(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 not self.deltas:
            self.deltas = error
        self.deltas[:] = self.keep_mask * error * alpha * self._train_scaling + beta * error
        return self.deltas
def bprop ( self , error , alpha = 1.0 , beta = 0.0 ) : if not self . deltas : self . deltas = error self . deltas [ : ] = self . keep_mask * error * alpha * self . _train_scaling + beta * error return self . deltas