.valueArrayOf(x.shape(),bounds.getDouble(0)),
      Nd4j.valueArrayOf(x.shape(),bounds.getDouble(1))};
}

代码示例来源：origin: deeplearning4j/nd4j

/**
 * Append the given
 * array with the specified value size
 * along a particular axis
 * @param arr the array to append to
 * @param padAmount the pad amount of the array to be returned
 * @param val the value to append
 * @param axis the axis to append to
 * @return the newly created array
 */
public static INDArray prepend(INDArray arr, int padAmount, double val, int axis) {
  if (padAmount == 0)
    return arr;
  long[] paShape = ArrayUtil.copy(arr.shape());
  if (axis < 0)
    axis = axis + arr.shape().length;
  paShape[axis] = padAmount;
  INDArray concatArr = Nd4j.valueArrayOf(paShape, val);
  return Nd4j.concat(axis, concatArr, arr);
}

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

System.out.println(allOnes);
INDArray allTens = Nd4j.valueArrayOf(nRows, nColumns, 10.0);
System.out.println("\nNd4j.valueArrayOf(nRows, nColumns, 10.0)");
System.out.println(allTens);

代码示例来源：origin: deeplearning4j/nd4j

/**
 * Append the given
 * array with the specified value size
 * along a particular axis
 * @param arr the array to append to
 * @param padAmount the pad amount of the array to be returned
 * @param val the value to append
 * @param axis the axis to append to
 * @return the newly created array
 */
public static INDArray append(INDArray arr, int padAmount, double val, int axis) {
  if (padAmount == 0)
    return arr;
  long[] paShape = ArrayUtil.copy(arr.shape());
  if (axis < 0)
    axis = axis + arr.shape().length;
  paShape[axis] = padAmount;
  INDArray concatArray = Nd4j.valueArrayOf(paShape, val);
  return Nd4j.concat(axis, arr, concatArray);
}

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

public static void main(String[] args){
  int nRows = 3;
  int nCols = 5;
  long rngSeed = 12345;
  //Generate random numbers between -1 and +1
  INDArray random = Nd4j.rand(nRows, nCols, rngSeed).muli(2).subi(1);
  System.out.println("Array values:");
  System.out.println(random);
  //For example, we can conditionally replace values less than 0.0 with 0.0:
  INDArray randomCopy = random.dup();
  BooleanIndexing.replaceWhere(randomCopy, 0.0, Conditions.lessThan(0.0));
  System.out.println("After conditionally replacing negative values:\n" + randomCopy);
  //Or conditionally replace NaN values:
  INDArray hasNaNs = Nd4j.create(new double[]{1.0,1.0,Double.NaN,1.0});
  BooleanIndexing.replaceWhere(hasNaNs,0.0, Conditions.isNan());
  System.out.println("hasNaNs after replacing NaNs with 0.0:\n" + hasNaNs);
  //Or we can conditionally copy values from one array to another:
  randomCopy = random.dup();
  INDArray tens = Nd4j.valueArrayOf(nRows, nCols, 10.0);
  BooleanIndexing.replaceWhere(randomCopy, tens, Conditions.lessThan(0.0));
  System.out.println("Conditionally copying values from array 'tens', if original value is less than 0.0\n" + randomCopy);
  //One simple task is to count the number of values that match the condition
  MatchCondition op = new MatchCondition(random, Conditions.greaterThan(0.0));
  int countGreaterThanZero = Nd4j.getExecutioner().exec(op,Integer.MAX_VALUE).getInt(0);  //MAX_VALUE = "along all dimensions" or equivalently "for entire array"
  System.out.println("Number of values matching condition 'greater than 0': " + countGreaterThanZero);
}

