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Python NervanaGPU.sig方法代码示例

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
开发者ID:YouVentures,项目名称:neon,代码行数:69,代码来源:gpu.py


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