Vanilla GAN in TensorFlow | Generative Adversarial Networks | Anime Faces Generation
Hello, friends. Welcome to this video. In this video, we will implement the Vanilla GAN using TensorFlow. For this tutorial, we will be using the Anime Face dataset.
Generative Adversarial Networks, or GANs, were introduced in 2014 by Ian Goodfellow and his team as an unsupervised machine learning approach for generative modelling.
GANs are made of two neural networks: a generator and a discriminator. The generator network learns to generate new examples, while the discriminator network tries to classify the examples as real or fake. These two networks compete with each other (thus adversarial) to generate new examples (fake) that look like real ones.
Datasets: https://www.kaggle.com/soumikrakshit/...
Blog: https://idiotdeveloper.com/vanilla-ga...
Code: https://github.com/nikhilroxtomar/GAN...
Timeline:
00:00 - Introduction
00:14 - What is GAN?
00:55 - Anime Faces dataset
01:28 - Importing Libraries and Functions
01:46 - Image Dimensions
01:54 - Defining Some Helper Functions.
04:22 - Generator Neural Network
06:38 - Discriminator Neural Network
07:37 - Training Function for GAN
12:45 - Function to Save Generated Images
13:11 - Training the GAN
19:56 - Testing the GAN (Generating Fake Images)
22:05 - Conclusion
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