(self, X, GT=None, S=None, compute_recon=False)
return recon, loss, loss_ae, loss_simplification, loss_projection
Trains the model with mini-batches of input data.
        If GT is not None, then the reconstruction loss compares the output of the net that is fed X, with the GT.
        This can be useful when training for instance a denoising auto-encoder.
        Returns:
            The loss of the mini-batch.
            The reconstructed (output) point-clouds.
Trains the model with mini-batches of input data.
        If GT is not None, then the reconstruction loss compares the output of the net that is fed X, with the GT.
        This can be useful when training for instance a denoising auto-encoder.
        Returns:
            The loss of the mini-batch.
            The reconstructed (output) point-clouds.
Trains the model with mini - batches of input data . If GT is not None then the reconstruction loss compares the output of the net that is fed X with the GT . This can be useful when training for instance a denoising auto - encoder . Returns : The loss of the mini - batch . The reconstructed ( output ) point - clouds .
def partial_fit(self, X, GT=None, S=None, compute_recon=False):
        """Trains the model with mini-batches of input data.
        If GT is not None, then the reconstruction loss compares the output of the net that is fed X, with the GT.
        This can be useful when training for instance a denoising auto-encoder.
        Returns:
            The loss of the mini-batch.
            The reconstructed (output) point-clouds.
        """
        if compute_recon:
            x_reconstr = self.x_reconstr
        else:
            x_reconstr = self.no_op

        is_training(True, session=self.sess)
        try:
            if GT is None:
                feed_dict = {self.x: X}
            else:
                feed_dict = {self.x: X, self.gt: GT}

            if S is not None:
                feed_dict[self.s] = S

            (
                _,
                loss,
                loss_ae,
                loss_simplification,
                loss_projection,
                recon,
            ) = self.sess.run(
                (
                    self.train_step,
                    self.loss,
                    self.loss_ae,
                    self.loss_simplification,
                    self.loss_projection,
                    x_reconstr,
                ),
                feed_dict=feed_dict,
            )

            is_training(False, session=self.sess)
        except Exception:
            raise
        finally:
            is_training(False, session=self.sess)
        return recon, loss, loss_ae, loss_simplification, loss_projection
def partial_fit ( self , X , GT = None , S = None , compute_recon = False ) : if compute_recon : x_reconstr = self . x_reconstr else : x_reconstr = self . no_op is_training ( True , session = self . sess ) try : if GT is None : feed_dict = { self . x : X } else : feed_dict = { self . x : X , self . gt : GT } if S is not None : feed_dict [ self . s ] = S ( _ , loss , loss_ae , loss_simplification , loss_projection , recon , ) = self . sess . run ( ( self . train_step , self . loss , self . loss_ae , self . loss_simplification , self . loss_projection , x_reconstr , ) , feed_dict = feed_dict , ) is_training ( False , session = self . sess ) except Exception : raise finally : is_training ( False , session = self . sess ) return recon , loss , loss_ae , loss_simplification , loss_projection