{"action":"closed","number":2,"pull_request":{"url":"https://api.github.com/repos/KyleBanks/tensorflow-docker-retrain/pulls/2","id":211701683,"node_id":"MDExOlB1bGxSZXF1ZXN0MjExNzAxNjgz","html_url":"https://github.com/KyleBanks/tensorflow-docker-retrain/pull/2","diff_url":"https://github.com/KyleBanks/tensorflow-docker-retrain/pull/2.diff","patch_url":"https://github.com/KyleBanks/tensorflow-docker-retrain/pull/2.patch","issue_url":"https://api.github.com/repos/KyleBanks/tensorflow-docker-retrain/issues/2","number":2,"state":"closed","locked":false,"title":"added paragraph about directory structure","user":{"login":"gucharbon","id":36370689,"node_id":"MDQ6VXNlcjM2MzcwNjg5","avatar_url":"https://avatars3.githubusercontent.com/u/36370689?v=4","gravatar_id":"","url":"https://api.github.com/users/gucharbon","html_url":"https://github.com/gucharbon","followers_url":"https://api.github.com/users/gucharbon/followers","following_url":"https://api.github.com/users/gucharbon/following{/other_user}","gists_url":"https://api.github.com/users/gucharbon/gists{/gist_id}","starred_url":"https://api.github.com/users/gucharbon/starred{/owner}{/repo}","subscriptions_url":"https://api.github.com/users/gucharbon/subscriptions","organizations_url":"https://api.github.com/users/gucharbon/orgs","repos_url":"https://api.github.com/users/gucharbon/repos","events_url":"https://api.github.com/users/gucharbon/events{/privacy}","received_events_url":"https://api.github.com/users/gucharbon/received_events","type":"User","site_admin":false},"body":"The commented documentation in [retrain.py](https://github.com/tensorflow/hub/blob/master/examples/image_retraining/retrain.py) talks about how labels are given to tensorflow when doing transfert Learning:\r\n\r\n> The top layer receives as input a 2048-dimensional vector (assuming\r\nInception V3) for each image. We train a softmax layer on top of this\r\nrepresentation. If the softmax layer contains N labels, this corresponds\r\nto learning N + 2048*N model parameters for the biases and weights.\r\n\r\n\r\n\r\n>Here's an example, which assumes you have a folder containing class-named\r\nsubfolders, each full of images for each label. The example folder flower_photos\r\nshould have a structure like this:\r\n\r\n\r\n    ~/flower_photos/daisy/photo1.jpg\r\n    ~/flower_photos/daisy/photo2.jpg\r\n    ...\r\n    ~/flower_photos/rose/anotherphoto77.jpg\r\n    ...\r\n    ~/flower_photos/sunflower/somepicture.jpg\r\n\r\n> The subfolder names are important, since they define what label is applied to\r\neach image, but the filenames themselves don't matter. (For a working example,\r\ndownload http://download.tensorflow.org/example_images/flower_photos.tgz\r\nand run  tar xzf flower_photos.tgz  to unpack it.)\r\n\r\n\r\n> Once your images are prepared, and you have pip-installed tensorflow-hub and\r\na sufficiently recent version of tensorflow, you can run the training with a\r\ncommand like this:\r\n\r\n```bash\r\npython retrain.py --image_dir ~/flower_photos\r\n```\r\n\r\nI understand that everything is explained in your [blog](https://kylewbanks.com/blog/tutorial-transfer-learning-retraining-inception-mobilenet-with-tensorflow-and-docker) but a little paragraph mentioning it in the README would be 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