The Keras Applications module has pre-trained neural network models, along with its pre-trained weights trained on ImageNet. These models can be used directly for prediction, feature extraction, and fine-tuning:

#import VGG16 network model and other necessary libraries 

from keras.applications.vgg16 import VGG16
from keras.preprocessing import image
from keras.applications.vgg16 import preprocess_input
import numpy as np

#Instantiate VGG16 and returns a vgg16 model instance 
vgg16_model = VGG16(weights='imagenet', include_top=False) 
#include_top: whether to include the 3 fully-connected layers at the top of the network.
#This has to be True for classification and False for feature extraction. Returns a model instance
#weights:'imagenet' means model is pre-training on ImageNet data.
model = VGG16(weights='imagenet', include_top=True)
model.summary()

#image file name to classify
image_path = 'jumping_dolphin.jpg'
#load the input image with keras helper utilities and resize the image. 
#Default input size for this model is 224x224 pixels.
img = image.load_img(image_path, target_size=(224, 224))
#convert PIL (Python Image Library??) image to numpy array
x = image.img_to_array(img)
print (x.shape)

#image is now represented by a NumPy array of shape (224, 224, 3),
# but we need to expand the dimensions to be (1, 224, 224, 3) so we can
# pass it through the network -- we'll also preprocess the image by
# subtracting the mean RGB pixel intensity from the ImageNet dataset
#Finally, we can load our Keras network and classify the image:

x = np.expand_dims(x, axis=0)
print (x.shape)

preprocessed_image = preprocess_input(x)

preds = model.predict(preprocessed_image)
print('Prediction:', decode_predictions(preds, top=2)[0])

The first time it executes the preceding script, Keras will automatically download and cache the architecture weights to disk in the ~/.keras/models directory. Subsequent runs will be faster.