Upload app.py

app.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()
+