diff --git a/training/hyper/hyper_iso17.py b/training/hyper/hyper_iso17.py
index 5c4b87a8..4e436744 100644
--- a/training/hyper/hyper_iso17.py
+++ b/training/hyper/hyper_iso17.py
@@ -53,15 +53,16 @@
                 "batch_size": 64, "epochs": 1000, "validation_freq": 1, "verbose": 2,
                 "callbacks": [
                     {"class_name": "kgcnn>LinearWarmupExponentialLRScheduler", "config": {
-                        "lr_start": 1e-03, "gamma": 0.995, "epo_warmup": 1, "verbose": 1, "steps_per_epoch": 8062}}
+                        "lr_start": 1e-03,
+                        "gamma": 0.995, "epo_warmup": 1, "verbose": 1, "steps_per_epoch": 8062}}
                 ]
             },
             "compile": {
                 "optimizer": {"class_name": "Adam", "config": {"learning_rate": 1e-03}},
-                "loss_weights": {"energy": 1.0, "force": 49.0}
+                "loss_weights": {"energy": 0.02, "force": 0.98}
             },
             "scaler": {"class_name": "EnergyForceExtensiveLabelScaler",
-                       "config": {"standardize_scale": False}},
+                       "config": {"standardize_scale": True}},
         },
         "data": {
             "dataset": {
diff --git a/training/hyper/hyper_md17.py b/training/hyper/hyper_md17.py
index e5db4b74..9273a64c 100644
--- a/training/hyper/hyper_md17.py
+++ b/training/hyper/hyper_md17.py
@@ -61,30 +61,30 @@
             },
             "compile": {
                 "optimizer": {"class_name": "Adam", "config": {"learning_rate": 1e-03}},
-                "loss_weights": {"energy": 0.1, "force": 4.9}
+                "loss_weights": {"energy": 0.02, "force": 0.98}
             },
             "scaler": {"class_name": "EnergyForceExtensiveLabelScaler",
                        "config": {"standardize_scale": True}},
         },
-        "data": {
-            "dataset": {
-                "class_name": "MD17Dataset",
-                "module_name": "kgcnn.data.datasets.MD17Dataset",
-                "config": {
-                    "trajectory_name": trajectory_name
-                },
-                "methods": [
-                    {"rename_property_on_graphs": {"old_property_name": "E", "new_property_name": "energy"}},
-                    {"rename_property_on_graphs": {"old_property_name": "F", "new_property_name": "force"}},
-                    {"rename_property_on_graphs": {"old_property_name": "z", "new_property_name": "atomic_number"}},
-                    {"rename_property_on_graphs": {"old_property_name": "R", "new_property_name": "node_coordinates"}},
-                    {"map_list": {"method": "set_range", "max_distance": 5, "max_neighbours": 10000,
-                                  "node_coordinates": "node_coordinates"}},
-                    {"map_list": {"method": "count_nodes_and_edges", "total_edges": "total_ranges",
-                                  "count_edges": "range_indices", "count_nodes": "atomic_number",
-                                  "total_nodes": "total_nodes"}},
-                ]
+        "dataset": {
+            "class_name": "MD17Dataset",
+            "module_name": "kgcnn.data.datasets.MD17Dataset",
+            "config": {
+                "trajectory_name": trajectory_name
             },
+            "methods": [
+                {"rename_property_on_graphs": {"old_property_name": "E", "new_property_name": "energy"}},
+                {"rename_property_on_graphs": {"old_property_name": "F", "new_property_name": "force"}},
+                {"rename_property_on_graphs": {"old_property_name": "z", "new_property_name": "atomic_number"}},
+                {"rename_property_on_graphs": {"old_property_name": "R", "new_property_name": "node_coordinates"}},
+                {"map_list": {"method": "set_range", "max_distance": 5, "max_neighbours": 10000,
+                              "node_coordinates": "node_coordinates"}},
+                {"map_list": {"method": "count_nodes_and_edges", "total_edges": "total_ranges",
+                              "count_edges": "range_indices", "count_nodes": "atomic_number",
+                              "total_nodes": "total_nodes"}},
+            ]
+        },
+        "data": {
         },
         "info": {
