(self, error, alpha=1.0, beta=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):
        """
        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.update:
            self.dW[:] = 0
            self.be.compound_bprop_lut(self.nin, self.inputs, error, self.outputs_t,
                                       self.dW, self.pad_idx, alpha, beta)
        return self.deltas
def bprop ( self , error , alpha = 1.0 , beta = 0 ) : if self . update : self . dW [ : ] = 0 self . be . compound_bprop_lut ( self . nin , self . inputs , error , self . outputs_t , self . dW , self . pad_idx , alpha , beta ) return self . deltas