{"action":"created","issue":{"url":"https://api.github.com/repos/y0ast/VAE-Torch/issues/3","labels_url":"https://api.github.com/repos/y0ast/VAE-Torch/issues/3/labels{/name}","comments_url":"https://api.github.com/repos/y0ast/VAE-Torch/issues/3/comments","events_url":"https://api.github.com/repos/y0ast/VAE-Torch/issues/3/events","html_url":"https://github.com/y0ast/VAE-Torch/issues/3","id":63328751,"number":3,"title":"Use of exp in the Reparametrization","user":{"login":"zencoding","id":8623645,"avatar_url":"https://avatars.githubusercontent.com/u/8623645?v=3","gravatar_id":"","url":"https://api.github.com/users/zencoding","html_url":"https://github.com/zencoding","followers_url":"https://api.github.com/users/zencoding/followers","following_url":"https://api.github.com/users/zencoding/following{/other_user}","gists_url":"https://api.github.com/users/zencoding/gists{/gist_id}","starred_url":"https://api.github.com/users/zencoding/starred{/owner}{/repo}","subscriptions_url":"https://api.github.com/users/zencoding/subscriptions","organizations_url":"https://api.github.com/users/zencoding/orgs","repos_url":"https://api.github.com/users/zencoding/repos","events_url":"https://api.github.com/users/zencoding/events{/privacy}","received_events_url":"https://api.github.com/users/zencoding/received_events","type":"User","site_admin":false},"labels":[],"state":"closed","locked":false,"assignee":null,"milestone":null,"comments":15,"created_at":"2015-03-20T23:16:52Z","updated_at":"2015-03-25T21:58:11Z","closed_at":"2015-03-22T05:35:31Z","body":"Hi,\r\n     I am trying to follow  DKingma/MWelling's paper at http://arxiv.org/pdf/1312.6114v10.pdf and your torch code, I am following everything in the code except that I am getting lost at use of exp in the Reparmeterization updateOutput and UpdateGradInput functions.  I did not find any mention of using exponential in the paper or other implementations of VAE (in Theano/PyLearn2). Also is the fill of 0.5 just a arbitrary parameter value or is there any specific reason for it.\r\n\r\nif torch.typename(input[1]) == 'torch.CudaTensor' then\r\n        self.eps = self.eps:cuda()\r\n        self.output = torch.CudaTensor():resizeAs(input[2]):fill(0.5)\r\nelse\r\n        self.output = torch.Tensor():resizeAs(input[2]):fill(0.5)\r\nend\r\n\r\nBTW, I have reviewed 5 different implementations of Variational AutoEncoder,your torch implementation is the most concise and clear.\r\n\r\nThank You"},"comment":{"url":"https://api.github.com/repos/y0ast/VAE-Torch/issues/comments/86231245","html_url":"https://github.com/y0ast/VAE-Torch/issues/3#issuecomment-86231245","issue_url":"https://api.github.com/repos/y0ast/VAE-Torch/issues/3","id":86231245,"user":{"login":"y0ast","id":485778,"avatar_url":"https://avatars.githubusercontent.com/u/485778?v=3","gravatar_id":"","url":"https://api.github.com/users/y0ast","html_url":"https://github.com/y0ast","followers_url":"https://api.github.com/users/y0ast/followers","following_url":"https://api.github.com/users/y0ast/following{/other_user}","gists_url":"https://api.github.com/users/y0ast/gists{/gist_id}","starred_url":"https://api.github.com/users/y0ast/starred{/owner}{/repo}","subscriptions_url":"https://api.github.com/users/y0ast/subscriptions","organizations_url":"https://api.github.com/users/y0ast/orgs","repos_url":"https://api.github.com/users/y0ast/repos","events_url":"https://api.github.com/users/y0ast/events{/privacy}","received_events_url":"https://api.github.com/users/y0ast/received_events","type":"User","site_admin":false},"created_at":"2015-03-25T21:58:11Z","updated_at":"2015-03-25T21:58:11Z","body":"That's what the sigmaInit variable is for in LinearVA, however that's treating the symptoms not the actual cause :).\r\n\r\nI'll have another deeper look this weekend, thanks for spending some time to look at it zencoding and Ajay."}}