             "postfix": "",
diff --git a/training/hyper/hyper_md17_revised.py b/training/hyper/hyper_md17_revised.py
index 412b4231..3d3acf4f 100644
--- a/training/hyper/hyper_md17_revised.py
+++ b/training/hyper/hyper_md17_revised.py
@@ -61,33 +61,33 @@
             },
             "compile": {
                 "optimizer": {"class_name": "Adam", "config": {"learning_rate": 1e-03}},
-                "loss_weights": {"energy": 1.0, "force": 49.0}
+                "loss_weights": {"energy": 0.02, "force": 0.98}
             },
             "scaler": {"class_name": "EnergyForceExtensiveLabelScaler",
                        "config": {"standardize_scale": True}},
         },
-        "data": {
-            "dataset": {
-                "class_name": "MD17RevisedDataset",
-                "module_name": "kgcnn.data.datasets.MD17RevisedDataset",
-                "config": {
-                    # toluene, aspirin, malonaldehyde, benzene, ethanol
-                    "trajectory_name": trajectory_name
-                },
-                "methods": [
-                    {"rename_property_on_graphs": {"old_property_name": "energies", "new_property_name": "energy"}},
-                    {"rename_property_on_graphs": {"old_property_name": "forces", "new_property_name": "force"}},
-                    {"rename_property_on_graphs": {"old_property_name": "nuclear_charges",
-                                                   "new_property_name": "atomic_number"}},
-                    {"rename_property_on_graphs": {"old_property_name": "coords",
-                                                   "new_property_name": "node_coordinates"}},
-                    {"map_list": {"method": "set_range", "max_distance": 5, "max_neighbours": 10000,
-                                  "node_coordinates": "node_coordinates"}},
-                    {"map_list": {"method": "count_nodes_and_edges", "total_edges": "total_ranges",
-                                  "count_edges": "range_indices", "count_nodes": "atomic_number",
-                                  "total_nodes": "total_nodes"}},
-                ]
+        "dataset": {
+            "class_name": "MD17RevisedDataset",
+            "module_name": "kgcnn.data.datasets.MD17RevisedDataset",
+            "config": {
+                # toluene, aspirin, malonaldehyde, benzene, ethanol
+                "trajectory_name": trajectory_name
             },
+            "methods": [
+                {"rename_property_on_graphs": {"old_property_name": "energies", "new_property_name": "energy"}},
+                {"rename_property_on_graphs": {"old_property_name": "forces", "new_property_name": "force"}},
+                {"rename_property_on_graphs": {"old_property_name": "nuclear_charges",
+                                               "new_property_name": "atomic_number"}},
+                {"rename_property_on_graphs": {"old_property_name": "coords",
+                                               "new_property_name": "node_coordinates"}},
+                {"map_list": {"method": "set_range", "max_distance": 5, "max_neighbours": 10000,
+                              "node_coordinates": "node_coordinates"}},
+                {"map_list": {"method": "count_nodes_and_edges", "total_edges": "total_ranges",
+                              "count_edges": "range_indices", "count_nodes": "atomic_number",
+                              "total_nodes": "total_nodes"}},
+            ]
+        },
+        "data": {
         },
         "info": {
             "postfix": "",
diff --git a/training/train_force.py b/training/train_force.py
index d568b808..49f9aa0b 100644
--- a/training/train_force.py
+++ b/training/train_force.py
@@ -148,10 +148,13 @@
 
     # Compile model with optimizer and loss
     model.compile(**hyper.compile(
-        loss={"energy": "mean_absolute_error", "force": ForceMeanAbsoluteError()},
+        loss={
+            "energy": "mean_absolute_error",
+            "force": "mean_absolute_error"  #  ForceMeanAbsoluteError()
+        },
         metrics=scaled_metrics))
 
-    print(model.predict(x_test))
+    model.predict(x_test)
     # Model summary
     model.summary()
     print(" Compiled with jit: %s" % model._jit_compile)  # noqa