(self, error, alpha=1.0, beta=0.0)
return self.deltas
bprop for deconv is equivalent to fprop for conv.
        fprop_conv takes input and output as "I" and "O".
        for deconv, fprop_conv will take error as input and delta as output

        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
bprop for deconv is equivalent to fprop for conv.
        fprop_conv takes input and output as "I" and "O".
        for deconv, fprop_conv will take error as input and delta as output
bprop for deconv is equivalent to fprop for conv . fprop_conv takes input and output as I and O . for deconv fprop_conv will take error as input and delta as output
def bprop(self, error, alpha=1.0, beta=0.0):
        """
        bprop for deconv is equivalent to fprop for conv.
        fprop_conv takes input and output as "I" and "O".
        for deconv, fprop_conv will take error as input and delta as output

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