代码示例来源：origin: deeplearning4j/nd4j

/**
 * A getter for the allocated ndarray
 * with this {@link SDVariable}.
 *
 * This getter will lazy initialize an array if one is not found
 * based on the associated shape and {@link WeightInitScheme}
 * if neither are found, an {@link ND4JIllegalStateException}
 * is thrown.
 *
 * If a {@link DifferentialFunction} is defined, note that
 * its getArr() method is called instead.
 * @return the {@link INDArray} associated with this variable.
 */
public INDArray getArr() {
  if(sameDiff.arrayAlreadyExistsForVarName(getVarName()))
    return sameDiff.getArrForVarName(getVarName());
  //initialize value if it's actually a scalar constant (zero or 1 typically...)
  if(getScalarValue() != null && ArrayUtil.prod(getShape()) == 1) {
    INDArray arr = Nd4j.valueArrayOf(getShape(),
        getScalarValue().doubleValue());
    sameDiff.associateArrayWithVariable(arr,this);
  }
  else if(sameDiff.getShapeForVarName(getVarName()) == null)
    return null;
  else {
    INDArray newAlloc = getWeightInitScheme().create(sameDiff.getShapeForVarName(getVarName()));
    sameDiff.associateArrayWithVariable(newAlloc,this);
  }
  return sameDiff.getArrForVarName(getVarName());
}

代码示例来源：origin: deeplearning4j/nd4j

arrayShape = new int[]{};
  INDArray array = Nd4j.valueArrayOf(arrayShape, (double) val);
  return array;
} else if (tfTensor.getInt64ValCount() > 0) {
    arrayShape = new int[]{};
  INDArray array = Nd4j.valueArrayOf(arrayShape, (double) val);
  return array;
} else if (tfTensor.getFloatValCount() > 0) {

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

print("ARange", stepOfThreeTillTen);
INDArray allEights = Nd4j.valueArrayOf(new int[] {2,3}, 8);
print("2x3 Eights", allEights);
print("Concatenated arrays on dimension 1", concatenatedAxisOne);
INDArray [] verticalSplit = CustomOperations.split(Nd4j.valueArrayOf(new int[] {9, 9}, 9),
  3);
print("Vertical Split", verticalSplit);
INDArray [] horizontalSplit = CustomOperations.hsplit(Nd4j.valueArrayOf(new int[]{10, 10}, 10),
  5);
print("Horizontal Split", horizontalSplit);

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

protected INDArray createBias(int nOut, double biasInit, INDArray biasParamView, boolean initializeParameters) {
  if (initializeParameters) {
    INDArray ret = Nd4j.valueArrayOf(nOut, biasInit);
    biasParamView.assign(ret);
  }
  return biasParamView;
}

代码示例来源：origin: improbable-research/keanu

public static INDArray valueArrayOf(long[] shape, double value, DataBuffer.Type bufferType) {
  Nd4j.setDataType(bufferType);
  switch (shape.length) {
    case 0:
      return scalar(value, bufferType);
    case 1:
      return reshapeToVector(Nd4j.valueArrayOf(shape, value));
    default:
      return Nd4j.valueArrayOf(shape, value);
  }
}

代码示例来源：origin: org.nd4j/nd4j-api

/**
 * Returns an ndarray with 1 if the element is epsilon equals
 *
 * @param other the number to compare
 * @return a ndarray with the given
 * binary conditions
 */
@Override
public INDArray epsi(Number other) {
  INDArray otherArr = Nd4j.valueArrayOf(shape(), other.doubleValue());
  return epsi(otherArr);
}

代码示例来源：origin: improbable-research/keanu

private static INDArray performOperationWithScalarTensorPreservingShape(INDArray left, INDArray right, BiFunction<INDArray, INDArray, INDArray> operation) {
  if (left.length() == 1 || right.length() == 1) {
    long[] resultShape = Shape.broadcastOutputShape(left.shape(), right.shape());
    INDArray result = (left.length() == 1) ?
      operation.apply(Nd4j.valueArrayOf(right.shape(), left.getDouble(0)), right) :
      operation.apply(left, Nd4j.valueArrayOf(left.shape(), right.getDouble(0)));
    return result.reshape(resultShape);
  } else {
    return operation.apply(left, right);
  }
}

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

protected INDArray createVisibleBias(NeuralNetConfiguration conf, INDArray visibleBiasView,
        boolean initializeParameters) {
  org.deeplearning4j.nn.conf.layers.BasePretrainNetwork layerConf =
          (org.deeplearning4j.nn.conf.layers.BasePretrainNetwork) conf.getLayer();
  if (initializeParameters) {
    INDArray ret = Nd4j.valueArrayOf(layerConf.getNIn(), layerConf.getVisibleBiasInit());
    visibleBiasView.assign(ret);
  }
  return visibleBiasView;
}

