Mastering Conditional DCGAN in TensorFlow | Deep Convolution Generative Adversarial Network Tutorial
📺 Video Description:
In this video, we'll delve into the fascinating world of Generative Adversarial Networks (GANs) and specifically explore the implementation of Conditional DCGAN (Deep Convolutional Generative Adversarial Network).
🌈 Key Points:
Conditional DCGAN is an extension of the traditional DCGAN architecture, where the generator not only learns to create realistic images but does so under specific conditions. These conditions could be anything from class labels to additional input data that guides the generation process. In simpler terms, it allows us to control and influence the generated output based on specific criteria.
🔧 Code: https://github.com/nikhilroxtomar/GAN...
🕒 Timeline:
00:00 - Introduction
00:12 - What are Conditional DCGANs?
00:38 - Male vs Female Dataset
01:38 - Importing Libraries and Functions
01:58 - Image Dimensions
02:25 - Defining Some Helper Functions.
05:18 - Convolutional Block
06:52 - Deconvolutional Block
08:40 - Generator Neural Network
12:40 - Discriminator Neural Network
15:36 - Training Function for GAN
20:02 - Function to Save Generated Images
20:56 - Training the GAN
31:21 - Testing the GAN (Generating Fake Images)
35:35 - Conclusion
🔗 Related Content:
What are GANs: • What are GANs | Generative Adversari...
Vanilla GAN in TensorFlow: • Vanilla GAN in TensorFlow | Generativ...
Conditional GAN in TensorFlow: • Conditional GAN in TensorFlow | Gener...
DCGAN in TensorFlow: • Deep Convolutional Generative Adversa...
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