Delete finetuning.py

finetuning.py

@@ -1,39 +0,0 @@
-import matplotlib.pyplot as plt
-
-
-# Trainiere das Modell
-history = model.fit(
-    train_generator,
-    steps_per_epoch=train_generator.samples // train_generator.batch_size,
-    epochs=10,
-    validation_data=validation_generator,
-    validation_steps=validation_generator.samples // validation_generator.batch_size
-)
-
-# Speichern von Genauigkeit und Verlust während des Trainings
-acc = history.history['accuracy']
-val_acc = history.history['val_accuracy']
-loss = history.history['loss']
-val_loss = history.history['val_loss']
-
-# Plot der Trainingshistorie
-plt.figure(figsize=(8, 8))
-
-# Subplot für Genauigkeit
-plt.subplot(2, 1, 1)
-plt.plot(acc, label='Training Accuracy')
-plt.plot(val_acc, label='Validation Accuracy')
-plt.ylim([0.4, 1])  # Setze die y-Achsen-Grenzen
-plt.plot([initial_epochs - 1, initial_epochs - 1], plt.ylim(), label='Start Fine Tuning')
-plt.legend(loc='lower right')
-plt.title('Training and Validation Accuracy')
-
-# Subplot für Verlust
-plt.subplot(2, 1, 2)
-plt.plot(loss, label='Training Loss')
-plt.plot(val_loss, label='Validation Loss')
-plt.plot([initial_epochs - 1, initial_epochs - 1], plt.ylim(), label='Start Fine Tuning')
-plt.legend(loc='upper right')
-plt.title('Training and Validation Loss')
-plt.xlabel('Epoch')
-plt.show()