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

model = ResNet50()
model.summary()
#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)

#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', 0.82658
