Implement Generative Adversarial Networks (GANs)

[someshsingh22]

@Adversarial-Deep-Learning/core-dev

https://papers.nips.cc/paper/2014/file/5ca3e9b122f61f8f06494c97b1afccf3-Paper.pdf

• Dataset Class
• Unit Test Cases
• Utils
• Core components (Generator / Discriminator)
• Training
• Linting Failures
• Remove Dummy Files
• Make Algorithm Modular
• Add documentation, Docstrings, References #21
• Shift functional code outside coverage
• Convert Unit Test Case to Argument IO Testing #24
• Multiple Channel Data Input Generator (current support for MNIST only) #22

[someshsingh22]

@blackrosedragon2 Final Linting report shows some errors, you can check them here https://github.com/Adversarial-Deep-Learning/code-soup/runs/3268983541 make the changes in a new PR and we will close this

Also init.py file is missing from chapter, you can take a look at the latest PR #9 to see how tests and modules can be built or called

Once built you can remove the dummy files

[someshsingh22]

@blackrosedragon2 check the coverage report we have to get maximum coverage, all functions that need time like training will be ignored only structural and functional requirements should be checked.

[someshsingh22]

The remaining issues are left open to avoid clutter in this Issue