xai_img9.py

# load imagenet id to class label mappings
import requests

response = requests.get('https://storage.googleapis.com/download.tensorflow.org/data/imagenet_class_index.json')
imgnet_map = response.json()
imgnet_map = {v[1]: k for k, v in imgnet_map.items()}

# make model predictions
img = tf.keras.applications.xception.preprocess_input(img)
predictions = model.predict(np.array([img]))
decode_predictions(predictions, top=5)