本文整理汇总了Python中nervanagpu.NervanaGPU.power方法的典型用法代码示例。如果您正苦于以下问题：Python NervanaGPU.power方法的具体用法？Python NervanaGPU.power怎么用？Python NervanaGPU.power使用的例子？那么恭喜您, 这里精选的方法代码示例或许可以为您提供帮助。您也可以进一步了解该方法所在类nervanagpu.NervanaGPU的用法示例。
在下文中一共展示了NervanaGPU.power方法的1个代码示例，这些例子默认根据受欢迎程度排序。您可以为喜欢或者感觉有用的代码点赞，您的评价将有助于系统推荐出更棒的Python代码示例。
示例1: GPU
# 需要导入模块: from nervanagpu import NervanaGPU [as 别名]
# 或者: from nervanagpu.NervanaGPU import power [as 别名]
#.........这里部分代码省略.........
order (int): The order or p upon which the norm is calculated.
Valid values include:
None, inf, -inf, 0, 1, -1, 2, -2, ...
axis (int): The axis along which to compute vector norms.
out (GPUTensor): where to write the results to. Must be
of the expected result shape.
Returns:
GPUTensor: p-norm of tsr along the specified axis.
Raises:
IndexError if invalid axis specified
AttributeError if invalid order specified
See Also:
`numpy.linalg.norm`
"""
if not isinstance(axis, int) or axis < 0 or axis >= len(tsr.shape):
raise IndexError("invalid axis value: %s", axis)
if not isinstance(order, (int, float)):
raise AttributeError("invalid order value: %s", order)
if out is None:
raise AttributeError("No output tensor speficied", order)
if order == float('Inf'):
self.ng.max(self.fabs(tsr), axis, out)
elif order == float('-Inf'):
self.ng.min(self.fabs(tsr), axis, out)
elif order == 0:
tmp = self.zeros(tsr.shape)
self.ng.not_equal(tsr, tmp, tmp)
self.ng.sum(tmp, axis, out)
else:
tmp = self.empty(tsr.shape)
self.ng.power(self.fabs(tsr), order, tmp)
self.ng.sum(tmp, axis, out)
self.ng.power(out, (1.0 / order), out)
return out
def mean(self, tsr, axes, out):
"""
Calculates the arithmetic mean of the elements along the specified
axes.
Arguments:
tsr (GPUTensor): Input tensor
axes (int): Axis along which the reduction is performed. If axes
is None, the tensor is flattened and reduced over
both dimensions.
out (GPUTensor): Output tensor
"""
if axes is None:
sze = tsr.shape[0]*tsr.shape[1]
self.ng.mean(tsr.reshape(sze, 1), axis=0, out=out)
else:
self.ng.mean(tsr, axis=axes, out=out)
return out
def min(self, tsr, axes, out):
"""
Calculates the minimum of the elements along the specified
axes.
Arguments:
tsr (GPUTensor): Input tensor
axes (int): Axis along which the reduction is performed. If axes
is None, the tensor is flattened and reduced over