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finetuning.py

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+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()

visualize.py

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+import tensorflow as tf
+import numpy as np
+import matplotlib.pyplot as plt
+from tensorflow.keras.preprocessing.image import ImageDataGenerator
+from tensorflow.keras.applications import ResNet50
+from tensorflow.keras.models import Model
+from tensorflow.keras.layers import Dense, GlobalAveragePooling2D
+from tensorflow.keras.optimizers import Adam
+
+# Laden der Validierungsdaten
+validation_datagen = ImageDataGenerator(rescale=1./255)
+validation_generator = validation_datagen.flow_from_directory(
+    r'C:\Coding\BlockImageClassification\validation',
+    target_size=(224, 224),
+    batch_size=8,
+    class_mode='categorical',
+    shuffle=True  # Mischt die Daten vor jeder Epoche
+)
+
+# Laden des Modells
+base_model = ResNet50(weights='imagenet', include_top=False, input_shape=(224, 224, 3))
+x = base_model.output
+x = GlobalAveragePooling2D()(x)
+x = Dense(1024, activation='relu')(x)
+predictions = Dense(3, activation='softmax')(x)
+model = Model(inputs=base_model.input, outputs=predictions)
+
+# Vorhersagen auf den Validierungsdaten
+predictions_in_percentage = model.predict(validation_generator)
+predictions = np.argmax(predictions_in_percentage, axis=-1)
+
+# Darstellen der Vorhersagen
+class_names = ['Bulbasaur', 'Charmander', 'Squirtle']  # Aktualisierte Klassennamen
+for i in range(len(predictions)):
+    image, label = validation_generator[i]
+    plt.imshow(image[0])
+    plt.title('pred. ' + class_names[predictions[i]] + ' war ' + class_names[np.argmax(label)] + ' ' + str(np.round(predictions_in_percentage[i], 2)), fontsize=8)
+    plt.axis("off")
+    plt.show()
+