{"action":"opened","issue":{"url":"https://api.github.com/repos/kuprel/skin/issues/1","repository_url":"https://api.github.com/repos/kuprel/skin","labels_url":"https://api.github.com/repos/kuprel/skin/issues/1/labels{/name}","comments_url":"https://api.github.com/repos/kuprel/skin/issues/1/comments","events_url":"https://api.github.com/repos/kuprel/skin/issues/1/events","html_url":"https://github.com/kuprel/skin/issues/1","id":137039738,"number":1,"title":"Weird issues with batches larger than 1","user":{"login":"bernardopires","id":1726375,"avatar_url":"https://avatars.githubusercontent.com/u/1726375?v=3","gravatar_id":"","url":"https://api.github.com/users/bernardopires","html_url":"https://github.com/bernardopires","followers_url":"https://api.github.com/users/bernardopires/followers","following_url":"https://api.github.com/users/bernardopires/following{/other_user}","gists_url":"https://api.github.com/users/bernardopires/gists{/gist_id}","starred_url":"https://api.github.com/users/bernardopires/starred{/owner}{/repo}","subscriptions_url":"https://api.github.com/users/bernardopires/subscriptions","organizations_url":"https://api.github.com/users/bernardopires/orgs","repos_url":"https://api.github.com/users/bernardopires/repos","events_url":"https://api.github.com/users/bernardopires/events{/privacy}","received_events_url":"https://api.github.com/users/bernardopires/received_events","type":"User","site_admin":false},"labels":[],"state":"open","locked":false,"assignee":null,"milestone":null,"comments":0,"created_at":"2016-02-28T11:15:42Z","updated_at":"2016-02-28T11:15:42Z","closed_at":null,"body":"Hi Brett,\r\n\r\nI came across your repo when I saw your post at tensorflow google group about retraining inception and was really excited that I'd be able to finetune the whole network.\r\n\r\nI have this weird issue with batches larger than 1 which I can not pinpoint exactly where it is happening. I basically reused your code for loading inception v3 and just use a different loss function. I trained a model and it seems like everything went fine. But when evaluating the images, i.e. not training, so simply doing a forward pass, every time I evaluate I get different results, by a relatively large amount (up to 20%) even though it's always exactly the same images. When using `batch_size=1` the results are consistently the same (minus tiny differences due to the batchnorm layer). What I find really weird is that if I use for example a batch of 16 images, and all 16 images are the same, then I consistently get the same results out, just as if I had a batch_size of 1. So it seems like somehow the images inside a batch are affecting the others.\r\n\r\nDid you notice anything like this? Can you please check if you can reproduce this? Thanks a lot!\r\n\r\nCheers,\r\nBernardo"}}