本文整理了Java中org.nd4j.linalg.factory.Nd4j.sizeOfDataType()方法的一些代码示例，展示了Nd4j.sizeOfDataType()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.sizeOfDataType()方法的具体详情如下：
包路径：org.nd4j.linalg.factory.Nd4j
类名称：Nd4j
方法名：sizeOfDataType

Nd4j.sizeOfDataType介绍

[英]This method returns sizeOf(currentDataType), in bytes
[中]此方法返回sizeOf（currentDataType），以字节为单位

代码示例

代码示例来源：origin: deeplearning4j/nd4j

/**
 * This method returns amount of shared memory required for this specific Aggregate.
 * PLEASE NOTE: this method is especially important for CUDA backend. On CPU backend it might be ignored, depending on Aggregate.
 *
 * @return
 */
@Override
public int getSharedMemorySize() {
  return (vectorLength * Nd4j.sizeOfDataType()) + 512;
}

代码示例来源：origin: deeplearning4j/nd4j

@Override
public int getSharedMemorySize() {
  return (vectorLength * Nd4j.sizeOfDataType() * 2) + 512;
}

代码示例来源：origin: deeplearning4j/nd4j

/**
 * This method returns amount of shared memory required for this specific Aggregate.
 * PLEASE NOTE: this method is especially important for CUDA backend. On CPU backend it might be ignored, depending on Aggregate.
 *
 * @return
 */
@Override
public int getSharedMemorySize() {
  return (getThreadsPerInstance() * Nd4j.sizeOfDataType()) + 512;
}

代码示例来源：origin: deeplearning4j/nd4j

/**
 * This method returns amount of shared memory required for this specific Aggregate.
 * PLEASE NOTE: this method is especially important for CUDA backend. On
 * CPU backend it might be ignored, depending on Aggregate.
 *
 * @return
 */
@Override
public int getSharedMemorySize() {
  return (getThreadsPerInstance() * Nd4j.sizeOfDataType()) + 256;
}

代码示例来源：origin: deeplearning4j/nd4j

/**
 * This method returns amount of shared memory required for this specific Aggregate.
 * PLEASE NOTE: this method is especially important for CUDA backend. On CPU backend it might be ignored, depending on Aggregate.
 *
 * @return
 */
@Override
public int getSharedMemorySize() {
  return (getThreadsPerInstance() * Nd4j.sizeOfDataType()) + 512;
}

代码示例来源：origin: deeplearning4j/nd4j

/**
 * This method returns sizeOf(currentDataType), in bytes
 *
 * @return number of bytes per element
 */
public static int sizeOfDataType() {
  return sizeOfDataType(Nd4j.dataType());
}

代码示例来源：origin: deeplearning4j/nd4j

/**
 * This method returns memory used by this DataSet
 *
 * @return
 */
@Override
public long getMemoryFootprint() {
  long reqMem = features.lengthLong() * Nd4j.sizeOfDataType();
  reqMem += labels == null ? 0 : labels.lengthLong() * Nd4j.sizeOfDataType();
  reqMem += featuresMask == null ? 0 : featuresMask.lengthLong() * Nd4j.sizeOfDataType();
  reqMem += labelsMask == null ? 0 : labelsMask.lengthLong() * Nd4j.sizeOfDataType();
  return reqMem;
}

代码示例来源：origin: deeplearning4j/nd4j

/**
 * This method returns memory used by this DataSet
 *
 * @return
 */
@Override
public long getMemoryFootprint() {
  long reqMem = 0;
  for (INDArray f : features)
    reqMem += f == null ? 0 : f.lengthLong() * Nd4j.sizeOfDataType();
  if (featuresMaskArrays != null)
    for (INDArray f : featuresMaskArrays)
      reqMem += f == null ? 0 : f.lengthLong() * Nd4j.sizeOfDataType();
  if (labelsMaskArrays != null)
    for (INDArray f : labelsMaskArrays)
      reqMem += f == null ? 0 : f.lengthLong() * Nd4j.sizeOfDataType();
  if (labels != null)
    for (INDArray f : labels)
      reqMem += f == null ? 0 : f.lengthLong() * Nd4j.sizeOfDataType();
  return reqMem;
}

代码示例来源：origin: deeplearning4j/nd4j

requiredMemory += div;
long numElements = requiredMemory / Nd4j.sizeOfDataType(type);

代码示例来源：origin: deeplearning4j/nd4j

protected static DataBuffer internalCreateBuffer(double[] data) {
  val perfX = PerformanceTracker.getInstance().helperStartTransaction();
  val buffer = Nd4j.createBuffer(data);
  PerformanceTracker.getInstance().helperRegisterTransaction(0, perfX, data.length * Nd4j.sizeOfDataType(), MemcpyDirection.HOST_TO_HOST);
  return buffer;
}

