{"action":"created","issue":{"url":"https://api.github.com/repos/DrSleep/tensorflow-deeplab-resnet/issues/12","repository_url":"https://api.github.com/repos/DrSleep/tensorflow-deeplab-resnet","labels_url":"https://api.github.com/repos/DrSleep/tensorflow-deeplab-resnet/issues/12/labels{/name}","comments_url":"https://api.github.com/repos/DrSleep/tensorflow-deeplab-resnet/issues/12/comments","events_url":"https://api.github.com/repos/DrSleep/tensorflow-deeplab-resnet/issues/12/events","html_url":"https://github.com/DrSleep/tensorflow-deeplab-resnet/issues/12","id":201680653,"number":12,"title":"Finetuning on new dataset / Modify input images on the fly","user":{"login":"mgarbade","id":3956593,"avatar_url":"https://avatars.githubusercontent.com/u/3956593?v=3","gravatar_id":"","url":"https://api.github.com/users/mgarbade","html_url":"https://github.com/mgarbade","followers_url":"https://api.github.com/users/mgarbade/followers","following_url":"https://api.github.com/users/mgarbade/following{/other_user}","gists_url":"https://api.github.com/users/mgarbade/gists{/gist_id}","starred_url":"https://api.github.com/users/mgarbade/starred{/owner}{/repo}","subscriptions_url":"https://api.github.com/users/mgarbade/subscriptions","organizations_url":"https://api.github.com/users/mgarbade/orgs","repos_url":"https://api.github.com/users/mgarbade/repos","events_url":"https://api.github.com/users/mgarbade/events{/privacy}","received_events_url":"https://api.github.com/users/mgarbade/received_events","type":"User","site_admin":false},"labels":[],"state":"open","locked":false,"assignee":null,"assignees":[],"milestone":null,"comments":13,"created_at":"2017-01-18T20:15:11Z","updated_at":"2017-02-08T16:44:34Z","closed_at":null,"body":"How can I modify images on the fly? Say I would like to set a certain area of the input images region to 0? Where in your code would I need to do the surgery for that?  \r\nRather in the `ImageReader` function where the image is loaded?  \r\nOr rather in the network graph itself, say by adding a layer after the data layer in `DeepLabResNetModel` that multiplies elementwise with some mask?  \r\n\r\nSorry for bothering you with this stupid question. I'm new to tensorflow. Also sorry for asking usage-question, but since your code differs quite a lot from the tensorflow tutorial code I don't really know where else to turn for that question...Thanks a lot for providing the deeplab-resnet model for tensorflow!"},"comment":{"url":"https://api.github.com/repos/DrSleep/tensorflow-deeplab-resnet/issues/comments/278384282","html_url":"https://github.com/DrSleep/tensorflow-deeplab-resnet/issues/12#issuecomment-278384282","issue_url":"https://api.github.com/repos/DrSleep/tensorflow-deeplab-resnet/issues/12","id":278384282,"user":{"login":"mgarbade","id":3956593,"avatar_url":"https://avatars.githubusercontent.com/u/3956593?v=3","gravatar_id":"","url":"https://api.github.com/users/mgarbade","html_url":"https://github.com/mgarbade","followers_url":"https://api.github.com/users/mgarbade/followers","following_url":"https://api.github.com/users/mgarbade/following{/other_user}","gists_url":"https://api.github.com/users/mgarbade/gists{/gist_id}","starred_url":"https://api.github.com/users/mgarbade/starred{/owner}{/repo}","subscriptions_url":"https://api.github.com/users/mgarbade/subscriptions","organizations_url":"https://api.github.com/users/mgarbade/orgs","repos_url":"https://api.github.com/users/mgarbade/repos","events_url":"https://api.github.com/users/mgarbade/events{/privacy}","received_events_url":"https://api.github.com/users/mgarbade/received_events","type":"User","site_admin":false},"created_at":"2017-02-08T16:44:34Z","updated_at":"2017-02-08T16:44:34Z","body":"I just saw that the model pretrained on ms-coco was just the exact same that you provided as init model.\r\nI further tried to monitor the development of the variables of the neural network by adding tf.summary.histogram loggers to all trainable variables in the network like this (based on your train.txt in the `train-orig`  branch:\r\n\r\n```\r\n    for v in conv_trainable + fc_w_trainable + fc_b_trainable: # Add histogram to all variables\r\n        tf.summary.histogram(v.name.replace(\":\",\"_\"),v)\r\n    merged_summary_op = tf.summary.merge_all() \r\n```\r\n\r\n\r\nIt looks like only the last layer is learning something, the earlier layers seem to not change at all: \r\n\r\n\r\n![screenshot from 2017-02-08 17 29 22](https://cloud.githubusercontent.com/assets/3956593/22746888/ac743440-ee25-11e6-998f-9a181d890eb9.png)\r\n\r\n`conv1/weights_0` is the first convolution layer. The other layers look the same.\r\n\r\n`fc1_voc12_c0/biases_0` and `fc1_voc12_c0/weights_0`  are the convolution weights from the last layers. Here, at least the bias is changing. Weights are again almost unchanged.\r\n\r\nThis pattern stays the same for more iterations... I will play around with the learning rate, but it seems like the optimization is not working correctly...\r\n "}}
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