{"action":"opened","issue":{"url":"https://api.github.com/repos/andrewgordonwilson/bayesgan/issues/4","repository_url":"https://api.github.com/repos/andrewgordonwilson/bayesgan","labels_url":"https://api.github.com/repos/andrewgordonwilson/bayesgan/issues/4/labels{/name}","comments_url":"https://api.github.com/repos/andrewgordonwilson/bayesgan/issues/4/comments","events_url":"https://api.github.com/repos/andrewgordonwilson/bayesgan/issues/4/events","html_url":"https://github.com/andrewgordonwilson/bayesgan/issues/4","id":280878081,"number":4,"title":"Add WGANGP to comparison","user":{"login":"htt210","id":16357422,"avatar_url":"https://avatars1.githubusercontent.com/u/16357422?v=4","gravatar_id":"","url":"https://api.github.com/users/htt210","html_url":"https://github.com/htt210","followers_url":"https://api.github.com/users/htt210/followers","following_url":"https://api.github.com/users/htt210/following{/other_user}","gists_url":"https://api.github.com/users/htt210/gists{/gist_id}","starred_url":"https://api.github.com/users/htt210/starred{/owner}{/repo}","subscriptions_url":"https://api.github.com/users/htt210/subscriptions","organizations_url":"https://api.github.com/users/htt210/orgs","repos_url":"https://api.github.com/users/htt210/repos","events_url":"https://api.github.com/users/htt210/events{/privacy}","received_events_url":"https://api.github.com/users/htt210/received_events","type":"User","site_admin":false},"labels":[],"state":"open","locked":false,"assignee":null,"assignees":[],"milestone":null,"comments":0,"created_at":"2017-12-11T02:46:13Z","updated_at":"2017-12-11T02:46:13Z","closed_at":null,"author_association":"NONE","body":"Hi Andrew,\r\nI've just done some experiments with WGAN with Gradient Penalty (Improved Training of Wasserstein GANs, Gulrajani et al.) and found that it can converge to a reasonable solution on the synthetic dataset. Although WGANGP does not converge as fast as bayesgan, I think it would be nice if you could add WGANGP to the baselines in your experiments.\r\nHere is the output of my (very bad) implementation of WGANGP after 8000 iterations\r\n```\r\nnx = 100\r\nnz = 10\r\nbatchSize = 64\r\nGconfig = [('Linear', (nz, 1000)), ('ReLU', ()), ('Linear', (1000, nx))]\r\nDconfig = [('Linear', (nx, 1000)), ('ReLU', ()), ('Linear', (1000, 1))]\r\noptimizer = 'Adam'\r\noptimParams = {'lr': 1e-4, 'betas': (0.5, 0.9)}\r\n```\r\n\r\n![iter_8000](https://user-images.githubusercontent.com/16357422/33813687-92ec4f54-de79-11e7-8029-c265b24133e8.png)\r\n\r\n"}}
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