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Hands-On Image Processing with Python

You're reading from   Hands-On Image Processing with Python Expert techniques for advanced image analysis and effective interpretation of image data

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Product type Paperback
Published in Nov 2018
Publisher Packt
ISBN-13 9781789343731
Length 492 pages
Edition 1st Edition
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Author (1):
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Sandipan Dey Sandipan Dey
Author Profile Icon Sandipan Dey
Sandipan Dey
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Table of Contents (20) Chapters Close

Title Page
Copyright and Credits
Dedication
About Packt
Contributors
Preface
1. Getting Started with Image Processing 2. Sampling, Fourier Transform, and Convolution FREE CHAPTER 3. Convolution and Frequency Domain Filtering 4. Image Enhancement 5. Image Enhancement Using Derivatives 6. Morphological Image Processing 7. Extracting Image Features and Descriptors 8. Image Segmentation 9. Classical Machine Learning Methods in Image Processing 10. Deep Learning in Image Processing - Image Classification 11. Deep Learning in Image Processing - Object Detection, and more 12. Additional Problems in Image Processing 1. 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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