Generative Adversarial Networks (GAN) using keras in python || GAN implementation in python
Generative adversarial networks (GANs) are deep learning architectures that use two neural networks (Generator and Discriminator), competing one against the other. The generator tries to create realistic looking fake data (e.g. images) and the discriminator tries to classify whether the data is real or fake. After a few thousand (or million) epochs, the generator trained model can be used to create new fake data that can pass for real data.
This tutorial the implementation of GAN using Keras in Python. It uses fully connected dense layers for both the generator and discriminator.
It also explains the use of trained model in generating realistic looking fake handwritten digits.
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Code is uploaded in to the Google Drive Link and Github link
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