Training Generative Adversarial Networks (GANs) in PyTorch
Part 7 of "Deep Learning with Pytorch: Zero to GANs"
This tutorial series is a hands-on beginner-friendly introduction to deep learning using PyTorch, an open-source neural networks library. These tutorials take a practical and coding-focused approach. The best way to learn the material is to execute the code and experiment with it yourself. Check out the full series here:
- PyTorch Basics: Tensors & Gradients
- Gradient Descent & Linear Regression
- Working with Images & Logistic Regression
- Training Deep Neural Networks on a GPU
- Image Classification using Convolutional Neural Networks
- Data Augmentation, Regularization and ResNets
- Generating Images using Generative Adversarial Networks
How to run the code
This tutorial is an executable Jupyter notebook hosted on Jovian. You can run this tutorial and experiment with the code examples in a couple of ways: using free online resources (recommended) or on your computer.
Option 1: Running using free online resources (1-click, recommended)
The easiest way to start executing the code is to click the Run button at the top of this page and select Run on Colab. Google Colab is a free online platform for running Jupyter notebooks using Google's cloud infrastructure. You can also select "Run on Binder" or "Run on Kaggle" if you face issues running the notebook on Google Colab.
Option 2: Running on your computer locally
To run the code on your computer locally, you'll need to set up Python, download the notebook and install the required libraries. We recommend using the Conda distribution of Python. Click the Run button at the top of this page, select the Run Locally option, and follow the instructions.
Using a GPU for faster training
You can use a Graphics Processing Unit (GPU) to train your models faster if your execution platform is connected to a GPU manufactured by NVIDIA. Follow these instructions to use a GPU on the platform of your choice:
- Google Colab: Use the menu option "Runtime > Change Runtime Type" and select "GPU" from the "Hardware Accelerator" dropdown.
- Kaggle: In the "Settings" section of the sidebar, select "GPU" from the "Accelerator" dropdown. Use the button on the top-right to open the sidebar.
- Binder: Notebooks running on Binder cannot use a GPU, as the machines powering Binder aren't connected to any GPUs.
- Linux: If your laptop/desktop has an NVIDIA GPU (graphics card), make sure you have installed the NVIDIA CUDA drivers.
- Windows: If your laptop/desktop has an NVIDIA GPU (graphics card), make sure you have installed the NVIDIA CUDA drivers.
- macOS: macOS is not compatible with NVIDIA GPUs
If you do not have access to a GPU or aren't sure what it is, don't worry, you can execute all the code in this tutorial just fine without a GPU.
Introduction to Generative Modeling
Deep neural networks are used mainly for supervised learning: classification or regression. Generative Adversarial Networks or GANs, however, use neural networks for a very different purpose: Generative modeling
Generative modeling is an unsupervised learning task in machine learning that involves automatically discovering and learning the regularities or patterns in input data in such a way that the model can be used to generate or output new examples that plausibly could have been drawn from the original dataset. - Source
To get a sense of the power of generative models, just visit thispersondoesnotexist.com. Every time you reload the page, a new image of a person's face is generated on the fly. The results are pretty fascinating:
While there are many approaches used for generative modeling, a Generative Adversarial Network takes the following approach:
There are two neural networks: a Generator and a Discriminator. The generator generates a "fake" sample given a random vector/matrix, and the discriminator attempts to detect whether a given sample is "real" (picked from the training data) or "fake" (generated by the generator). Training happens in tandem: we train the discriminator for a few epochs, then train the generator for a few epochs, and repeat. This way both the generator and the discriminator get better at doing their jobs.
GANs however, can be notoriously difficult to train, and are extremely sensitive to hyperparameters, activation functions and regularization. In this tutorial, we'll train a GAN to generate images of anime characters' faces.
We'll use the Anime Face Dataset, which consists of over 63,000 cropped anime faces. Note that generative modeling is an unsupervised learning task, so the images do not have any labels. Most of the code in this tutorial is based on this notebook.
project_name = '06b-anime-dcgan'