Commit 2f264f7c authored by Dennis Willers's avatar Dennis Willers
Browse files

Recall eingebunden.

parent 9e9d1038

.gitignore

+2 −0
Original line number Original line Diff line number Diff line
@@ -53,3 +53,5 @@ Thumbs.db 




assets/Bilder/*

assets/Bilder/*

assets/Bilder/AktuelleTrainingsUndTestdaten/*

assets/Bilder/AktuelleTrainingsUndTestdaten/*

assets/Bilder/Datengrundlage/*

assets/Bilder/Datengrundlage/

main.py

+5 −3
Original line number Original line Diff line number Diff line
@@ -21,10 +21,12 @@ def run_model(): 
    with open("ressources/config/config.yaml", "r") as file:

    with open("ressources/config/config.yaml", "r") as file:

        config = yaml.load(file, Loader=yaml.FullLoader)

        config = yaml.load(file, Loader=yaml.FullLoader)

    # Initialisieren der KNN-Modellbau Eigenschaften

    # Initialisieren der KNN-Modellbau Eigenschaften

    config_knn = ConfigKNN(excluded_folder=Markt.MARKT_B,

    config_knn = ConfigKNN(

                           activation_function_1_units=Aktivierungsfunktion.Sigmoid,

        excluded_folder=Markt.Kein_Markt,

        activation_function_1_units=Aktivierungsfunktion.sigmoid,

        activation_function_128_units=Aktivierungsfunktion.ReLU,

        activation_function_128_units=Aktivierungsfunktion.ReLU,

                           optimization_method=Optimierungsverfahren.Adam)

        optimization_method=Optimierungsverfahren.SGD

    )





    print("Initialisiere Callback")

    print("Initialisiere Callback")

    # Erstellen Sie eine Instanz des benutzerdefinierten Callbacks

    # Erstellen Sie eine Instanz des benutzerdefinierten Callbacks

ressources/config/config.yaml

+2 −1
Original line number Original line Diff line number Diff line 
bilder:

bilder:

  original_path: "/Users/dwillers/Programmierung/Master/Evaluation_OOS-Erkennung/assets/Bilder/Datengrundlage-Reduziert-Test/"

  original_path: "/Users/dwillers/Programmierung/Master/Evaluation_OOS-Erkennung/assets/Bilder/Datengrundlage-Reduziert-Test/"

  # original_path: "/Users/dwillers/Programmierung/Master/Evaluation_OOS-Erkennung/assets/Bilder/Datengrundlage/"

  knn_path: "/Users/dwillers/Programmierung/Master/Evaluation_OOS-Erkennung/assets/Bilder/AktuelleTrainingsUndTestdaten/"

  knn_path: "/Users/dwillers/Programmierung/Master/Evaluation_OOS-Erkennung/assets/Bilder/AktuelleTrainingsUndTestdaten/"

knn:

knn:

  epochs: 2

  epochs: 6

result:

result:

  plot_path: "/Users/dwillers/Programmierung/Master/Evaluation_OOS-Erkennung/ressources/results/plot/"

  plot_path: "/Users/dwillers/Programmierung/Master/Evaluation_OOS-Erkennung/ressources/results/plot/"

  excel_path: "/Users/dwillers/Programmierung/Master/Evaluation_OOS-Erkennung/ressources/results/excel/"
 
  excel_path: "/Users/dwillers/Programmierung/Master/Evaluation_OOS-Erkennung/ressources/results/excel/"

src/data/copyData.py

+6 −5
Original line number Original line Diff line number Diff line
@@ -45,20 +45,21 @@ def copy_images_with_exclusion(src_dir, dest_dir, exclude_dir=None): 
            os.remove(file_or_dir_path)

            os.remove(file_or_dir_path)





    # Erstelle die gewünschte Ordnerstruktur im Zielverzeichnis

    # Erstelle die gewünschte Ordnerstruktur im Zielverzeichnis

    os.makedirs(os.path.join(dest_dir, 'OOS'))

    os.makedirs(os.path.join(dest_dir, '0_OOS'))

    os.makedirs(os.path.join(dest_dir, '!OOS'))

    os.makedirs(os.path.join(dest_dir, '1_!OOS'))







    # Durchlaufe die Unterordner im Quellverzeichnis

    # Durchlaufe die Unterordner im Quellverzeichnis

    for subdir in os.listdir(src_dir):

    for subdir in os.listdir(src_dir):

        #  if subdir == exclude_dir:

        #  if subdir == exclude_dir:

        subdirString = bytes(subdir, 'utf-8').decode('unicode_escape')

        subdirString = bytes(subdir, 'utf-8').decode('unicode_escape')

        exclude_dirString = bytes(exclude_dir, 'utf-8').decode('unicode_escape')

        exclude_dirString = bytes(exclude_dir.name, 'utf-8').decode('unicode_escape')

        if subdirString == exclude_dirString:

        if subdirString == exclude_dirString:

            continue

            continue

        for folder in os.listdir(os.path.join(src_dir, subdir)):

        for folder in os.listdir(os.path.join(src_dir, subdir)):

            if folder == 'OOS':

            if folder == 'OOS':

                for img in os.listdir(os.path.join(src_dir, subdir, folder)):

                for img in os.listdir(os.path.join(src_dir, subdir, folder)):

                    shutil.copy(os.path.join(src_dir, subdir, folder, img), os.path.join(dest_dir, 'OOS'))

                    shutil.copy(os.path.join(src_dir, subdir, folder, img), os.path.join(dest_dir, '0_OOS'))

            elif folder == '!OOS':

            elif folder == '!OOS':

                for img in os.listdir(os.path.join(src_dir, subdir, folder)):

                for img in os.listdir(os.path.join(src_dir, subdir, folder)):

                    shutil.copy(os.path.join(src_dir, subdir, folder, img), os.path.join(dest_dir, '!OOS'))
 
                    shutil.copy(os.path.join(src_dir, subdir, folder, img), os.path.join(dest_dir, '1_!OOS'))

src/data/modelData.py

+2 −2
Original line number Original line Diff line number Diff line
@@ -19,7 +19,7 @@ def define_augmentation_rules(): 
def get_train_data(datagen, directory):

def get_train_data(datagen, directory):

    train_it = datagen.flow_from_directory(

    train_it = datagen.flow_from_directory(

        directory=directory,

        directory=directory,

        class_mode='binary',

        class_mode='categorical',

        batch_size=64,

        batch_size=64,

        target_size=(224, 224),

        target_size=(224, 224),

        subset='training')

        subset='training')
@@ -29,7 +29,7 @@ def get_train_data(datagen, directory): 
def get_test_data(datagen, directory):

def get_test_data(datagen, directory):

    test_it = datagen.flow_from_directory(

    test_it = datagen.flow_from_directory(

        directory=directory,

        directory=directory,

        class_mode='binary',

        class_mode='categorical',

        batch_size=64,

        batch_size=64,

        target_size=(224, 224),

        target_size=(224, 224),

        subset='validation')

        subset='validation')