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

Nd4j.dataType介绍

[英]Returns the data opType used for the runtime
[中]返回用于运行时的数据类型

代码示例

代码示例来源：origin: deeplearning4j/nd4j

public ArrayDescriptor(int[] array) {
  this.intArray = array;
  this.dtype = DTYPE.INT;
  this.bufferType = Nd4j.dataType();
}

代码示例来源：origin: deeplearning4j/nd4j

public ArrayDescriptor(long[] array) {
  this.longArray = array;
  this.dtype = DTYPE.LONG;
  this.bufferType = Nd4j.dataType();
}

代码示例来源：origin: deeplearning4j/nd4j

public ArrayDescriptor(float[] array) {
  this.floatArray = array;
  this.dtype = DTYPE.FLOAT;
  this.bufferType = Nd4j.dataType();
}

代码示例来源：origin: deeplearning4j/nd4j

/**
 * Returns the data opType for this ndarray
 *
 * @return the data opType for this ndarray
 */
@Override
public DataBuffer.Type dtype() {
  return Nd4j.dataType();
}

代码示例来源：origin: deeplearning4j/nd4j

public ArrayDescriptor(double[] array) {
  this.doubleArray = array;
  this.dtype = DTYPE.DOUBLE;
  this.bufferType = Nd4j.dataType();
}

代码示例来源：origin: deeplearning4j/nd4j

@Override
public Boolean apply(Number input) {
  if (Nd4j.dataType() == DataBuffer.Type.DOUBLE)
    return input.doubleValue() != value.doubleValue();
  else
    return input.floatValue() != value.floatValue();
}

代码示例来源：origin: deeplearning4j/nd4j

@Override
public Boolean apply(Number input) {
  if (Nd4j.dataType() == DataBuffer.Type.DOUBLE)
    return input.doubleValue() == value.doubleValue();
  else
    return input.floatValue() == value.floatValue();
}

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

protected DataBuffer.TypeEx getGlobalTypeEx() {
  DataBuffer.Type type = Nd4j.dataType();
  return convertType(type);
}

代码示例来源：origin: deeplearning4j/nd4j

@Override
public void init(INDArray x, INDArray y, INDArray z, long n) {
  super.init(x, y, z, n);
  if (Nd4j.dataType() == DataBuffer.Type.DOUBLE) {
    this.extraArgs = new Object[] {zeroDouble()};
  } else if (Nd4j.dataType() == DataBuffer.Type.FLOAT) {
    this.extraArgs = new Object[] {zeroFloat()};
  } else if (Nd4j.dataType() == DataBuffer.Type.HALF) {
    this.extraArgs = new Object[] {zeroHalf()};
  }
}

代码示例来源：origin: deeplearning4j/nd4j

/**
 * Create double based on real and imaginary
 *
 * @param real real component
 * @param imag imag component
 * @return
 */
public static IComplexNumber createComplexNumber(Number real, Number imag) {
  if (dataType() == DataBuffer.Type.FLOAT)
    return INSTANCE.createFloat(real.floatValue(), imag.floatValue());
  return INSTANCE.createDouble(real.doubleValue(), imag.doubleValue());
}

代码示例来源：origin: deeplearning4j/nd4j

@Override
public INDArray trueScalar(Number value) {
  val dtype = Nd4j.dataType();
  switch (dtype) {
    case DOUBLE:
      return create(new double[] {value.doubleValue()}, new int[] {}, new int[] {}, 0);
    case FLOAT:
      return create(new float[] {value.floatValue()}, new int[] {}, new int[] {}, 0);
    case HALF:
      return create(new float[] {value.floatValue()}, new int[] {}, new int[] {}, 0);
    default:
      throw new UnsupportedOperationException("Unsupported data type: [" + dtype + "]");
  }
}

代码示例来源：origin: deeplearning4j/nd4j

/**
 * Returns the number of bytes
 * for the graph
 *
 * @return
 */
public long memoryForGraph() {
  return numElements() * DataTypeUtil.lengthForDtype(Nd4j.dataType());
}

