{"action":"created","issue":{"url":"https://api.github.com/repos/BenWhetton/keras-surgeon/issues/5","repository_url":"https://api.github.com/repos/BenWhetton/keras-surgeon","labels_url":"https://api.github.com/repos/BenWhetton/keras-surgeon/issues/5/labels{/name}","comments_url":"https://api.github.com/repos/BenWhetton/keras-surgeon/issues/5/comments","events_url":"https://api.github.com/repos/BenWhetton/keras-surgeon/issues/5/events","html_url":"https://github.com/BenWhetton/keras-surgeon/issues/5","id":320455652,"number":5,"title":"Problem to prune VGG-Like archicteture","user":{"login":"arturjordao","id":12400861,"avatar_url":"https://avatars2.githubusercontent.com/u/12400861?v=4","gravatar_id":"","url":"https://api.github.com/users/arturjordao","html_url":"https://github.com/arturjordao","followers_url":"https://api.github.com/users/arturjordao/followers","following_url":"https://api.github.com/users/arturjordao/following{/other_user}","gists_url":"https://api.github.com/users/arturjordao/gists{/gist_id}","starred_url":"https://api.github.com/users/arturjordao/starred{/owner}{/repo}","subscriptions_url":"https://api.github.com/users/arturjordao/subscriptions","organizations_url":"https://api.github.com/users/arturjordao/orgs","repos_url":"https://api.github.com/users/arturjordao/repos","events_url":"https://api.github.com/users/arturjordao/events{/privacy}","received_events_url":"https://api.github.com/users/arturjordao/received_events","type":"User","site_admin":false},"labels":[],"state":"open","locked":false,"assignee":null,"assignees":[],"milestone":null,"comments":1,"created_at":"2018-05-04T23:14:43Z","updated_at":"2018-05-15T21:02:24Z","closed_at":null,"author_association":"NONE","body":"Congratulations, this tool is very useful. However, I'm not able to prune this architecture \r\nLayer (type)                 Output Shape              Param #   \r\n=================================================================\r\nconv2d_1 (Conv2D)            (None, 32, 32, 64)        1792      \r\n_________________________________________________________________\r\nactivation_1 (Activation)    multiple                  0         \r\n_________________________________________________________________\r\nbatch_normalization_1 (Batch (None, 32, 32, 64)        256       \r\n_________________________________________________________________\r\ndropout_1 (Dropout)          multiple                  0         \r\n_________________________________________________________________\r\nconv2d_2 (Conv2D)            (None, 32, 32, 64)        36928     \r\n_________________________________________________________________\r\nactivation_2 (Activation)    (None, 32, 32, 64)        0         \r\n_________________________________________________________________\r\nbatch_normalization_2 (Batch (None, 32, 32, 64)        256       \r\n_________________________________________________________________\r\nmax_pooling2d_1 (MaxPooling2 (None, 16, 16, 64)        0         \r\n_________________________________________________________________\r\nconv2d_3 (Conv2D)            (None, 16, 16, 128)       73856     \r\n_________________________________________________________________\r\nactivation_3 (Activation)    (None, 16, 16, 128)       0         \r\n_________________________________________________________________\r\nbatch_normalization_3 (Batch (None, 16, 16, 128)       512       \r\n_________________________________________________________________\r\ndropout_2 (Dropout)          (None, 16, 16, 128)       0         \r\n_________________________________________________________________\r\nconv2d_4 (Conv2D)            (None, 16, 16, 128)       147584    \r\n_________________________________________________________________\r\nactivation_4 (Activation)    (None, 16, 16, 128)       0         \r\n_________________________________________________________________\r\nbatch_normalization_4 (Batch (None, 16, 16, 128)       512       \r\n_________________________________________________________________\r\nmax_pooling2d_2 (MaxPooling2 (None, 8, 8, 128)         0         \r\n_________________________________________________________________\r\nconv2d_5 (Conv2D)            (None, 8, 8, 256)         295168    \r\n_________________________________________________________________\r\nactivation_5 (Activation)    (None, 8, 8, 256)         0         \r\n_________________________________________________________________\r\nbatch_normalization_5 (Batch (None, 8, 8, 256)         1024      \r\n_________________________________________________________________\r\ndropout_3 (Dropout)          (None, 8, 8, 256)         0         \r\n_________________________________________________________________\r\nconv2d_6 (Conv2D)            (None, 8, 8, 256)         590080    \r\n_________________________________________________________________\r\nactivation_6 (Activation)    (None, 8, 8, 256)         0         \r\n_________________________________________________________________\r\nbatch_normalization_6 (Batch (None, 8, 8, 256)         1024      \r\n_________________________________________________________________\r\ndropout_4 (Dropout)          (None, 8, 8, 256)         0         \r\n_________________________________________________________________\r\nconv2d_7 (Conv2D)            (None, 8, 8, 256)         590080    \r\n_________________________________________________________________\r\nactivation_7 (Activation)    (None, 8, 8, 256)         0         \r\n_________________________________________________________________\r\nbatch_normalization_7 (Batch (None, 8, 8, 256)         1024      \r\n_________________________________________________________________\r\nmax_pooling2d_3 (MaxPooling2 (None, 4, 4, 256)         0         \r\n_________________________________________________________________\r\nconv2d_8 (Conv2D)            (None, 4, 4, 512)         1180160   \r\n_________________________________________________________________\r\nactivation_8 (Activation)    (None, 4, 4, 512)         0         \r\n_________________________________________________________________\r\nbatch_normalization_8 (Batch (None, 4, 4, 512)         2048      \r\n_________________________________________________________________\r\ndropout_5 (Dropout)          (None, 4, 4, 512)         0         \r\n_________________________________________________________________\r\nconv2d_9 (Conv2D)            (None, 4, 4, 512)         2359808   \r\n_________________________________________________________________\r\nactivation_9 (Activation)    (None, 4, 4, 512)         0         \r\n_________________________________________________________________\r\nbatch_normalization_9 (Batch (None, 4, 4, 512)         2048      \r\n_________________________________________________________________\r\ndropout_6 (Dropout)          (None, 4, 4, 512)         0         \r\n_________________________________________________________________\r\nconv2d_10 (Conv2D)           (None, 4, 4, 512)         2359808   \r\n_________________________________________________________________\r\nactivation_10 (Activation)   (None, 4, 4, 512)         0         \r\n_________________________________________________________________\r\nbatch_normalization_10 (Batc (None, 4, 4, 512)         2048      \r\n_________________________________________________________________\r\nmax_pooling2d_4 (MaxPooling2 (None, 2, 2, 512)         0         \r\n_________________________________________________________________\r\nconv2d_11 (Conv2D)           (None, 2, 2, 512)         2359808   \r\n_________________________________________________________________\r\nactivation_11 (Activation)   (None, 2, 2, 512)         0         \r\n_________________________________________________________________\r\nbatch_normalization_11 (Batc (None, 2, 2, 512)         2048      \r\n_________________________________________________________________\r\ndropout_7 (Dropout)          (None, 2, 2, 512)         0         \r\n_________________________________________________________________\r\nconv2d_12 (Conv2D)           (None, 2, 2, 512)         2359808   \r\n_________________________________________________________________\r\nactivation_12 (Activation)   (None, 2, 2, 512)         0         \r\n_________________________________________________________________\r\nbatch_normalization_12 (Batc (None, 2, 2, 512)         2048      \r\n_________________________________________________________________\r\ndropout_8 (Dropout)          (None, 2, 2, 512)         0         \r\n_________________________________________________________________\r\nconv2d_13 (Conv2D)           (None, 2, 2, 512)         2359808   \r\n_________________________________________________________________\r\nactivation_13 (Activation)   (None, 2, 2, 512)         0         \r\n_________________________________________________________________\r\nbatch_normalization_13 (Batc (None, 2, 2, 512)         2048      \r\n_________________________________________________________________\r\nmax_pooling2d_5 (MaxPooling2 (None, 1, 1, 512)         0         \r\n_________________________________________________________________\r\ndropout_9 (Dropout)          (None, 1, 1, 512)         0         \r\n_________________________________________________________________\r\nflatten_1 (Flatten)          (None, 512)               0         \r\n_________________________________________________________________\r\ndense_1 (Dense)              (None, 512)               262656    \r\n_________________________________________________________________\r\nactivation_14 (Activation)   (None, 512)               0         \r\n_________________________________________________________________\r\nbatch_normalization_14 (Batc (None, 512)               2048      \r\n_________________________________________________________________\r\ndropout_10 (Dropout)         (None, 512)               0         \r\n_________________________________________________________________\r\ndense_2 (Dense)              (None, 10)                5130      \r\n_________________________________________________________________\r\nactivation_15 (Activation)   (None, 10)                0         \r\n\r\nI have found this problem when pruning the layer \"conv2d_10\" with these indexes \"[ 34,  58,  63,  72, 141, 142, 154, 172, 177, 181, 182, 202, 204,\r\n       206, 208, 222, 226, 236, 238, 250, 263, 274, 278, 284, 291, 293,\r\n       299, 315, 338, 351, 371, 380, 391, 392, 394, 398, 411, 415, 421,\r\n       423, 431, 446, 469, 474, 479, 482, 489, 492, 493, 504]\":\r\nIndexError: boolean index did not match indexed array along dimension 0; dimension is 3 but corresponding boolean dimension is 2\r\n\r\nUntil pruning this layer the code works fine. Any idea what happens?\r\n\r\n@BenWhetton "},"comment":{"url":"https://api.github.com/repos/BenWhetton/keras-surgeon/issues/comments/389312356","html_url":"https://github.com/BenWhetton/keras-surgeon/issues/5#issuecomment-389312356","issue_url":"https://api.github.com/repos/BenWhetton/keras-surgeon/issues/5","id":389312356,"user":{"login":"BenWhetton","id":23295064,"avatar_url":"https://avatars1.githubusercontent.com/u/23295064?v=4","gravatar_id":"","url":"https://api.github.com/users/BenWhetton","html_url":"https://github.com/BenWhetton","followers_url":"https://api.github.com/users/BenWhetton/followers","following_url":"https://api.github.com/users/BenWhetton/following{/other_user}","gists_url":"https://api.github.com/users/BenWhetton/gists{/gist_id}","starred_url":"https://api.github.com/users/BenWhetton/starred{/owner}{/repo}","subscriptions_url":"https://api.github.com/users/BenWhetton/subscriptions","organizations_url":"https://api.github.com/users/BenWhetton/orgs","repos_url":"https://api.github.com/users/BenWhetton/repos","events_url":"https://api.github.com/users/BenWhetton/events{/privacy}","received_events_url":"https://api.github.com/users/BenWhetton/received_events","type":"User","site_admin":false},"created_at":"2018-05-15T21:02:24Z","updated_at":"2018-05-15T21:02:24Z","author_association":"OWNER","body":"Hi there! I'm glad you're finding the library useful.\r\nIf you supply some example code that demonstrates the problem, I'll have a look at it. I don't have time to recreate your scenario by myself.\r\nThanks!"}}
{"actor":{"display_login":"BenWhetton"}}