(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.compound_dot(A=self.W.T, B=error, C=self.deltas, alpha=alpha, beta=beta)
        self.be.compound_dot(A=error, B=self.inputs.T, C=self.dW)
        return self.deltas
def bprop ( self , error , alpha = 1.0 , beta = 0.0 ) : if self . deltas : self . be . compound_dot ( A = self . W . T , B = error , C = self . deltas , alpha = alpha , beta = beta ) self . be . compound_dot ( A = error , B = self . inputs . T , C = self . dW ) return self . deltas