Book Image

Hands-On Deep Learning for Images with TensorFlow

By : Will Ballard
Book Image

Hands-On Deep Learning for Images with TensorFlow

By: Will Ballard

Overview of this book

TensorFlow is Google’s popular offering for machine learning and deep learning, quickly becoming a favorite tool for performing fast, efficient, and accurate deep learning tasks. Hands-On Deep Learning for Images with TensorFlow shows you the practical implementations of real-world projects, teaching you how to leverage TensorFlow’s capabilities to perform efficient image processing using the power of deep learning. With the help of this book, you will get to grips with the different paradigms of performing deep learning such as deep neural nets and convolutional neural networks, followed by understanding how they can be implemented using TensorFlow. By the end of this book, you will have mastered all the concepts of deep learning and their implementation with TensorFlow and Keras.
Table of Contents (7 chapters)

REST API definition

Let's begin by defining the REST API. This is comprised of four activities: getting the project source code from GitHub with git; installing the necessary packages and reviewing the packages that will be needed in order to run our server; editing and creating the OpenAPI or Swagger definition file in YAML; and then finally handling a POST-ed image in that code that the REST API takes to turn an actual image file into a tensor.

First, we need to clone the repository that we've provided in order to have a REST service. I'm getting this over HTTPS and cloning it with the command line:

$ git clone https://github.com/wballard/kerasvideo-server/tree/2018.git

You can put it in any directory you like. Afterwards, we'll be able to use this source code for the rest of this section, and in the remaining chapters of this book:

Files in Kerasvideo...