Deep Convolutional Generative Adversarial Network (DCGAN) Implementation in TensorFlow
📺 Video Description:
Dive deep into the world of Generative Adversarial Networks (GANs) as we guide you through the step-by-step implementation of Deep Convolutional Generative Adversarial Networks (DCGAN) using TensorFlow.
🌈 Key Points:
DCGAN is an extension of the traditional GAN architecture, designed to enhance the generation of high-quality images. DCGAN incorporates convolutional layers in both the generator and discriminator networks, providing several key advantages. These convolutional layers enable DCGANs to effectively capture spatial hierarchies and patterns in images, making them well-suited for tasks like image generation and manipulation.
🔧 Code: https://github.com/nikhilroxtomar/GAN...
🕒 Timeline:
00:00 - Introduction
00:13 - What is Deep Convolutional Generative Adversarial Network (DCGAN)?
00:40 - Anime Faces dataset
01:17 - Importing Libraries and Functions
01:39 - Image Dimensions
01:54 - Defining Some Helper Functions.
04:15 - Convolutional Block
05:32 - Deconvolutional Block
07:16 - Generator Neural Network
09:09 - Discriminator Neural Network
10:10 - Training Function for GAN
15:21 - Function to Save Generated Images
15:54 - Training the GAN
21:54 - Testing the GAN (Generating Fake Images)
25:01 - Conclusion
📚 Datasets:
https://www.kaggle.com/soumikrakshit/...
🔗 Related Content:
Vanilla GAN in TensorFlow: • Vanilla GAN in TensorFlow | Generativ...
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