代码示例来源：origin: org.nd4j/nd4j-api

/**
 * Append the given
 * array with the specified value size
 * along a particular axis
 * @param arr the array to append to
 * @param padAmount the pad amount of the array to be returned
 * @param val the value to append
 * @param axis the axis to append to
 * @return the newly created array
 */
public static INDArray append(INDArray arr, int padAmount, double val, int axis) {
  if (padAmount == 0)
    return arr;
  int[] paShape = ArrayUtil.copy(arr.shape());
  if (axis < 0)
    axis = axis + arr.shape().length;
  paShape[axis] = padAmount;
  INDArray concatArray = Nd4j.valueArrayOf(paShape, val);
  return Nd4j.concat(axis, arr, concatArray);
}

代码示例来源：origin: org.nd4j/nd4j-api

/**
 * Append the given
 * array with the specified value size
 * along a particular axis
 * @param arr the array to append to
 * @param padAmount the pad amount of the array to be returned
 * @param val the value to append
 * @param axis the axis to append to
 * @return the newly created array
 */
public static INDArray prepend(INDArray arr, int padAmount, double val, int axis) {
  if (padAmount == 0)
    return arr;
  int[] paShape = ArrayUtil.copy(arr.shape());
  if (axis < 0)
    axis = axis + arr.shape().length;
  paShape[axis] = padAmount;
  INDArray concatArr = Nd4j.valueArrayOf(paShape, val);
  return Nd4j.concat(axis, concatArr, arr);
}

代码示例来源：origin: org.nd4j/nd4j-cuda-10.0

ret = Nd4j.zeros(retShape);
} else {
  ret = Nd4j.valueArrayOf(retShape, op.zeroDouble());

代码示例来源：origin: org.nd4j/nd4j-cuda-7.5

protected void buildZ(IndexAccumulation op, int... dimension) {
  Arrays.sort(dimension);
  for (int i = 0; i < dimension.length; i++) {
    if (dimension[i] < 0)
      dimension[i] += op.x().rank();
  }
  //do op along all dimensions
  if (dimension.length == op.x().rank())
    dimension = new int[] {Integer.MAX_VALUE};
  int[] retShape = Shape.wholeArrayDimension(dimension) ? new int[] {1, 1}
          : ArrayUtil.removeIndex(op.x().shape(), dimension);
  //ensure vector is proper shape
  if (retShape.length == 1) {
    if (dimension[0] == 0)
      retShape = new int[] {1, retShape[0]};
    else
      retShape = new int[] {retShape[0], 1};
  } else if (retShape.length == 0) {
    retShape = new int[] {1, 1};
  }
  INDArray ret = null;
  if (Math.abs(op.zeroDouble()) < Nd4j.EPS_THRESHOLD) {
    ret = Nd4j.zeros(retShape);
  } else {
    ret = Nd4j.valueArrayOf(retShape, op.zeroDouble());
  }
  op.setZ(ret);
}

代码示例来源：origin: improbable-research/keanu

private static INDArray executeNd4jTransformOpWithPreservedScalarTensorShape(INDArray mask, INDArray right, DataBuffer.Type bufferType, QuadFunction<INDArray, INDArray, INDArray, Long, BaseTransformOp> baseTransformOpConstructor) {
  if (mask.length() == 1 || right.length() == 1) {
    long[] resultShape = Shape.broadcastOutputShape(mask.shape(), right.shape());
    if (mask.length() == 1) {
      mask = Nd4j.valueArrayOf(right.shape(), mask.getDouble(0));
      Nd4j.getExecutioner().exec(
        baseTransformOpConstructor.apply(mask, right, mask, mask.length())
      );
    } else {
      Nd4j.getExecutioner().exec(
        baseTransformOpConstructor.apply(mask,
          valueArrayOf(mask.shape(), right.getDouble(0), bufferType),
          mask,
          mask.length()
        )
      );
    }
    return mask.reshape(resultShape);
  } else {
    Nd4j.getExecutioner().exec(
      baseTransformOpConstructor.apply(mask, right, mask, mask.length())
    );
    return mask;
  }
}

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