(self, inputs, inference=False)
return self.outputs
Compute the actual fprop from op-tree, update the global estimations

        Arguments:
            inputs (Tensor): input data
            inference (bool): is inference only

        Returns:
            Tensor: output data
Compute the actual fprop from op-tree, update the global estimations
Compute the actual fprop from op - tree update the global estimations
def fprop(self, inputs, inference=False):
        """
        Compute the actual fprop from op-tree, update the global estimations

        Arguments:
            inputs (Tensor): input data
            inference (bool): is inference only

        Returns:
            Tensor: output data
        """
        if inference:
            return self._fprop_inference(inputs)
        self.init_buffers(inputs)
        if self.allparams is None:
            self.init_params(self.nfm)
            self.fprop_op_tree = self.get_forward_optree()

        # the actual f-prop
        self.y[:] = self.fprop_op_tree

        # for inference
        self.gmean[:] = (self.gmean * self.rho + (1.0 - self.rho) * self.be.mean(self.x, axis=1))
        self.gvar[:] = (self.gvar * self.rho + (1.0 - self.rho) * self.be.var(self.x, axis=1))

        return self.outputs
def fprop ( self , inputs , inference = False ) : if inference : return self . _fprop_inference ( inputs ) self . init_buffers ( inputs ) if self . allparams is None : self . init_params ( self . nfm ) self . fprop_op_tree = self . get_forward_optree ( ) # the actual f-prop self . y [ : ] = self . fprop_op_tree # for inference self . gmean [ : ] = ( self . gmean * self . rho + ( 1.0 - self . rho ) * self . be . mean ( self . x , axis = 1 ) ) self . gvar [ : ] = ( self . gvar * self . rho + ( 1.0 - self . rho ) * self . be . var ( self . x , axis = 1 ) ) return self . outputs