In the previous subsection, we have seen how to work with the applications module of the Keras deep learning library, providing both deep learning model definitions and pre-trained weights for a number of popular architectures.
In this subsection, we are going to see how to create a deep learning REST API based on one of these pre-trained architectures.
The Keras deep learning REST API is a single file named keras_server.py. The code for this script can be seen next:
# Import required packages:from keras.applications import nasnet, NASNetMobilefrom keras.preprocessing.image import img_to_arrayfrom keras.applications import imagenet_utilsfrom PIL import Imageimport numpy as npimport flaskimport ...
