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:
Then you can launch jupyter notebook by executing
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.