本文整理汇总了Python中nervanagpu.NervanaGPU.sig方法的典型用法代码示例。如果您正苦于以下问题：Python NervanaGPU.sig方法的具体用法？Python NervanaGPU.sig怎么用？Python NervanaGPU.sig使用的例子？那么, 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.sig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 sig [as 别名]
#.........这里部分代码省略.........
layer), or the outputs from the previous layer.
deltas (GPUTensor): The error values for this layer
op (string): The type of pooling operation to apply. We support
"max", "avg", "l2" currently.
ofmshape (tuple): Dimensions of each output feature map (typically
height and width).
ofmsize (int): Total size of each output feature map.
ofmlocs (GPUTensor): Indices giving the location of each element in
each output feature map stored in out.
fshape (tuple): Dimensions of each filter (typically height and
width).
fpsize (int): The size of each filter.
ifmshape (tuple): Dimensions of each input feature map (typically
height and width).
links (GPUTensor): Input receptive field indices.
nifm (int): Total number of input feature maps.
padding (int): Number of additional elements to include along each
dimension of each local receptive field during the
pooling operation.
stride (int): Number of neurons to shift the filter at each step.
bpropbuf (GPUTensor): Temporary storage buffer used to hold the
backpropagated error for a single receptive
field
"""
op = op.lower()
if op == "max":
self.ng.bprop_pool(layer=bpropbuf, I=inputs, E=deltas, grad_I=out,
repeat=1)
else:
raise AttributeError("unexpected pooling op type: %s", op)
def logistic(self, x, out):
"""
Logistic sigmoid nonlinearity, 1/(1+exp(-x))
Arguments:
x (GPUTensor): Input tensor
out (GPUTensor): Output tensor
"""
self.ng.sig(x, out=out)
return out
def rectlin(self, x, out):
"""
Rectified Linear nonlinearity
Arguments:
x (GPUTensor): Input tensor
out (GPUTensor): Output tensor
"""
self.ng.maximum(x, 0., out=out)
return out
def rectleaky(self, x, slope, out):
out[:] = self.ng.maximum(x, x*slope)
def rectleaky_derivative(self, x, slope, out):
out[:] = self.ng.greater(x, 0) * (1.0 - slope) + slope
def sum(self, tsr, axes, out):
"""
Sum