(self, error, alpha=1.0, beta=0.0)
return self.deltas
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
        """
        if self.error_views is None:
            self.error_views = self.get_partitions(error, self.slices)
        self.be.bprop_mergebroadcast(
            self.ngLayer, self.layers, self.error_views, error,
            self.deltas, self.out_shape, alpha, beta, self.alphas, self.betas)
        return self.deltas
def bprop ( self , error , alpha = 1.0 , beta = 0.0 ) : if self . error_views is None : self . error_views = self . get_partitions ( error , self . slices ) self . be . bprop_mergebroadcast ( self . ngLayer , self . layers , self . error_views , error , self . deltas , self . out_shape , alpha , beta , self . alphas , self . betas ) return self . deltas