(self, error, alpha=1.0, beta=0.0)
return self.error
Apply the backward pass transformation to the input data.

        Arguments:
            error (Tensor): deltas back propagated from the adjacent higher layer
            alpha (float, optional): scale to apply to input for activation
                                     gradient bprop.  Defaults to 1.0
            beta (float, optional): scale to apply to output activation
                                    gradient bprop.  Defaults to 0.0

        Returns:
            Tensor: deltas to propagate to the adjacent lower layer
Apply the backward pass transformation to the input data.
Apply the backward pass transformation to the input data .
def bprop(self, error, alpha=1.0, beta=0.0):
        """
        Apply the backward pass transformation to the input data.

        Arguments:
            error (Tensor): deltas back propagated from the adjacent higher layer
            alpha (float, optional): scale to apply to input for activation
                                     gradient bprop.  Defaults to 1.0
            beta (float, optional): scale to apply to output activation
                                    gradient bprop.  Defaults to 0.0

        Returns:
            Tensor: deltas to propagate to the adjacent lower layer
        """

        self.error.fill(0)

        if self.bprop_enabled:
            # # bprop through the roipooling layer
            self.be.roipooling_bprop(error, self.rois, self.error, self.max_idx,
                                     self.rois_per_img, self.fm_channel, self.fm_height,
                                     self.fm_width, self.roi_H, self.roi_W, self.spatial_scale)

        # bprop back through the imagenet layer container
        return self.error
def bprop ( self , error , alpha = 1.0 , beta = 0.0 ) : self . error . fill ( 0 ) if self . bprop_enabled : # # bprop through the roipooling layer self . be . roipooling_bprop ( error , self . rois , self . error , self . max_idx , self . rois_per_img , self . fm_channel , self . fm_height , self . fm_width , self . roi_H , self . roi_W , self . spatial_scale ) # bprop back through the imagenet layer container return self . error