Erweiterung um Optimierungverfahren und Aktivierungsfunktionen.

main.py

-import yaml
 from datetime import datetime
-
+import yaml
 from src.data.copyData import copy_images_with_exclusion
 from src.data.modelData import get_test_data, get_train_data, define_augmentation_rules
 from src.enum.activierungsfunktionEnum import Aktivierungsfunktion
@@ -12,7 +11,6 @@ from src.knn.defineKNN import define_model
 from src.result.createExcelFile import create_excel_result, get_excel_workbook, get_excel_worksheet, save_excel, \
     average_evaluate_cross_validation
 from src.result.customCallback import CustomCallback
-from src.result.plotResult import plot_values
 
 
 def run_cross_validation():
@@ -23,10 +21,14 @@ def run_cross_validation():
 
     # Initialisieren der KNN-Modellbau Eigenschaften
     for optimization_method in Optimierungsverfahren:
-        for activation_function_1 in Aktivierungsfunktion:
-            if activation_function_1.name in config['knn']['exception_activation_funktion_1']:
+        for activation_function_2 in Aktivierungsfunktion:
+            if activation_function_2.name in config['knn']['exception_activation_funktion_1']:
                 continue
             for activation_function_128 in Aktivierungsfunktion:
+                if check_if_kombination_is_not_allowed(
+                        optimization_method.name, activation_function_128.name, activation_function_2.name, config
+                ):
+                    continue
                 model_evaluate_metrics = []
                 training_duration_models = []
                 workbook = get_excel_workbook()
@@ -34,7 +36,7 @@ def run_cross_validation():
                 r = 1
                 config_knn = ConfigKNN(
                     excluded_folder=Markt.Kein_Markt,
-                    activation_function_1_units=activation_function_1,
+                    activation_function_1_units=activation_function_2,
                     activation_function_128_units=activation_function_128,
                     optimization_method=optimization_method
                 )
@@ -98,5 +100,13 @@ def run_model(config, config_knn, worksheet, r):
     return worksheet, r, evaluate_metrics, training_duration_model
 
 
+def check_if_kombination_is_not_allowed(opt, act128, act2, config):
+    currentKombination = [opt, act128, act2]
+    ignoreKombinationList = config['knn']['ignoreKnnCombinations']
+    if currentKombination in ignoreKombinationList:
+        return True
+    return False
+
+
 # entry point
-run_cross_validation()
+run_cross_validation()
\ No newline at end of file

ressources/config/config.yaml

 bilder:
   # original_path: "assets/Bilder/Datengrundlage-Reduziert-Test/"
-  # original_path: "assets/Bilder/Datengrundlage/"
-  original_path: "assets/Bilder/Datengrundlage-Augmentiert/"
+  original_path: "assets/Bilder/Datengrundlage/"
+  # original_path: "assets/Bilder/Datengrundlage-Augmentiert/"
   knn_path: "assets/Bilder/AktuelleTrainingsUndTestdaten/"
 knn:
   epochs: 10
   exception_activation_funktion_1:
     ['ReLU']
+  ignoreKnnCombinations:
+    [
+      ['SGD','ReLU','sigmoid'],
+      ['SGD','sigmoid','sigmoid'],
+      ['Adam','ReLU','sigmoid'],
+      ['Adam','sigmoid','sigmoid']
+    ]
 result:
   plot_path: "ressources/results/plot/"
   excel_path: "ressources/results/excel/"

src/enum/activierungsfunktionEnum.py

@@ -4,3 +4,5 @@ from enum import Enum
 class Aktivierungsfunktion(Enum):
     ReLU = 0,
     sigmoid = 1,
+    tanh = 2,
+    softmax = 3

src/enum/optimierungsverfahren.py

@@ -3,4 +3,5 @@ from enum import Enum
 
 class Optimierungsverfahren(Enum):
     SGD = 0,
-    Adam = 1
+    Adam = 1,
+    Ftrl = 2

src/knn/defineKNN.py

@@ -38,6 +38,10 @@ def get_next_layer(units, activation_function, flat_or_dense):
                                      kernel_initializer='he_uniform')(flat_or_dense)
     if activation_function == Aktivierungsfunktion.sigmoid:
         return tf.keras.layers.Dense(units, activation=activation_function.name)(flat_or_dense)
+    if activation_function == Aktivierungsfunktion.tanh:
+        return tf.keras.layers.Dense(units, activation=activation_function.name)(flat_or_dense)
+    if activation_function == Aktivierungsfunktion.softmax:
+        return tf.keras.layers.Dense(units, activation=activation_function.name)(flat_or_dense)
     return None
 
 
@@ -46,5 +50,6 @@ def get_optimization_method(optimization_method):
         return tf.keras.optimizers.SGD(learning_rate=0.001, momentum=0.9)
     if optimization_method == Optimierungsverfahren.Adam:
         return tf.keras.optimizers.Adam(learning_rate=0.001)
+    if optimization_method == Optimierungsverfahren.Ftrl:
+        return tf.keras.optimizers.Ftrl(learning_rate=0.001)
     return None
-