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Hands-On Image Processing with Python

You're reading from  Hands-On Image Processing with Python

Product type Book
Published in Nov 2018
Publisher Packt
ISBN-13 9781789343731
Pages 492 pages
Edition 1st Edition
Languages
Author (1):
Sandipan Dey Sandipan Dey
Profile icon Sandipan Dey

Table of Contents (20) Chapters

Title Page
Copyright and Credits
Dedication
About Packt
Contributors
Preface
Getting Started with Image Processing Sampling, Fourier Transform, and Convolution Convolution and Frequency Domain Filtering Image Enhancement Image Enhancement Using Derivatives Morphological Image Processing Extracting Image Features and Descriptors Image Segmentation Classical Machine Learning Methods in Image Processing Deep Learning in Image Processing - Image Classification Deep Learning in Image Processing - Object Detection, and more Additional Problems in Image Processing Other Books You May Enjoy Index

Image classification with TensorFlow or Keras


In this section, we shall revisit the problem of handwritten digits classification (with the MNIST dataset), but this time with deep neural networks. We are going to solve the problem using two very popular deep learning libraries, namely TensorFlow and Keras. TensorFlow (TF) is the most famous library used in production for deep learning models. It has a very large and awesome community. However, TensorFlow is not that easy to use. On the other hand, Keras is a high level API built on TensorFlow. It is more user-friendly and easy to use compared to TF, although it provides less control over low-level structures. Low-level libraries provide more flexibility. Hence TF can be tweaked much more as compared to Keras.

Classification with TF

First, we shall start with a very simple deep neural network, one containing only a single FC hidden layer (with ReLU activation) and a softmax FC layer, with no convolutional layer. The next screenshot shows the...

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