diff --git a/mala/network/predictor.py b/mala/network/predictor.py
index 5a4a44588..0e1c6e484 100644
--- a/mala/network/predictor.py
+++ b/mala/network/predictor.py
@@ -1,4 +1,4 @@
-"""Tester class for testing a network."""
+"""Predictor class."""
 
 import numpy as np
 import torch
@@ -59,13 +59,6 @@ def predict_from_qeout(self, path_to_file, gather_ldos=False):
         predicted_ldos : numpy.array
             Precicted LDOS for these atomic positions.
         """
-        self.data.grid_dimension = self.parameters.inference_data_grid
-        self.data.grid_size = (
-            self.data.grid_dimension[0]
-            * self.data.grid_dimension[1]
-            * self.data.grid_dimension[2]
-        )
-
         self.data.target_calculator.read_additional_calculation_data(
             path_to_file, "espresso-out"
         )
@@ -230,18 +223,17 @@ def _forward_snap_descriptors(
             )
 
             for i in range(0, self.number_of_batches_per_snapshot):
-                inputs = snap_descriptors[
-                    i
-                    * self.parameters.mini_batch_size : (i + 1)
-                    * self.parameters.mini_batch_size
-                ]
-                inputs = inputs.to(self.parameters._configuration["device"])
-                predicted_outputs[
-                    i
-                    * self.parameters.mini_batch_size : (i + 1)
-                    * self.parameters.mini_batch_size
-                ] = self.data.output_data_scaler.inverse_transform(
-                    self.network(inputs).to("cpu"), as_numpy=True
+                sl = slice(
+                    i * self.parameters.mini_batch_size,
+                    (i + 1) * self.parameters.mini_batch_size,
+                )
+                inputs = snap_descriptors[sl].to(
+                    self.parameters._configuration["device"]
+                )
+                predicted_outputs[sl] = (
+                    self.data.output_data_scaler.inverse_transform(
+                        self.network(inputs).to("cpu"), as_numpy=True
+                    )
                 )
 
             # Restricting the actual quantities to physical meaningful values,