代码示例来源：origin: deeplearning4j/nd4j

/**
 * Create a scalar nd array with the specified value and offset
 *
 * @param value the value of the scalar
 * @return the scalar nd array
 */
@Override
public INDArray scalar(double value) {
  if (Nd4j.dataType() == DataBuffer.Type.DOUBLE)
    return create(new double[] {value}, new int[] {1, 1}, new int[] {1, 1}, 0);
  else
    return scalar((float) value);
}

代码示例来源：origin: deeplearning4j/nd4j

public static DataBuffer createBufferDetached(float[] data) {
  DataBuffer ret;
  if (dataType() == DataBuffer.Type.FLOAT)
    ret = DATA_BUFFER_FACTORY_INSTANCE.createFloat(data);
  else if (dataType() == DataBuffer.Type.HALF)
    ret = DATA_BUFFER_FACTORY_INSTANCE.createHalf(data);
  else
    ret = DATA_BUFFER_FACTORY_INSTANCE.createDouble(ArrayUtil.toDoubles(data));
  logCreationIfNecessary(ret);
  return ret;
}

代码示例来源：origin: deeplearning4j/nd4j

public static DataBuffer createBufferDetached(double[] data) {
  DataBuffer ret;
  if (dataType() == DataBuffer.Type.DOUBLE)
    ret = DATA_BUFFER_FACTORY_INSTANCE.createDouble(data);
  else if (dataType() == DataBuffer.Type.HALF)
    ret = DATA_BUFFER_FACTORY_INSTANCE.createHalf(ArrayUtil.toFloats(data));
  else
    ret = DATA_BUFFER_FACTORY_INSTANCE.createFloat(ArrayUtil.toFloats(data));
  logCreationIfNecessary(ret);
  return ret;
}

代码示例来源：origin: deeplearning4j/nd4j

@Override
  public long getRequiredBatchMemorySize() {
    long result = maxIntArrays() * maxIntArraySize() * 4;
    result += maxArguments() * 8; // pointers
    result += maxShapes() * 8; // pointers
    result += maxIndexArguments() * 4;
    result += maxRealArguments() * (Nd4j.dataType() == DataBuffer.Type.DOUBLE ? 8
            : Nd4j.dataType() == DataBuffer.Type.FLOAT ? 4 : 2);
    result += 5 * 4; // numArgs

    return result * Batch.getBatchLimit();
  }
}

代码示例来源：origin: deeplearning4j/nd4j

public static INDArray toNDArray(int[] nums) {
  if (Nd4j.dataType() == DataBuffer.Type.DOUBLE) {
    double[] doubles = ArrayUtil.toDoubles(nums);
    INDArray create = Nd4j.create(doubles, new int[] {1, nums.length});
    return create;
  } else {
    float[] doubles = ArrayUtil.toFloats(nums);
    INDArray create = Nd4j.create(doubles, new int[] {1, nums.length});
    return create;
  }
}

代码示例来源：origin: deeplearning4j/nd4j

public static INDArray toNDArray(long[] nums) {
  if (Nd4j.dataType() == DataBuffer.Type.DOUBLE) {
    double[] doubles = ArrayUtil.toDoubles(nums);
    INDArray create = Nd4j.create(doubles, new int[] {1, nums.length});
    return create;
  } else {
    float[] doubles = ArrayUtil.toFloats(nums);
    INDArray create = Nd4j.create(doubles, new int[] {1, nums.length});
    return create;
  }
}

代码示例来源：origin: deeplearning4j/nd4j

public static INDArray toNDArray(int[][] nums) {
  if (Nd4j.dataType() == DataBuffer.Type.DOUBLE) {
    double[] doubles = ArrayUtil.toDoubles(nums);
    INDArray create = Nd4j.create(doubles, new int[] {nums[0].length, nums.length});
    return create;
  } else {
    float[] doubles = ArrayUtil.toFloats(nums);
    INDArray create = Nd4j.create(doubles, new int[] {nums[0].length, nums.length});
    return create;
  }
}