diff --git a/learn_it.py b/learn_it.py
index a167f5b..a06d4db 100644
--- a/learn_it.py
+++ b/learn_it.py
@@ -452,43 +452,45 @@ with torch.no_grad():
                                 eps_xy[id], dtype=torch.float64
                             )
 
-                        # Save the new values
-                        np.save(
-                            cfg.weight_path
-                            + "/Weight_L"
-                            + str(id)
-                            + "_S"
-                            + str(cfg.learning_step)
-                            + ".npy",
-                            network[id].weights.detach().numpy(),
-                        )
-
-                        try:
-                            tb.add_histogram(
-                                "Weights " + str(id),
-                                network[id].weights,
-                                cfg.learning_step,
+                        if cfg.network_structure.w_trainable[id] is True:
+                            # Save the new values
+                            np.save(
+                                cfg.weight_path
+                                + "/Weight_L"
+                                + str(id)
+                                + "_S"
+                                + str(cfg.learning_step)
+                                + ".npy",
+                                network[id].weights.detach().numpy(),
                             )
-                        except ValueError:
-                            pass
 
-                        np.save(
-                            cfg.eps_xy_path
-                            + "/EpsXY_L"
-                            + str(id)
-                            + "_S"
-                            + str(cfg.learning_step)
-                            + ".npy",
-                            network[id].epsilon_xy.detach().numpy(),
-                        )
-                        try:
-                            tb.add_histogram(
-                                "Epsilon XY " + str(id),
+                            try:
+                                tb.add_histogram(
+                                    "Weights " + str(id),
+                                    network[id].weights,
+                                    cfg.learning_step,
+                                )
+                            except ValueError:
+                                pass
+
+                        if cfg.network_structure.eps_xy_trainable[id] is True:
+                            np.save(
+                                cfg.eps_xy_path
+                                + "/EpsXY_L"
+                                + str(id)
+                                + "_S"
+                                + str(cfg.learning_step)
+                                + ".npy",
                                 network[id].epsilon_xy.detach().numpy(),
-                                cfg.learning_step,
                             )
-                        except ValueError:
-                            pass
+                            try:
+                                tb.add_histogram(
+                                    "Epsilon XY " + str(id),
+                                    network[id].epsilon_xy.detach().numpy(),
+                                    cfg.learning_step,
+                                )
+                            except ValueError:
+                                pass
 
                     # Let the torch learning rate scheduler update the
                     # learning rates of the optimiers