{"action":"closed","issue":{"url":"https://api.github.com/repos/tiny-dnn/tiny-dnn/issues/357","repository_url":"https://api.github.com/repos/tiny-dnn/tiny-dnn","labels_url":"https://api.github.com/repos/tiny-dnn/tiny-dnn/issues/357/labels{/name}","comments_url":"https://api.github.com/repos/tiny-dnn/tiny-dnn/issues/357/comments","events_url":"https://api.github.com/repos/tiny-dnn/tiny-dnn/issues/357/events","html_url":"https://github.com/tiny-dnn/tiny-dnn/issues/357","id":184506124,"number":357,"title":"Caffe Converter Needs to Be Able to Import Power Layer","user":{"login":"goranrauker","id":10147555,"avatar_url":"https://avatars.githubusercontent.com/u/10147555?v=3","gravatar_id":"","url":"https://api.github.com/users/goranrauker","html_url":"https://github.com/goranrauker","followers_url":"https://api.github.com/users/goranrauker/followers","following_url":"https://api.github.com/users/goranrauker/following{/other_user}","gists_url":"https://api.github.com/users/goranrauker/gists{/gist_id}","starred_url":"https://api.github.com/users/goranrauker/starred{/owner}{/repo}","subscriptions_url":"https://api.github.com/users/goranrauker/subscriptions","organizations_url":"https://api.github.com/users/goranrauker/orgs","repos_url":"https://api.github.com/users/goranrauker/repos","events_url":"https://api.github.com/users/goranrauker/events{/privacy}","received_events_url":"https://api.github.com/users/goranrauker/received_events","type":"User","site_admin":false},"labels":[],"state":"closed","locked":false,"assignee":null,"assignees":[],"milestone":null,"comments":3,"created_at":"2016-10-21T14:55:53Z","updated_at":"2016-10-29T05:06:38Z","closed_at":"2016-10-29T05:06:38Z","body":"When importing some caffe models (see below) a power_layer is required by the converter.  The current layer_factory_impl does not provide it as an option.  In addtion, the power_layer needs to be updated to allow for the optional scale parameter.\r\n\r\n`input: \"data\"\r\ninput_shape {\r\n  dim: 1\r\n  dim: 3\r\n  dim: 28\r\n  dim: 28\r\n}\r\nlayer {\r\n  name: \"scale\"\r\n  type: \"Power\"\r\n  bottom: \"data\"\r\n  top: \"scale\"\r\n  power_param {\r\n    scale: 0.0125\r\n  }\r\n}\r\nlayer {\r\n  name: \"conv1\"\r\n  type: \"Convolution\"\r\n  bottom: \"scale\"\r\n  top: \"conv1\"\r\n  param {\r\n    lr_mult: 1.0\r\n  }\r\n  param {\r\n    lr_mult: 2.0\r\n  }\r\n  convolution_param {\r\n    num_output: 20\r\n    kernel_size: 5\r\n    stride: 1\r\n    weight_filler {\r\n      type: \"xavier\"\r\n    }\r\n    bias_filler {\r\n      type: \"constant\"\r\n    }\r\n  }\r\n}\r\nlayer {\r\n  name: \"pool1\"\r\n  type: \"Pooling\"\r\n  bottom: \"conv1\"\r\n  top: \"pool1\"\r\n  pooling_param {\r\n    pool: MAX\r\n    kernel_size: 2\r\n    stride: 2\r\n  }\r\n}\r\nlayer {\r\n  name: \"conv2\"\r\n  type: \"Convolution\"\r\n  bottom: \"pool1\"\r\n  top: \"conv2\"\r\n  param {\r\n    lr_mult: 1.0\r\n  }\r\n  param {\r\n    lr_mult: 2.0\r\n  }\r\n  convolution_param {\r\n    num_output: 50\r\n    kernel_size: 5\r\n    stride: 1\r\n    weight_filler {\r\n      type: \"xavier\"\r\n    }\r\n    bias_filler {\r\n      type: \"constant\"\r\n    }\r\n  }\r\n}\r\nlayer {\r\n  name: \"pool2\"\r\n  type: \"Pooling\"\r\n  bottom: \"conv2\"\r\n  top: \"pool2\"\r\n  pooling_param {\r\n    pool: MAX\r\n    kernel_size: 2\r\n    stride: 2\r\n  }\r\n}\r\nlayer {\r\n  name: \"ip1\"\r\n  type: \"InnerProduct\"\r\n  bottom: \"pool2\"\r\n  top: \"ip1\"\r\n  param {\r\n    lr_mult: 1.0\r\n  }\r\n  param {\r\n    lr_mult: 2.0\r\n  }\r\n  inner_product_param {\r\n    num_output: 500\r\n    weight_filler {\r\n      type: \"xavier\"\r\n    }\r\n    bias_filler {\r\n      type: \"constant\"\r\n    }\r\n  }\r\n}\r\nlayer {\r\n  name: \"relu1\"\r\n  type: \"ReLU\"\r\n  bottom: \"ip1\"\r\n  top: \"ip1\"\r\n}\r\nlayer {\r\n  name: \"ip2\"\r\n  type: \"InnerProduct\"\r\n  bottom: \"ip1\"\r\n  top: \"ip2\"\r\n  param {\r\n    lr_mult: 1.0\r\n  }\r\n  param {\r\n    lr_mult: 2.0\r\n  }\r\n  inner_product_param {\r\n    num_output: 34\r\n    weight_filler {\r\n      type: \"xavier\"\r\n    }\r\n    bias_filler {\r\n      type: \"constant\"\r\n    }\r\n  }\r\n}\r\nlayer {\r\n  name: \"prob\"\r\n  type: \"Softmax\"\r\n  bottom: \"ip2\"\r\n  top: \"prob\"\r\n}\r\n`"}}
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