diff --git a/src/gpu_experiments.py b/src/gpu_experiments.py
index 5988ca6..2731898 100644
--- a/src/gpu_experiments.py
+++ b/src/gpu_experiments.py
@@ -4,6 +4,7 @@ import matplotlib.pyplot as plt
 import matplotlib.colors as mcolors
 import matplotlib.patches as mpatches
 import pickle
+import csv
 
 
 ###### Experimental parameters
@@ -27,6 +28,8 @@ df_results = pd.DataFrame(
 )
 
 data_file.close()
+
+# remove charge as species
 df_design.drop("Charge", axis=1, inplace=True, errors="ignore")
 df_results.drop("Charge", axis=1, inplace=True, errors="ignore")
 
@@ -122,36 +125,11 @@ model.compile(
 
 ###### train model
 
-epochs = 200
+epochs = 3
 
 history = model_training(model, epochs=epochs)
 
 
-###### evaluate model
-
-
-results = mass_balance_evaluation(model, X_test, preprocess)
-mass_balance_ratio(results, threshold=1e-5)
-
-def test_model(model, X_test, y_test):
-    
-    X_test.reset_index(inplace=True, drop=True)
-    y_test.reset_index(inplace=True, drop=True)
-    all = model.evaluate(X_test.loc[:, X_test.columns != "Class"], y_test.loc[:, y_test.columns != "Class"])
-    class_0 = model.evaluate(X_test[X_test["Class"] == 0].iloc[:, X_test.columns != "Class"], y_test[X_test["Class"] == 0].iloc[:, y_test.columns != "Class"])
-    class_1 = model.evaluate(
-    X_test[X_test["Class"] == 1].iloc[:, :-1], y_test[X_test["Class"] == 1].iloc[:, :-1])
-    
-    print("metric all data: ", all)
-    print("metric class 0: ", class_0)
-    print("metric class 1: ", class_1)
-    
-    
-test_model(model, X_test, y_test)
-
-
-
-
 ###### save model and history
 
 delimiter = "_"
@@ -160,4 +138,38 @@ file_name = "history_" + idx_string
 with open('../results/'+file_name, 'wb') as file_pi:
     pickle.dump(history.history, file_pi)
 
-model.save_weights("../results/models/model_"+idx_string + ".weights.h5")
\ No newline at end of file
+model.save_weights("../results/models/model_"+idx_string + ".weights.h5")
+
+
+###### evaluate model
+
+results = mass_balance_evaluation(model, X_test, preprocess)
+proportion = mass_balance_ratio(results, threshold=1e-5)
+
+X_test.reset_index(inplace=True, drop=True)
+y_test.reset_index(inplace=True, drop=True)
+all_classes = model.evaluate(X_test.loc[:, X_test.columns != "Class"], y_test.loc[:, y_test.columns != "Class"])
+class_0 = model.evaluate(X_test[X_test["Class"] == 0].iloc[:, X_test.columns != "Class"], y_test[X_test["Class"] == 0].iloc[:, y_test.columns != "Class"])
+class_1 = model.evaluate(X_test[X_test["Class"] == 1].iloc[:, :-1], y_test[X_test["Class"] == 1].iloc[:, :-1])
+
+print("metric all data: ", all_classes)
+print("metric class 0: ", class_0)
+print("metric class 1: ", class_1)
+
+
+    # Save evaluation results to a file
+results_file_name = "../results/evaluation_" + idx_string + ".csv"
+with open(results_file_name, mode="w", newline="") as results_file:
+    writer = csv.writer(results_file)
+    writer.writerow(["Metric", "Value"])
+    writer.writerow(["Mass balance fulfilled (all classes)", proportion["overall"]])
+    writer.writerow(["Mass balance fulfilled (class 0)", proportion["class_0"]])
+    writer.writerow(["Mass balance fulfilled (class 1)", proportion["class_1"]])
+    writer.writerow(["Metrics (all classes)", all_classes])
+    writer.writerow(["Metrics (class 0)", class_0])
+    writer.writerow(["Metrics (class 1)", class_1])
+    
+results_file.close() 
+
+
+