(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.deltas:
            self.be.bprop_conv(self.nglayer, self.W, error, self.deltas,
                               alpha=alpha, beta=beta, layer_op=self)
        self.be.update_conv(self.nglayer, self.inputs, error, self.dW, layer_op=self)
        return self.deltas
def bprop ( self , error , alpha = 1.0 , beta = 0.0 ) : if self . deltas : self . be . bprop_conv ( self . nglayer , self . W , error , self . deltas , alpha = alpha , beta = beta , layer_op = self ) self . be . update_conv ( self . nglayer , self . inputs , error , self . dW , layer_op = self ) return self . deltas