{"action":"created","issue":{"url":"https://api.github.com/repos/akanimax/pro_gan_pytorch/issues/7","repository_url":"https://api.github.com/repos/akanimax/pro_gan_pytorch","labels_url":"https://api.github.com/repos/akanimax/pro_gan_pytorch/issues/7/labels{/name}","comments_url":"https://api.github.com/repos/akanimax/pro_gan_pytorch/issues/7/comments","events_url":"https://api.github.com/repos/akanimax/pro_gan_pytorch/issues/7/events","html_url":"https://github.com/akanimax/pro_gan_pytorch/issues/7","id":381738629,"node_id":"MDU6SXNzdWUzODE3Mzg2Mjk=","number":7,"title":"Why defining bias term yourself?","user":{"login":"SaoYan","id":25550286,"node_id":"MDQ6VXNlcjI1NTUwMjg2","avatar_url":"https://avatars2.githubusercontent.com/u/25550286?v=4","gravatar_id":"","url":"https://api.github.com/users/SaoYan","html_url":"https://github.com/SaoYan","followers_url":"https://api.github.com/users/SaoYan/followers","following_url":"https://api.github.com/users/SaoYan/following{/other_user}","gists_url":"https://api.github.com/users/SaoYan/gists{/gist_id}","starred_url":"https://api.github.com/users/SaoYan/starred{/owner}{/repo}","subscriptions_url":"https://api.github.com/users/SaoYan/subscriptions","organizations_url":"https://api.github.com/users/SaoYan/orgs","repos_url":"https://api.github.com/users/SaoYan/repos","events_url":"https://api.github.com/users/SaoYan/events{/privacy}","received_events_url":"https://api.github.com/users/SaoYan/received_events","type":"User","site_admin":false},"labels":[],"state":"open","locked":false,"assignee":null,"assignees":[],"milestone":null,"comments":5,"created_at":"2018-11-16T19:55:28Z","updated_at":"2018-11-19T06:02:49Z","closed_at":null,"author_association":"NONE","body":"Hi there, in CustomLayers.py, the bias term is defined by hand:\r\n> self.bias = th.nn.Parameter(th.FloatTensor(c_out).fill_(0))\r\n\r\nWhy not just use bias=True/False in nn.Conv2d and nn.Linear?"},"comment":{"url":"https://api.github.com/repos/akanimax/pro_gan_pytorch/issues/comments/439780860","html_url":"https://github.com/akanimax/pro_gan_pytorch/issues/7#issuecomment-439780860","issue_url":"https://api.github.com/repos/akanimax/pro_gan_pytorch/issues/7","id":439780860,"node_id":"MDEyOklzc3VlQ29tbWVudDQzOTc4MDg2MA==","user":{"login":"akanimax","id":16590868,"node_id":"MDQ6VXNlcjE2NTkwODY4","avatar_url":"https://avatars0.githubusercontent.com/u/16590868?v=4","gravatar_id":"","url":"https://api.github.com/users/akanimax","html_url":"https://github.com/akanimax","followers_url":"https://api.github.com/users/akanimax/followers","following_url":"https://api.github.com/users/akanimax/following{/other_user}","gists_url":"https://api.github.com/users/akanimax/gists{/gist_id}","starred_url":"https://api.github.com/users/akanimax/starred{/owner}{/repo}","subscriptions_url":"https://api.github.com/users/akanimax/subscriptions","organizations_url":"https://api.github.com/users/akanimax/orgs","repos_url":"https://api.github.com/users/akanimax/repos","events_url":"https://api.github.com/users/akanimax/events{/privacy}","received_events_url":"https://api.github.com/users/akanimax/received_events","type":"User","site_admin":false},"created_at":"2018-11-19T06:02:49Z","updated_at":"2018-11-19T06:02:49Z","author_association":"OWNER","body":"I have actually gone through most of the Optimizer code as well. It doesn't have any additional code for the equalized learning rate. Indeed, the graphs are statically built, but notice how the call is made to `tf.get_variable()` inside the `get_weight()` function which is in turn called by the `conv2d()` function. The function `tf.get_variable()` initializes a new weight if the variable doesn't exist, else it returns the already present variable with the same name. basically, in the graph, everytime the conv operation is applied, they are multiplying the weights of the convolution by the `wscale` constant parameter.\r\n\r\nLet me know if you find anything more."}}
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