Final Codebasis

main.py

@@ -83,7 +83,7 @@ def run_model(config, config_knn, worksheet, r):
     test_it = get_test_data(datagen, config["bilder"]["knn_path"])
     print("Trainiere das Modell")
     # fit model
-    history = fit_model(config, model, train_it, test_it, callback)
+    fit_model(config, model, train_it, test_it, callback)
     # evaluate model
     print("Evaluiere das Modell")
     evaluate_metrics = evaluate_model(model, test_it, datagen, config, config_knn)
@@ -97,9 +97,9 @@ def run_model(config, config_knn, worksheet, r):
     # print("Erstelle den Plot Graphen")
     # plot_values(history, config)
     print("Inhalte in die Excel-Tabelle schreiben")
-    worksheet, r = create_excel_result(worksheet, r, callback, config, config_knn, evaluate_metrics,
+    r = create_excel_result(worksheet, r, callback, config, config_knn, evaluate_metrics,
                                        training_duration_model)
-    return worksheet, r, evaluate_metrics, training_duration_model
+    return r, evaluate_metrics, training_duration_model
 
 
 def check_if_kombination_is_not_allowed(opt, act128, act2, config):

ressources/config/config.yaml

@@ -4,13 +4,11 @@ bilder:
   # original_path: "assets/Bilder/Datengrundlage-Augmentiert/"
   knn_path: "assets/Bilder/AktuelleTrainingsUndTestdaten/"
 knn:
-  epochs: 10
+  epochs: 2
   exception_optimization_method:
-    ['SGD', 'Adam']
   exception_activation_funktion_2:
     ['ReLU']
   ignoreKnnCombinations:
-    []
 result:
   plot_path: "ressources/results/plot/"
   excel_path: "ressources/results/excel/"

src/data/pictureHandling.py

@@ -4,6 +4,7 @@ import tensorflow as tf
 from src.enum.marktEnum import Markt
 
 
+# Wird nicht mehr gebraucht
 def createPathWithPictures():
     # Pfad zu den 6 Ordnern
     main_folder_path = "/assets/Bilder/Datengrundlage"
@@ -26,7 +27,7 @@ def createPathWithPictures():
                     shutil.copy(image_path, new_subfolder_path)
 
 
-def generateAugmentedNotOOSPictures():
+def generate_augmented_not_oos_pictures():
     for markt in Markt:
         # Pfad zum Ordner "!OOS"
         folder_path = "assets/Bilder/Datengrundlage_Test/" + markt.name + "/"
@@ -62,8 +63,6 @@ def get_images():
         brightness_range=[0.5, 1.5],
         zoom_range=0.5,
     )
-    # specify imagenet mean values for centering
-    # datagen.mean = [124, 150, 130]
     return augmentedImageDefinition
 
 

src/knn/createKNN.py

@@ -3,7 +3,7 @@ from src.enum.marktEnum import Markt
 
 
 def fit_model(config, model, train_it, test_it, callback):
-    trained_model = model.fit(
+    model.fit(
         train_it,
         steps_per_epoch=len(train_it),
         validation_data=test_it,
@@ -12,7 +12,6 @@ def fit_model(config, model, train_it, test_it, callback):
         verbose=1,
         callbacks=[callback]
     )
-    return trained_model
 
 
 def evaluate_model(model, test_it, datagen, config, config_knn):

src/knn/defineKNN.py

@@ -47,11 +47,9 @@ def get_next_layer(units, activation_function, flat_or_dense):
 
 def get_optimization_method(optimization_method):
     if optimization_method == Optimierungsverfahren.SGD:
-        return tf.keras.optimizers.SGD(learning_rate=0.001, momentum=0.9)
+        return tf.keras.optimizers.experimental.SGD(learning_rate=0.001, momentum=0.9)
     if optimization_method == Optimierungsverfahren.Adam:
-        return tf.keras.optimizers.Adam()
+        return tf.keras.optimizers.AdamAdam()
     if optimization_method == Optimierungsverfahren.Ftrl:
-        return tf.keras.optimizers.legacy.Ftrl(learning_rate=0.001,
-                                               learning_rate_power=-0.5,
-                                               initial_accumulator_value=0.1)
+        return tf.keras.optimizers.legacy.Ftrl()
     return None

src/result/analyseExcel.py

@@ -129,5 +129,5 @@ def write_matrix_in_excel(sheet, unique_opt, unique_act, final_matrix, c):
 
 # Call the function with the folder path
 # swap_excel_cols("/Users/dwillers/Programmierung/Master/Okan-Server/Results Kopie/5050")
-# swap_file_cols("/Users/dwillers/Programmierung/Master/Okan-Server/Results Kopie/5050/Ftrl_softmax_softmax_03_02_23__03_19.xlsx")
+swap_file_cols("/Users/dwillers/Programmierung/Master/Okan-Server/Results Kopie/1090/SGD_sigmoid_ReLU_24_01_23__08_39.xlsx")
 # read_metrics("/Users/dwillers/Programmierung/Master/Okan-Server/Results Kopie/5050")

src/result/createExcelFile.py

-import time
-from datetime import datetime
+from datetime import datetime, timedelta
 
 import openpyxl
 
@@ -30,7 +29,6 @@ def create_excel_result(worksheet, r, callback, config, config_knn, evaluate_met
     r = r + 2
 
     # Schreiben Sie die Überschriften in die erste Zeile
-    # TODO: Ergänzung Recall 1, Recall 2
     worksheet.cell(row=r, column=1).value = 'epoch'
     worksheet.cell(row=r, column=2).value = 'training_loss'
     worksheet.cell(row=r, column=3).value = 'training_accuracy'
@@ -76,7 +74,7 @@ def create_excel_result(worksheet, r, callback, config, config_knn, evaluate_met
     worksheet.cell(row=r, column=4).value = evaluate_metrics[3]
     worksheet.cell(row=r, column=5).value = evaluate_metrics[2]
     r = r + 4
-    return worksheet, r
+    return r
 
 
 # TODO: Abschluss Statistik nach Cross-Validation von einer Konfiguration: Durchschnitt Accuracy, Recall 1, Recall 2, Loss, Duration für Evaluation
@@ -120,11 +118,9 @@ def sum_values_into_one_array(model_evaluate_metrics, training_duration_models):
     avg_array = [value / num_arrays for value in sum_array]
 
     # Jetzt durchschnittsdauer des Modelltrainings berechnen
-    current_time = datetime.now()
-    add_times = current_time
+    add_times = timedelta()
     for training_time in training_duration_models:
-        add_times = add_times + training_time
-    add_times = add_times - current_time
+        add_times += training_time
     # Durchschnitt berechnen
     average_duration = add_times / len(training_duration_models)
     # Ergebnis dem avg_array hinzufügen