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Chapter 14: Classifying Images with Deep Convolutional Neural Networks

Chapter Outline

  • The building blocks of CNNs
    • Understanding CNNs and feature hierarchies
    • Performing discrete convolutions
      • Discrete convolutions in one dimension
      • Padding inputs to control the size of the output feature maps
      • Determining the size of the convolution output
      • Performing a discrete convolution in 2D
    • Subsampling layers
    • Putting everything together – implementing a CNN
      • Working with multiple input or color channels
      • Regularizing an NN with dropout
    • Loss functions for classification
  • Implementing a deep CNN using PyTorch
    • The multilayer CNN architecture
    • Loading and preprocessing the data
    • Smile classification from face images using a CNN
      • Loading the CelebA dataset
      • Image transformation and data augmentation
      • Training a CNN smile classifier
  • Summary

A note on using the code examples

The recommended way to interact with the code examples in this book is via Jupyter Notebook (the .ipynb files). Using Jupyter Notebook, you will be able to execute the code step by step and have all the resulting outputs (including plots and images) all in one convenient document.

Setting up Jupyter Notebook is really easy: if you are using the Anaconda Python distribution, all you need to install jupyter notebook is to execute the following command in your terminal:

conda install jupyter notebook

Then you can launch jupyter notebook by executing

jupyter notebook

A window will open up in your browser, which you can then use to navigate to the target directory that contains the .ipynb file you wish to open.

More installation and setup instructions can be found in the README.md file of Chapter 1.

(Even if you decide not to install Jupyter Notebook, note that you can also view the notebook files on GitHub by simply clicking on them: ch14_part1.ipynb and ch14_part2.ipynb)

In addition to the code examples, I added a table of contents to each Jupyter notebook as well as section headers that are consistent with the content of the book. Also, I included the original images and figures in hope that these make it easier to navigate and work with the code interactively as you are reading the book.

When I was creating these notebooks, I was hoping to make your reading (and coding) experience as convenient as possible! However, if you don't wish to use Jupyter Notebooks, I also converted these notebooks to regular Python script files (.py files) that can be viewed and edited in any plaintext editor.