读懂caffe模型

caffe 模型可视化网页网址：http://ethereon.github.io/netscope/#/editor
caffe模型是自底向上流的，所以bottom是前一层，top就是本层，输出层

卷积层

　　lr_mult: 學習率的係數，最終的學習率是這個數乘以solver.prototxt配置文檔中的base_lr。如果有兩個lr_mult, 則第一個表示權值的學習率，第二個表示偏置項的學習率。一般偏置項的學習率是權值學習率的兩倍
　　r_mult indicates what to multiply the learning rate by for a particular layer. This is useful if you want to update some layers with a smaller learning rate (e.g. when finetuning some layers while training others from scratch) or if you do not want to update the weights for one layer, decay_mult is the same, just for weight decay.

layer {
  name: "conv_att"
  type: "Convolution"
  bottom: "input_feature"
  top: "conv_att"
  param {
    lr_mult: 1 #kernel lr——mult
    decay_mult: 1
  }
  param {
    lr_mult: 2 # bias lr——mult
    decay_mult: 0 
  }
  convolution_param {
    num_output: 8  #输出feature map个数
    kernel_size: 1 # 卷积核大小
    weight_filler {
      type: "msra"
    }
    bias_filler {
      type: "constant"
    }
  }
}