Complete Guide to Build ConvNet HTTP-Based Application using TensorFlow and Flask RESTful Python API

This tutorial takes you along the steps required to create a convolutional neural network (CNN/ConvNet) using TensorFlow and get it into production by allowing remote access via a HTTP-based application using Flask RESTful API. In this tutorial, a CNN is to be built using TensorFlow NN (tf.nn) module. The CNN model architecture is created and trained and tested against the CIFAR10 dataset. To make the model remotely accessible, a Flask Web application is created using Python to receive an uploaded image and return its classification label using HTTP. Anaconda3 is used in addition to TensorFlow on Windows with CPU support.