Book Image

Deep Learning for Computer Vision

By : Rajalingappaa Shanmugamani
Book Image

Deep Learning for Computer Vision

By: Rajalingappaa Shanmugamani

Overview of this book

Deep learning has shown its power in several application areas of Artificial Intelligence, especially in Computer Vision. Computer Vision is the science of understanding and manipulating images, and finds enormous applications in the areas of robotics, automation, and so on. This book will also show you, with practical examples, how to develop Computer Vision applications by leveraging the power of deep learning. In this book, you will learn different techniques related to object classification, object detection, image segmentation, captioning, image generation, face analysis, and more. You will also explore their applications using popular Python libraries such as TensorFlow and Keras. This book will help you master state-of-the-art, deep learning algorithms and their implementation.
Table of Contents (17 chapters)
Title Page
Copyright and Credits
Packt Upsell
Foreword
Contributors
Preface

Training the MNIST model in Keras


In this section, we will use the same model as defined in the previous section using tf.keras APIs. It is better to learn both Keras and layers packages from TensorFlow as they could be seen at several open source codes. The objective of the book is to make you understand various offerings of TensorFlow so that you can build products on top of it. 

"Code is read more often than it is written."

Bearing in mind the preceding quote, you are shown how to implement the same model using various APIs. Open source code of any implementation of the latest algorithms will be a mix of these APIs. Next, we will start with the Keras implementation. 

Preparing the dataset

The MNIST data is available with Keras. First, import tensorflow. Then define a few constants such as batch size, the classes, and the number of epochs. The batch size can be selected based on the RAM available on your machine. The higher the batch size, the more RAM required. The impact of the batch size...