代码示例来源：origin: deeplearning4j/nd4j

protected static DataBuffer internalCreateBuffer(float[] data, long offset) {
  val perfX = PerformanceTracker.getInstance().helperStartTransaction();
  val buffer = Nd4j.createBuffer(data, offset);
  PerformanceTracker.getInstance().helperRegisterTransaction(0, perfX, data.length * Nd4j.sizeOfDataType(), MemcpyDirection.HOST_TO_HOST);
  return buffer;
}

代码示例来源：origin: deeplearning4j/nd4j

protected static DataBuffer internalCreateBuffer(double[] data, long offset) {
  val perfX = PerformanceTracker.getInstance().helperStartTransaction();
  val buffer = Nd4j.createBuffer(data, offset);
  PerformanceTracker.getInstance().helperRegisterTransaction(0, perfX, data.length * Nd4j.sizeOfDataType(), MemcpyDirection.HOST_TO_HOST);
  return buffer;
}

代码示例来源：origin: deeplearning4j/nd4j

protected static DataBuffer internalCreateBuffer(int[] data, long offset) {
  val perfX = PerformanceTracker.getInstance().helperStartTransaction();
  val buffer = Nd4j.createBuffer(data, offset);
  PerformanceTracker.getInstance().helperRegisterTransaction(0, perfX, data.length * Nd4j.sizeOfDataType(), MemcpyDirection.HOST_TO_HOST);
  return buffer;
}

代码示例来源：origin: deeplearning4j/nd4j

protected static DataBuffer internalCreateBuffer(float[] data) {
  val perfX = PerformanceTracker.getInstance().helperStartTransaction();
  val buffer = Nd4j.createBuffer(data);
  PerformanceTracker.getInstance().helperRegisterTransaction(0, perfX, data.length * Nd4j.sizeOfDataType(), MemcpyDirection.HOST_TO_HOST);
  return buffer;
}

代码示例来源：origin: deeplearning4j/nd4j

protected static DataBuffer internalCreateBuffer(int[] data) {
  val perfX = PerformanceTracker.getInstance().helperStartTransaction();
  val buffer = Nd4j.createBuffer(data);
  PerformanceTracker.getInstance().helperRegisterTransaction(0, perfX, data.length * Nd4j.sizeOfDataType(), MemcpyDirection.HOST_TO_HOST);
  return buffer;
}

代码示例来源：origin: deeplearning4j/nd4j

/**
 *
 * @param data
 * @param shape
 * @param stride
 * @param offset
 * @param ordering
 */
public BaseNDArray(float[] data, int[] shape, int[] stride, long offset, char ordering) {
  setShapeInformation(Nd4j.getShapeInfoProvider().createShapeInformation(shape, stride, offset,
      Shape.elementWiseStride(shape, stride, ordering == 'f'), ordering));
  if (data != null && data.length > 0) {
    val perfD = PerformanceTracker.getInstance().helperStartTransaction();
    this.data = internalCreateBuffer(data, offset);
    PerformanceTracker.getInstance().helperRegisterTransaction(0, perfD, data.length * Nd4j.sizeOfDataType(), MemcpyDirection.HOST_TO_HOST);
    if (offset >= data.length)
      throw new IllegalArgumentException("invalid offset: must be < data.length");
  }
  init(shape, stride);
}

代码示例来源：origin: org.nd4j/nd4j-api

@Override
public int getSharedMemorySize() {
  return (vectorLength * Nd4j.sizeOfDataType() * 2) + 512;
}

代码示例来源：origin: org.nd4j/nd4j-api

/**
 * This method returns amount of shared memory required for this specific Aggregate.
 * PLEASE NOTE: this method is especially important for CUDA backend. On CPU backend it might be ignored, depending on Aggregate.
 *
 * @return
 */
@Override
public int getSharedMemorySize() {
  return (getThreadsPerInstance() * Nd4j.sizeOfDataType()) + 512;
}

代码示例来源：origin: org.nd4j/nd4j-api

/**
 * This method returns sizeOf(currentDataType), in bytes
 *
 * @return number of bytes per element
 */
public static int sizeOfDataType() {
  return sizeOfDataType(Nd4j.dataType());
}

代码示例来源：origin: org.nd4j/nd4j-api

/**
 * This method returns amount of shared memory required for this specific Aggregate.
 * PLEASE NOTE: this method is especially important for CUDA backend. On
 * CPU backend it might be ignored, depending on Aggregate.
 *
 * @return
 */
@Override
public int getSharedMemorySize() {
  return (getThreadsPerInstance() * Nd4j.sizeOfDataType()) + 256;
}