ResNet Neural Network

resnet.py

from tensorflow.keras.applications.resnet50 import ResNet50
from tensorflow.keras.preprocessing import image
from tensorflow.keras.applications.resnet50 import preprocess_input, decode_predictions
import numpy as np

# image
img_path = 'elephant.jpg'
img = image.load_img(img_path, target_size=(224, 224))
x = image.img_to_array(img)
x = np.expand_dims(x, axis=0)
x = preprocess_input(x)

# ResNet with pre-trained weights
model = ResNet50(weights='imagenet')
preds = model.predict(x)

# decode the results into a list of tuples (class, description, probability)
# (one such list for each sample in the batch)
print('Predicted:', decode_predictions(preds, top=3)[0])

# Predicted: [(u'n02504013', u'Indian_elephant', 0.82658225), 
# (u'n01871265', u'tusker', 0.1122357), 
# (u'n02504458', u'African_elephant', 0.061040461)]