In this article, we will be looking at conditional and controllable GANs, what’s their need, and how to implement Naive conditional GAN using TensorFlow 2.x.
In this article, we will be looking at conditional and controllable GANs, what’s their need, and how to implement Naive conditional GAN using TensorFlow 2.x. Before you read further, I would like you to be familiar with DCGANs, which you can find here.
Till now, the generator was generating images randomly, and we had no control over the class of image to be generated i.e while training GAN, the generator was generating a random digit each time i.e it may generate one, six, or three, we don't know what it will generate. But will conditional GANs we can tell the generator to generate an image of one or six. This is where conditional GAN becomes handy. With conditional GAN you can generate images of the class of your choice.
Till now, we were feeding images as an only input to our generator and discriminator. But now we will be feeding class information to both the networks.
The code for this article is almost the same as that of DCGAN, but with some modifications. Let us see those differences.
Note: Following Implementation is a Naive way and is very slow. You can refer _[**_here**](https://machinelearningmastery.com/how-to-develop-a-conditional-generative-adversarial-network-from-scratch/)_for a much better way of coding conditional GANs._
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