(self, error, alpha=1.0, beta=0.0)
return self.deltas
Compute gradients for learning gamma and beta as well as layer weights.

        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:
Compute gradients for learning gamma and beta as well as layer weights.
Compute gradients for learning gamma and beta as well as layer weights .
def bprop(self, error, alpha=1.0, beta=0.0):
        """
        Compute gradients for learning gamma and beta as well as layer weights.

        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:

        """
        assert alpha == 1.0 and beta == 0.0
        if not self.error_view:
            self.error_view = error.reshape((self.nfm, -1))

        self.be.compound_bprop_bn(self.deltas, self.grad_gamma, self.grad_beta,
                                  self.error_view,
                                  self.inputs, self.xsum, self.xvar, self.gamma,
                                  self.eps, binary=self.binary, layer=self.nglayer)
        return self.deltas
def bprop ( self , error , alpha = 1.0 , beta = 0.0 ) : assert alpha == 1.0 and beta == 0.0 if not self . error_view : self . error_view = error . reshape ( ( self . nfm , - 1 ) ) self . be . compound_bprop_bn ( self . deltas , self . grad_gamma , self . grad_beta , self . error_view , self . inputs , self . xsum , self . xvar , self . gamma , self . eps , binary = self . binary , layer = self . nglayer ) return self . deltas