(self, fprop_in, scale_weights=0)
return self.fprop_out
Conv Layer forward propagation.

        Arguments:
            fprop_in (Tensor): Inputs
            scale_weights (float): Scale weights by scale/mean if nonzero

        Returns:
            fprop_out (Tensor): Output activations
        or
            (self.fprop_out, self.batch_sum) (tuple): Tuple with batch_sum
                added as the second entry.
Conv Layer forward propagation.
Conv Layer forward propagation .
def fprop(self, fprop_in, scale_weights=0):
        """
        Conv Layer forward propagation.

        Arguments:
            fprop_in (Tensor): Inputs
            scale_weights (float): Scale weights by scale/mean if nonzero

        Returns:
            fprop_out (Tensor): Output activations
        or
            (self.fprop_out, self.batch_sum) (tuple): Tuple with batch_sum
                added as the second entry.
        """
        fprop_in = super(ConvLayer, self).fprop(fprop_in)
        self.lib.fprop_conv(self, fprop_in, self.weights, self.fprop_out, bsum=self.batch_sum)

        if scale_weights:
            self.scale_weights(scale_weights)
            self.fprop(fprop_in)

        if self.bsum:
            return (self.fprop_out, self.batch_sum)
        return self.fprop_out
def fprop ( self , fprop_in , scale_weights = 0 ) : fprop_in = super ( ConvLayer , self ) . fprop ( fprop_in ) self . lib . fprop_conv ( self , fprop_in , self . weights , self . fprop_out , bsum = self . batch_sum ) if scale_weights : self . scale_weights ( scale_weights ) self . fprop ( fprop_in ) if self . bsum : return ( self . fprop_out , self . batch_sum ) return self . fprop_out