{"action":"created","issue":{"url":"https://api.github.com/repos/Lasagne/Recipes/issues/43","labels_url":"https://api.github.com/repos/Lasagne/Recipes/issues/43/labels{/name}","comments_url":"https://api.github.com/repos/Lasagne/Recipes/issues/43/comments","events_url":"https://api.github.com/repos/Lasagne/Recipes/issues/43/events","html_url":"https://github.com/Lasagne/Recipes/pull/43","id":129463467,"number":43,"title":"adding missing dropout layers to VGG16 and VG19","user":{"login":"webeng","id":508225,"avatar_url":"https://avatars.githubusercontent.com/u/508225?v=3","gravatar_id":"","url":"https://api.github.com/users/webeng","html_url":"https://github.com/webeng","followers_url":"https://api.github.com/users/webeng/followers","following_url":"https://api.github.com/users/webeng/following{/other_user}","gists_url":"https://api.github.com/users/webeng/gists{/gist_id}","starred_url":"https://api.github.com/users/webeng/starred{/owner}{/repo}","subscriptions_url":"https://api.github.com/users/webeng/subscriptions","organizations_url":"https://api.github.com/users/webeng/orgs","repos_url":"https://api.github.com/users/webeng/repos","events_url":"https://api.github.com/users/webeng/events{/privacy}","received_events_url":"https://api.github.com/users/webeng/received_events","type":"User","site_admin":false},"labels":[],"state":"open","locked":false,"assignee":null,"milestone":null,"comments":9,"created_at":"2016-01-28T14:13:17Z","updated_at":"2016-01-30T15:04:56Z","closed_at":null,"pull_request":{"url":"https://api.github.com/repos/Lasagne/Recipes/pulls/43","html_url":"https://github.com/Lasagne/Recipes/pull/43","diff_url":"https://github.com/Lasagne/Recipes/pull/43.diff","patch_url":"https://github.com/Lasagne/Recipes/pull/43.patch"},"body":"According to the paper [\"Very Deep Convolutional Networks for Large-Scale Image Recognition\"](http://arxiv.org/abs/1409.1556) there are two dropout layers (ratio 0.5) for the first two fully-connected layers in VGG16 and VGG19.\r\n\r\nI added the two dropout layers and it drastically reduces overfitting.\r\n"},"comment":{"url":"https://api.github.com/repos/Lasagne/Recipes/issues/comments/177201328","html_url":"https://github.com/Lasagne/Recipes/pull/43#issuecomment-177201328","issue_url":"https://api.github.com/repos/Lasagne/Recipes/issues/43","id":177201328,"user":{"login":"webeng","id":508225,"avatar_url":"https://avatars.githubusercontent.com/u/508225?v=3","gravatar_id":"","url":"https://api.github.com/users/webeng","html_url":"https://github.com/webeng","followers_url":"https://api.github.com/users/webeng/followers","following_url":"https://api.github.com/users/webeng/following{/other_user}","gists_url":"https://api.github.com/users/webeng/gists{/gist_id}","starred_url":"https://api.github.com/users/webeng/starred{/owner}{/repo}","subscriptions_url":"https://api.github.com/users/webeng/subscriptions","organizations_url":"https://api.github.com/users/webeng/orgs","repos_url":"https://api.github.com/users/webeng/repos","events_url":"https://api.github.com/users/webeng/events{/privacy}","received_events_url":"https://api.github.com/users/webeng/received_events","type":"User","site_admin":false},"created_at":"2016-01-30T15:04:56Z","updated_at":"2016-01-30T15:04:56Z","body":"Yes, [ImageNet Pretrained Network (VGG_S).ipynb](https://gist.github.com/ksimonyan/211839e770f7b538e2d8#file-vgg_ilsvrc_16_layers_deploy-prototxt) also has the dropout layers after the dense layers. \r\n\r\nI think it's more inline with Lasagne to use *deterministic=true* when you call *get_output* as it will already ignore the dropout Layers, don't you think? This is how it's done in the previous example.\r\n\r\n"}}