This Vanilla GAN Pytorch tutorial will show you how to implement the Vanilla GAN architecture from scratch using Pytorch.
For more information check out our video:
Introduction to Vanilla GANs
A vanilla GAN is a simple generative adversarial network (GAN) that is used to generate new data from scratch. The GAN consists of two neural networks, a generator and a discriminator, that are pitted against each other in a zero-sum game. The generator tries to generate data that is realistic enough to fool the discriminator, while the discriminator tries to distinguish between real and generated data.
The vanilla GAN was introduced in 2014 by Ian Goodfellow et al. in their paper “Generative Adversarial Nets”. Since then, GANs have become very popular and have been used for a variety of tasks such as image generation, image translation, and text generation.
In this tutorial, we will implement a vanilla GAN to generate images of handwritten digits from the MNIST dataset. We will be using the PyTorch deep learning framework throughout this tutorial.
What is a GAN?
A GAN is a machine learning algorithm that is used to generate new data that looks similar to training data. It is made up of two neural networks, one generator and one discriminator. The generator network takes in noise as input and outputs a generated image. The discriminator network takes in an image, either generated or real, and outputs a probability that the image is real. The objective of the GAN is to have the generator network generate images that look so realistic that the discriminator network can not tell if it is a real image or not.
The Generator Network
In this tutorial, we’ll be building a generative adversarial network (GAN) to generate new images of handwritten digits. Our generator network will take as input a vector of noise, and generate an image of a handwritten digit that looks realistic enough to fool our discriminator network.
We’ll be using the PyTorch library for this tutorial. If you’re not familiar with PyTorch, it’s a powerful tool for building and training neural networks.
The Discriminator Network
The Discriminator network is made up of a series of convolutional layers, batch normalization layers, and Leaky ReLU activations. The network starts with a sequence of convolutional layers, where the number of filters increases from 32 to 128. We then apply batch normalization and Leaky ReLU activation functions after each convolutional layer.
Training the Vanilla GAN
This Vanilla GAN Pytorch tutorial will show you how to train a generative adversarial network to generate new images of handwritten digits. The examples in this tutorial use the MNIST dataset, which consists of black and white images of handwritten digits.
To train the Vanilla GAN, you will need to first define the generator and discriminator models. The generator model will take as input a noise vector, and generate an image from that noise vector. The discriminator model will take as input an image, and output a probability that the image is real (i.e. not generated by the generator).
Once you have defined the generator and discriminator models, you will need to train them using a training loop. In each training iteration, you will first have the generator generate a new image from a noise vector. You will then pass that generated image into the discriminator, which will output a probability that the image is real. Finally, you will compare the output of the discriminator to the actual labels of whether or not the images are real, and update the weights of both the generator and discriminator models accordingly.
Once you have trained your Vanilla GAN, you will be able to generate new images of handwritten digits by passing noise vectors into your generator model!
Evaluating the Vanilla GAN
In this post, we’ll evaluate the Vanilla GAN that we trained in the previous post. We’ll compute the Inception score and Frechet inception distance, as well as visualize some samples from the generator.
The Inception score was proposed in Improved Techniques for Training GANs, which measures the performance of a GAN by evaluating the generated images with an image classifier. The intuition behind this metric is that if the generated images are of high quality, then the classifier should be able to correctly classify them with high confidence.
mathematically, it is defined as where is thesoftmax outputs of the Inception network for generated images and for real images, and is the number of classes.
Frechet Inception Distance
The Frechet inception distance (FID) was proposed inGANs Trained by a Two Time-Scale Update Rule Converge to a Local Nash Equilibrium, and is another method for evaluating GANs. It measures how similar the feature distributions of real images and generated images are. If two sets of features have similar distributions (i.e., they came from similar classes of data), then we would expect their Frechet distances to be small.
mathematically, it is defined aswhere and are the mean and covariance matrix of features from real images, while and are those computed from generated images
Tips and Tricks for Training Vanilla GANs
Here are some important tips and tricks for training Vanilla GANs effectively:
1. Use a small batch size. This helps to stabilize training and prevents mode collapse.
2. Use a noise vector of low dimensionality. This helps the GAN to learn the latent space better and prevents mode collapse.
3. Train for a large number of iterations. This is necessary to fully converge the GAN training process.
4. Monitor the loss function carefully. This will help you to detect mode collapse and other issues during training.
Further Reading and Resources
If you want to learn more about vanilla GANs or Pytorch, check out the following resources:
-Vanilla GAN Paper: https://arxiv.org/pdf/1703.05192.pdf
-Pytorch Tutorials: https://pytorch.org/tutorials/beginner/dcgan_faces_tutorial.html
In this tutorial, we have seen how to write a simple Vanilla GAN in PyTorch, and apply it to generate realistic looking handwritten digits. We have also seen how to update the discriminator and generator networks using the Adam optimizer.
About the Authors
This Vanilla Gan Pytorch Tutorial is brought to you by the team at Analytics Vidhya. We are a group of data science and machine learning enthusiasts who believe in sharing knowledge and contributing to the open source community.
Keyword: Vanilla Gan Pytorch Tutorial