org.nd4j.linalg.factory.Nd4j.createUninitialized()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.createUninitialized()方法的一些代码示例，展示了Nd4j.createUninitialized()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.createUninitialized()方法的具体详情如下：
包路径：org.nd4j.linalg.factory.Nd4j
类名称：Nd4j
方法名：createUninitialized

Nd4j.createUninitialized介绍

[英]This method creates an uninitialized ndarray of specified length and default ordering. PLEASE NOTE: Do not use this method unless you're 100% sure why you use it.
[中]此方法创建指定长度和默认顺序的未初始化数据数组。请注意：除非你100%确定为什么要使用这种方法，否则不要使用这种方法。

代码示例

代码示例来源：origin: deeplearning4j/nd4j

  1. @Override
  2. public INDArray nextGaussian(char order, int[] shape) {
  3.   INDArray array = Nd4j.createUninitialized(shape, order);
  4.   GaussianDistribution op = new GaussianDistribution(array, 0.0, 1.0);
  5.   Nd4j.getExecutioner().exec(op, this);
  6.   return array;
  7. }

代码示例来源：origin: deeplearning4j/nd4j

  1. @Override
  2. public INDArray nextGaussian(char order, long[] shape) {
  3.   INDArray array = Nd4j.createUninitialized(shape, order);
  4.   GaussianDistribution op = new GaussianDistribution(array, 0.0, 1.0);
  5.   Nd4j.getExecutioner().exec(op, this);
  6.   return array;
  7. }

代码示例来源：origin: deeplearning4j/nd4j

  1. @Override
  2. public INDArray nextDouble(char order, int[] shape) {
  3.   INDArray array = Nd4j.createUninitialized(shape, order);
  4.   UniformDistribution op = new UniformDistribution(array, 0.0, 1.0);
  5.   Nd4j.getExecutioner().exec(op, this);
  6.   return array;
  7. }

代码示例来源：origin: deeplearning4j/nd4j

  1. @Override
  2. public INDArray nextFloat(char order, long[] shape) {
  3.   INDArray array = Nd4j.createUninitialized(shape, order);
  4.   UniformDistribution op = new UniformDistribution(array, 0.0, 1.0);
  5.   Nd4j.getExecutioner().exec(op, this);
  6.   return array;
  7. }

代码示例来源：origin: deeplearning4j/nd4j

  1. @Override
  2. public INDArray nextFloat(char order, int[] shape) {
  3.   INDArray array = Nd4j.createUninitialized(shape, order);
  4.   UniformDistribution op = new UniformDistribution(array, 0.0, 1.0);
  5.   Nd4j.getExecutioner().exec(op, this);
  6.   return array;
  7. }

代码示例来源：origin: deeplearning4j/nd4j

  1. @Override
  2. public INDArray nextDouble(char order, long[] shape) {
  3.   INDArray array = Nd4j.createUninitialized(shape, order);
  4.   UniformDistribution op = new UniformDistribution(array, 0.0, 1.0);
  5.   Nd4j.getExecutioner().exec(op, this);
  6.   return array;
  7. }

代码示例来源：origin: deeplearning4j/nd4j

  1. /**
  2.  * Atan2 operation, new INDArray instance will be returned
  3.  * Note the order of x and y parameters is opposite to that of java.lang.Math.atan2
  4.  *
  5.  * @param x the abscissa coordinate
  6.  * @param y the ordinate coordinate
  7.  * @return the theta from point (r, theta) when converting (x,y) from to cartesian to polar coordinates
  8.  */
  9. public static INDArray atan2(@NonNull INDArray x, @NonNull INDArray y) {
  10.   return Nd4j.getExecutioner()
  11.           .execAndReturn(new OldAtan2Op(x, y, Nd4j.createUninitialized(x.shape(), x.ordering())));
  12. }

