from tensorflow.keras.preprocessing.image import load_img
from tensorflow.keras.preprocessing.image import img_to_array
from tensorflow.keras.applications.vgg16 import preprocess_input
from tensorflow.keras.applications.vgg16 import decode_predictions
from tensorflow.keras.applications.vgg16 import VGG16

model = VGG16()  # model = VGG16(weights=”imagenet”)
image = load_img('mug.jpg', target_size=(224, 224))
image = img_to_array(image)

# reshape data for the model -> (batchsize, height, width, channels)
image = image.reshape((1, image.shape[0], image.shape[1], image.shape[2]))

# prepare the image for the VGG model
image = preprocess_input(image)

# predict the probability across all output classes
yhat = model.predict(image)

# convert the probabilities to class labels, top 5 default
label = decode_predictions(yhat)

# retrieve the most likely result, e.g. highest probability
label = label[0][0]

# print the classification
print('%s (%.2f%%)' % (label[1], label[2]*100))