Commit af35f578 authored by Dennis Willers's avatar Dennis Willers
Browse files

Excel Dokumentationsausgabe implementiert

parent 2f264f7c

.gitignore

+2 −0
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@@ -55,3 +55,5 @@ assets/Bilder/* 
assets/Bilder/AktuelleTrainingsUndTestdaten/*

assets/Bilder/Datengrundlage/*

assets/Bilder/Datengrundlage/

/ressources/results/

/ressources/results/*

main.py

+5 −4
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@@ -22,7 +22,7 @@ def run_model(): 
        config = yaml.load(file, Loader=yaml.FullLoader)

    # Initialisieren der KNN-Modellbau Eigenschaften

    config_knn = ConfigKNN(

        excluded_folder=Markt.Kein_Markt,

        excluded_folder=Markt.Markt_C,

        activation_function_1_units=Aktivierungsfunktion.sigmoid,

        activation_function_128_units=Aktivierungsfunktion.ReLU,

        optimization_method=Optimierungsverfahren.SGD
@@ -56,16 +56,17 @@ def run_model(): 
    history = fit_model(config, model, train_it, test_it, callback)

    # evaluate model

    print("Evaluiere das Modell")

    evaluate_model(model, test_it)

    evaluate_metrics = evaluate_model(model, test_it, datagen, config, config_knn)

    # print duration

    print("Stoppe die Zeit")

    end_time = datetime.now()

    print('Die Dauer für das Erstellen des Modells beträgt: {}'.format(end_time - start_time))

    training_duration_model = format(end_time - start_time)

    print('Die Dauer für das Erstellen des Modells beträgt: ' + training_duration_model)

    # learning curves

    print("Erstelle den Plot Graphen")

    plot_values(history, config)

    print("Erstelle die Excel-Tabelle")

    create_excel_result(callback, config)

    create_excel_result(callback, config, config_knn, evaluate_metrics, training_duration_model)





# entry point, run the test harness

ressources/config/config.yaml

+6 −6
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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/"

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

  original_path: "assets/Bilder/Datengrundlage-Reduziert-Test/"

  # original_path: "assets/Bilder/Datengrundlage/"

  knn_path: "assets/Bilder/AktuelleTrainingsUndTestdaten/"

knn:

  epochs: 6

  epochs: 2

result:

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

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

  excel_path: "ressources/results/excel/"

src/data/copyData.py

+2 −3
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@@ -2,15 +2,14 @@ import os 
import shutil

import random





def create_reduzierte_testdaten():

    # Pfad des Quellordners

    src_dir = '/assets/Bilder/Datengrundlage'



    # Pfad des Zielordners

    dst_dir = '/assets/Bilder/Datengrundlage-Reduziert-Test'

dst_dir_trainingsdaten = '/assets/Bilder/AktuelleTrainingsUndTestdaten'





def create_reduzierte_testdaten():

    # Prozentualer Anteil der Bilder, die kopiert werden sollen

    sample_rate = 0.05

src/data/modelData.py

+9 −0
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@@ -34,3 +34,12 @@ def get_test_data(datagen, directory): 
        target_size=(224, 224),

        subset='validation')

    return test_it





def get_markt_data(datagen, directory):

    test_it = datagen.flow_from_directory(

        directory=directory,

        class_mode='categorical',

        batch_size=64,

        target_size=(224, 224))

    return test_it