代码示例来源：origin: deeplearning4j/nd4j

  1. public static INDArray and(INDArray x, INDArray y) {
  2.   INDArray z = Nd4j.createUninitialized(x.shape(), x.ordering());
  3.   Nd4j.getExecutioner().exec(new And(x, y, z, 0.0));
  4.   return z;
  5. }

代码示例来源：origin: deeplearning4j/nd4j

  1. public static INDArray not(INDArray x) {
  2.   INDArray z = Nd4j.createUninitialized(x.shape(), x.ordering());
  3.   Nd4j.getExecutioner().exec(new Not(x, z, 0.0));
  4.   return z;
  5. }

代码示例来源：origin: deeplearning4j/nd4j

  1. public static INDArray xor(INDArray x, INDArray y) {
  2.   INDArray z = Nd4j.createUninitialized(x.shape(), x.ordering());
  3.   Nd4j.getExecutioner().exec(new Xor(x, y, z, 0.0));
  4.   return z;
  5. }

代码示例来源：origin: deeplearning4j/nd4j

  1. public static INDArray or(INDArray x, INDArray y) {
  2.   INDArray z = Nd4j.createUninitialized(x.shape(), x.ordering());
  3.   Nd4j.getExecutioner().exec(new Or(x, y, z, 0.0));
  4.   return z;
  5. }

代码示例来源：origin: deeplearning4j/nd4j

  1. /**
  2.  * Negate each element.
  3.  */
  4. @Override
  5. public INDArray neg() {
  6.   return Nd4j.getExecutioner().exec(new Negative(this, Nd4j.createUninitialized(this.shape(), this.ordering())))
  7.       .z();
  8. }

代码示例来源：origin: deeplearning4j/nd4j

  1. public static INDArray reverse(INDArray x, boolean dup) {
  2.   return Nd4j.getExecutioner().exec(new OldReverse(x, dup ? Nd4j.createUninitialized(x.shape(), x.ordering()) : x))
  3.           .z();
  4. }

代码示例来源：origin: deeplearning4j/nd4j

  1. /**
  2.  * Like the scipy function tri.
  3.  * From the scipy documentation:
  4.  *  An array with ones at and below the given diagonal and zeros elsewhere.
  5.  * @param n number of rows in the array
  6.  * @param m number of columns in the array ( can be just equal to n)
  7.  * @param k    The sub-diagonal at and below which the array is filled.
  8. `k` = 0 is the main diagonal, while `k` < 0 is below it,
  9. and `k` > 0 is above.  The default is 0.
  10.  * @return
  11.  */
  12. public static INDArray tri(int n,int m,int k) {
  13.   /*
  14.   INDArray mRet = Transforms.greaterThanOrEqual(arange(n),arange(-k,m - k));
  15.   return mRet;
  16.   */
  17.   INDArray ret = Nd4j.createUninitialized(n, m);
  18.   val op = DynamicCustomOp.builder("tri")
  19.       .addIntegerArguments(n, m, k)
  20.       .addOutputs(ret)
  21.       .build();
  22.   Nd4j.getExecutioner().exec(op);
  23.   return ret;
  24. }

代码示例来源：origin: deeplearning4j/nd4j

  1. /**
  2.  * Meshgrid op. Returns a pair of arrays where values are broadcast on a 2d grid.<br>
  3.  * For example, if x = [1,2,3,4] and y = [5,6,7], then:<br>
  4.  * out[0] =<br>
  5.  * [1,2,3,4]<br>
  6.  * [1,2,3,4]<br>
  7.  * [1,2,3,4]<br>
  8.  * <br>
  9.  * out[1] =<br>
  10.  * [5,5,5,5]<br>
  11.  * [6,6,6,6]<br>
  12.  * [7,7,7,7]<br>
  13.  * <br>
  14.  *
  15.  * @param x X array input
  16.  * @param y Y array input
  17.  * @return INDArray[] of length 2, shape [y.length, x.length]
  18.  */
  19. public static INDArray[] meshgrid(@NonNull INDArray x, @NonNull INDArray y){
  20.   Preconditions.checkArgument(x.isVectorOrScalar(), "X must be a vector");
  21.   Preconditions.checkArgument(y.isVectorOrScalar(), "Y must be a vector");
  22.   INDArray xOut = Nd4j.createUninitialized(y.length(), x.length());
  23.   INDArray yOut = Nd4j.createUninitialized(y.length(), x.length());
  24.   CustomOp op = DynamicCustomOp.builder("meshgrid")
  25.       .addInputs(x, y)
  26.       .addOutputs(xOut, yOut)
  27.       .build();
  28.   Nd4j.getExecutioner().exec(op);
  29.   return new INDArray[]{xOut, yOut};
  30. }

