Delete visualize.py

visualize.py

@@ -1,40 +0,0 @@
-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()
-