ch17

Python Machine Learning - Code Examples

Chapter 17: Generative Adversarial Networks for Synthesizing New Data

Chapter Outline

• Introducing generative adversarial networks
  • Starting with autoencoders
  • Generative models for synthesizing new data
  • Generating new samples with GANs
  • Understanding the loss functions for the generator and discriminator networks in a GAN model
• Implementing a GAN from scratch
  • Training GAN models on Google Colab
  • Implementing the generator and the discriminator networks
  • Defining the training dataset
  • Training the GAN model
• Improving the quality of synthesized images using a convolutional and Wasserstein GAN
  • Transposed convolution
  • Batch normalization
  • Implementing the generator and discriminator
  • Dissimilarity measures between two distributions
  • Using EM distance in practice for GANs
  • Gradient penalty
  • Implementing WGAN-GP to train the DCGAN model
  • Mode collapse
  • Other GAN applications
• 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: ch17_part1.ipynb and ch17_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.