(self, X, GT=None)
return recon, loss
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):
        """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.
        """
        is_training(True, session=self.sess)
        try:
            if GT is not None:
                _, loss, recon = self.sess.run(
                    (self.train_step, self.loss, self.x_reconstr),
                    feed_dict={self.x: X, self.gt: GT},
                )
            else:
                _, loss, recon = self.sess.run(
                    (self.train_step, self.loss, self.x_reconstr), feed_dict={self.x: X}
                )

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