代码示例来源：origin: deeplearning4j/nd4j

  1. INDArray result = Nd4j.createUninitialized(m.shape());
  2.     .build();
  3. Nd4j.getExecutioner().exec(op);

代码示例来源：origin: deeplearning4j/nd4j

  1. return Nd4j.createUninitialized(shape).assign(this.getDouble(0));
  2.   Nd4j.getExecutioner().exec(new Tile(new INDArray[]{this.dup(this.ordering())},new INDArray[]{result},repeat));
  3. } else
  4.   Nd4j.getExecutioner().exec(new Tile(new INDArray[]{this},new INDArray[]{result},repeat));

代码示例来源：origin: deeplearning4j/nd4j

  1. int oW = (int) Math.ceil(img.size(3) * 1.f / sx);
  2.   output = Nd4j.createUninitialized(new long[] {img.size(0), img.size(1), kh, kw, oH, oW}, 'c');
  3.   int oW = ((int) img.size(3) - (kw + (kw-1)*(1-1)) + 2*pw)/sx + 1;
  4.   output = Nd4j.createUninitialized(new long[] {img.size(0), img.size(1), kh, kw, oH, oW}, 'c');
  5.         .build()).build();
  6. Nd4j.getExecutioner().exec(im2col);
  7. return im2col.outputArguments()[0];

代码示例来源：origin: deeplearning4j/nd4j

  1. Nd4j.getExecutioner().commit();
  2.     Nd4j.getExecutioner().commit();
  3.     DataBuffer buffer = Nd4j.createBuffer(this.lengthLong(), false);
  4.     INDArray copy = Nd4j.createUninitialized(this.shape(), this.ordering());
  5.     copy.assign(this);
  6.     Nd4j.getExecutioner().commit();
  7.     copy = Nd4j.createUninitialized(this.shape(), this.ordering());
  8.     copy.assign(this);
  9.     Nd4j.getExecutioner().commit();

代码示例来源：origin: deeplearning4j/nd4j

  1. /**
  2.  * in place addition of two matrices
  3.  *
  4.  * @param other  the second ndarray to add
  5.  * @param result the result ndarray
  6.  * @return the result of the addition
  7.  */
  8. @Override
  9. public INDArray addi(INDArray other, INDArray result) {
  10.   if (other.isScalar()) {
  11.     return result.addi(other.getDouble(0), result);
  12.   }
  13.   if (isScalar()) {
  14.     return other.addi(getDouble(0), result);
  15.   }
  16.   if(!Shape.shapeEquals(this.shape(),other.shape())) {
  17.     int[] broadcastDimensions = Shape.getBroadcastDimensions(this.shape(),other.shape());
  18.     result = Nd4j.createUninitialized(Shape.broadcastOutputShape(this.shape(),other.shape()));
  19.     Nd4j.getExecutioner().exec(new BroadcastAddOp(this,other,result,broadcastDimensions),broadcastDimensions);
  20.     return result;
  21.   }
  22.   LinAlgExceptions.assertSameShape(other, result);
  23.   Nd4j.getExecutioner().exec(new OldAddOp(this, other, result, length()));
  24.   if (Nd4j.ENFORCE_NUMERICAL_STABILITY)
  25.     Nd4j.clearNans(result);
  26.   return result;
  27. }

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