{"action":"created","issue":{"url":"https://api.github.com/repos/albermax/innvestigate/issues/118","repository_url":"https://api.github.com/repos/albermax/innvestigate","labels_url":"https://api.github.com/repos/albermax/innvestigate/issues/118/labels{/name}","comments_url":"https://api.github.com/repos/albermax/innvestigate/issues/118/comments","events_url":"https://api.github.com/repos/albermax/innvestigate/issues/118/events","html_url":"https://github.com/albermax/innvestigate/issues/118","id":376355268,"node_id":"MDU6SXNzdWUzNzYzNTUyNjg=","number":118,"title":"LRP for semantic segmentation","user":{"login":"gchlebus","id":447383,"node_id":"MDQ6VXNlcjQ0NzM4Mw==","avatar_url":"https://avatars0.githubusercontent.com/u/447383?v=4","gravatar_id":"","url":"https://api.github.com/users/gchlebus","html_url":"https://github.com/gchlebus","followers_url":"https://api.github.com/users/gchlebus/followers","following_url":"https://api.github.com/users/gchlebus/following{/other_user}","gists_url":"https://api.github.com/users/gchlebus/gists{/gist_id}","starred_url":"https://api.github.com/users/gchlebus/starred{/owner}{/repo}","subscriptions_url":"https://api.github.com/users/gchlebus/subscriptions","organizations_url":"https://api.github.com/users/gchlebus/orgs","repos_url":"https://api.github.com/users/gchlebus/repos","events_url":"https://api.github.com/users/gchlebus/events{/privacy}","received_events_url":"https://api.github.com/users/gchlebus/received_events","type":"User","site_admin":false},"labels":[],"state":"open","locked":false,"assignee":null,"assignees":[],"milestone":null,"comments":14,"created_at":"2018-11-01T11:30:36Z","updated_at":"2018-12-13T12:54:46Z","closed_at":null,"author_association":"NONE","body":"Hi,\r\n\r\nThank you very much for this nice library. I think it is a great initiative to have a collection of neural network analysis tools! \r\n\r\nI tried to use iNNvestigate to get some insights into semantic segmentation neural networks. I created a toy-task to segment the mnist images using a small U-net architecture. The desired target images were created by thresholding the input images at 0.5. I encountered the following problems when running different variants of LRP analysis:\r\n- Running the same analysis for the same output neuron for the same input multiple times results in different relevance maps (e.g., the relevance at the same position can be sometimes negative and sometime positive).\r\n- The relevance map doesn't sum up to the output value of the analysed neuron.\r\n\r\nPlease find attached a jupyter notebook (as a `txt` file, since github doesn't support the `ipynb` extension) and a generated pdf with my code, that I used for this toy-task. I would appreciate your feedback and any hints on how to use LRP to analyse semantic segmentation models.\r\n\r\nBest, Grzegorz\r\n[mnist_unet.pdf](https://github.com/albermax/innvestigate/files/2538287/mnist_unet.pdf)\r\n[mnist_unet.txt](https://github.com/albermax/innvestigate/files/2538293/mnist_unet.txt)\r\n"},"comment":{"url":"https://api.github.com/repos/albermax/innvestigate/issues/comments/446958354","html_url":"https://github.com/albermax/innvestigate/issues/118#issuecomment-446958354","issue_url":"https://api.github.com/repos/albermax/innvestigate/issues/118","id":446958354,"node_id":"MDEyOklzc3VlQ29tbWVudDQ0Njk1ODM1NA==","user":{"login":"gchlebus","id":447383,"node_id":"MDQ6VXNlcjQ0NzM4Mw==","avatar_url":"https://avatars0.githubusercontent.com/u/447383?v=4","gravatar_id":"","url":"https://api.github.com/users/gchlebus","html_url":"https://github.com/gchlebus","followers_url":"https://api.github.com/users/gchlebus/followers","following_url":"https://api.github.com/users/gchlebus/following{/other_user}","gists_url":"https://api.github.com/users/gchlebus/gists{/gist_id}","starred_url":"https://api.github.com/users/gchlebus/starred{/owner}{/repo}","subscriptions_url":"https://api.github.com/users/gchlebus/subscriptions","organizations_url":"https://api.github.com/users/gchlebus/orgs","repos_url":"https://api.github.com/users/gchlebus/repos","events_url":"https://api.github.com/users/gchlebus/events{/privacy}","received_events_url":"https://api.github.com/users/gchlebus/received_events","type":"User","site_admin":false},"created_at":"2018-12-13T12:54:46Z","updated_at":"2018-12-13T12:54:46Z","author_association":"NONE","body":"Hi Max,\r\n\r\nThank you very much. Your fix makes the analysis reproducible. I am getting exactly the same results for the same output neuron when running the analysis multiple times. However, the sum of the relevance map still deviates from the value of the analysed output neuron (I confirmed this behaviour with a model with and without batch norm). The deviation can be sometimes quite large (e.g., neuron outputs 1.7 and the relevance map for this neuron sums up to 40). \r\n\r\nMaybe there is something wrong with the way I call the analyser? Which relevance rule would you recommend to use for semantic segmentation ConvNets (in the notebook I use the `EpsilonRule`)? The `input_layer_rule` parameter passed to the analyzer defines the range of input values the model receives, is it correct?\r\n\r\nBest, Grzegorz"}}
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