深度學習之Caffe框架

caffe框架：
結構：
Blob:stores data and derivatives
Layer: transform bottom blobs to top blobs
Net:Many layers;computes gradients via forward/backward
Solver:Uses gradients to updata weights
流程：
no need to write code
1.convert data (run a script) 數據轉換:create LMDB using convert_imageset
2.define net (edit prototxt) 定義網絡
3.define solver(edit prototxt)) 定義配置參數
4.train (run a script)
學習資源：Model Zoo
#定義層 例

name:""
layer{
   name:""
    trandform_param{
    scale:0.03}
}

layer{
  name:"conv1"
  type:"Convolution"
  bottom:"data"
  top:"conv1"
  param{
      lr_mult:1
  }
  convolution_param{
      num_output:20
      kernel_size:5
      stride:1  
      weight_filler{
          type:"xavier"   #權重初始化方式
      }
      bias_filler{
          type:"constant"
      }
     }
}
layer{
    name:"pool1"
    type:"Pooling"
    bottom:"conv1"
    top:"pool1"
    pooling_param{
        pool:MAX
        kernel_size:2
        stride:2
        }
}

#訓練 例

./bulid/tools/caffe train \
  -gpu 0 \
  -model path/to/trainval.prototxt \
  -solver
  -weights