{"action":"opened","issue":{"url":"https://api.github.com/repos/jakeret/tf_unet/issues/2","repository_url":"https://api.github.com/repos/jakeret/tf_unet","labels_url":"https://api.github.com/repos/jakeret/tf_unet/issues/2/labels{/name}","comments_url":"https://api.github.com/repos/jakeret/tf_unet/issues/2/comments","events_url":"https://api.github.com/repos/jakeret/tf_unet/issues/2/events","html_url":"https://github.com/jakeret/tf_unet/issues/2","id":185546734,"number":2,"title":"regarding the size of input masking image and definition of \"in_size\" and \"size\" for offset","user":{"login":"surfreta","id":23064201,"avatar_url":"https://avatars.githubusercontent.com/u/23064201?v=3","gravatar_id":"","url":"https://api.github.com/users/surfreta","html_url":"https://github.com/surfreta","followers_url":"https://api.github.com/users/surfreta/followers","following_url":"https://api.github.com/users/surfreta/following{/other_user}","gists_url":"https://api.github.com/users/surfreta/gists{/gist_id}","starred_url":"https://api.github.com/users/surfreta/starred{/owner}{/repo}","subscriptions_url":"https://api.github.com/users/surfreta/subscriptions","organizations_url":"https://api.github.com/users/surfreta/orgs","repos_url":"https://api.github.com/users/surfreta/repos","events_url":"https://api.github.com/users/surfreta/events{/privacy}","received_events_url":"https://api.github.com/users/surfreta/received_events","type":"User","site_admin":false},"labels":[],"state":"open","locked":false,"assignee":null,"assignees":[],"milestone":null,"comments":0,"created_at":"2016-10-27T00:43:57Z","updated_at":"2016-10-27T00:43:57Z","closed_at":null,"body":"Hello Jakeret,\r\n\r\nThank you very much for sharing your code, which is very well written.\r\n\r\nI have several questions, would you mind sharing your thoughts on them?\r\n\r\n1)\tIn your implementation,  the input mask training data set has to be of share `row*column*2.` For my use case, the input masking training data set is of shape `row*column*1`. Do I have to transform my input masking training data set into the form of `row*column*2`. Are there any reason that you would like to specify the mask data set that way?\r\n2)\t In Create_conv_net, you defined `in_size=1000, and size=in_size`. Value of size is changed during convolution, pooling, deconv and unpooling operations. Then create_conv_net will return in_size-size as offset, which will be used to compute px and py. This is copied from the program returns prediction: `The unet prediction Shape [n, px, py, labels] (px=nx-self.offset/2)`\r\nI don’t understand why in_size is setup as 1000, and why we need this offset. Looks like un-pooling and deconvolution can resize the output map to the original image. Especially, the conv2d should allow us to specify the shape of output map.\r\n3)\tIn the training process, you use `test_x, test_y = data_provider(4)`\r\n           ` pred_shape = self.store_prediction(sess, test_x, test_y, \"_init\")`\r\nWhat’s the reason to generate a batch of 4 at the very begining. Are there any considerations here?\r\n4)\tYou include regularizer in the training process. Are there any rules to select regularizers? The original paper does not mention this. If this is based on some paper or technical report, would you mind let me know?\r\n    ```\r\nif add_regularizers:\r\n            reg_constant = 0.001  # Choose an appropriate one.\r\n            regularizers = sum([tf.nn.l2_loss(variable) for variable in self.variables])\r\n            self.cost += (reg_constant * regularizers)\r\n```\r\nThank you very much for your help.\r\n"}}
{"actor":{"display_login":"surfreta"}}