{"action":"created","issue":{"url":"https://api.github.com/repos/fchollet/keras/issues/2242","repository_url":"https://api.github.com/repos/fchollet/keras","labels_url":"https://api.github.com/repos/fchollet/keras/issues/2242/labels{/name}","comments_url":"https://api.github.com/repos/fchollet/keras/issues/2242/comments","events_url":"https://api.github.com/repos/fchollet/keras/issues/2242/events","html_url":"https://github.com/fchollet/keras/issues/2242","id":147138562,"number":2242,"title":"How can i implement inception style network with keras","user":{"login":"erogol","id":1402048,"avatar_url":"https://avatars.githubusercontent.com/u/1402048?v=3","gravatar_id":"","url":"https://api.github.com/users/erogol","html_url":"https://github.com/erogol","followers_url":"https://api.github.com/users/erogol/followers","following_url":"https://api.github.com/users/erogol/following{/other_user}","gists_url":"https://api.github.com/users/erogol/gists{/gist_id}","starred_url":"https://api.github.com/users/erogol/starred{/owner}{/repo}","subscriptions_url":"https://api.github.com/users/erogol/subscriptions","organizations_url":"https://api.github.com/users/erogol/orgs","repos_url":"https://api.github.com/users/erogol/repos","events_url":"https://api.github.com/users/erogol/events{/privacy}","received_events_url":"https://api.github.com/users/erogol/received_events","type":"User","site_admin":false},"labels":[],"state":"open","locked":false,"assignee":null,"milestone":null,"comments":1,"created_at":"2016-04-09T15:32:52Z","updated_at":"2016-05-21T11:32:21Z","closed_at":null,"body":"As inception style networks uses I like to branch a layer's output and merge at the end of the next layer. I implemented that by the following snippet but I got DisconnectedInputError\r\n\r\n```\r\n    # model is defined previously as the last layers output\r\n    # conv 3x3\r\n    left = Sequential()\r\n    left = ConvFactory(left, kernel=(3, 3), stride=(2, 2), num_filter=ch_3x3,\r\n                      input_shape = model.output_shape[1:])\r\n    # pool\r\n    right = Sequential()\r\n    right.add(layers.MaxPooling2D(pool_size=(3, 3), strides=(2, 2),\r\n             input_shape = model.output_shape[1:]))\r\n    \r\n    model.add(layers.Merge([left, right], mode='concat',concat_axis=1))\r\n````"},"comment":{"url":"https://api.github.com/repos/fchollet/keras/issues/comments/220772866","html_url":"https://github.com/fchollet/keras/issues/2242#issuecomment-220772866","issue_url":"https://api.github.com/repos/fchollet/keras/issues/2242","id":220772866,"user":{"login":"huster-wgm","id":12251249,"avatar_url":"https://avatars.githubusercontent.com/u/12251249?v=3","gravatar_id":"","url":"https://api.github.com/users/huster-wgm","html_url":"https://github.com/huster-wgm","followers_url":"https://api.github.com/users/huster-wgm/followers","following_url":"https://api.github.com/users/huster-wgm/following{/other_user}","gists_url":"https://api.github.com/users/huster-wgm/gists{/gist_id}","starred_url":"https://api.github.com/users/huster-wgm/starred{/owner}{/repo}","subscriptions_url":"https://api.github.com/users/huster-wgm/subscriptions","organizations_url":"https://api.github.com/users/huster-wgm/orgs","repos_url":"https://api.github.com/users/huster-wgm/repos","events_url":"https://api.github.com/users/huster-wgm/events{/privacy}","received_events_url":"https://api.github.com/users/huster-wgm/received_events","type":"User","site_admin":false},"created_at":"2016-05-21T11:32:21Z","updated_at":"2016-05-21T11:32:21Z","body":"`from keras.layers import merge, Convolution2D, MaxPooling2D, Input\r\n\r\ninput_img = Input(shape=(3, 256, 256))\r\n\r\ntower_1 = Convolution2D(64, 1, 1, border_mode='same', activation='relu')(input_img)\r\ntower_1 = Convolution2D(64, 3, 3, border_mode='same', activation='relu')(tower_1)\r\n\r\ntower_2 = Convolution2D(64, 1, 1, border_mode='same', activation='relu')(input_img)\r\ntower_2 = Convolution2D(64, 5, 5, border_mode='same', activation='relu')(tower_2)\r\n\r\ntower_3 = MaxPooling2D((3, 3), strides=(1, 1), border_mode='same')(input_img)\r\ntower_3 = Convolution2D(64, 1, 1, border_mode='same', activation='relu')(tower_3)\r\n\r\noutput = merge([tower_1, tower_2, tower_3], mode='concat', concat_axis=1)`\r\n\r\n**These codes come directly from official document. Hope it will help.**\r\n[http://keras.io/getting-started/functional-api-guide/#getting-started-with-the-keras-functional-api](url)"}}
{"actor":{"display_login":"huster-wgm"}}