資料層（Data）

layers {
  name: "data"
  type: DATA
  top: "data"
  top: "label"
  data_param {
    source: "../data/ImageNet/imagenet-train" #資料存放位置
    batch_size: 128 #一次批處理的大小，視記憶體大小而定。四維陣列N*C*H*W中的N
    backend: LMDB #資料庫型別，預設為leveldb
  }
  include: { phase: TRAIN } #如果加了這一行的話表示是在訓練過程中使用該層，可將TRAIN替換為TEST
}
 

卷積層(Convolution)

layer {
  name: "conv"
  type: "Convolution"
  bottom: "data"
  top: "conv"
  param {  
    lr_mult: 1 #權重的學習率 該層lr=lr_mult*base_lr
    decay_mult: 1 #權重的衰減值
  }
  param {  
    lr_mult: 2 #偏置項的學習率
    decay_mult: 0 #偏置項的衰減值
  }
  convolution_param {
    num_output: 96 #該層輸出的filter的個數。四維陣列N*C*H*W中的W
    kernel_size: 11 #卷積核大小11*11。可設定長kernel_h與寬kernel_w
    stride: 4 #步長，也就是卷積核滑動的距離
    weight_filler { #卷積核初始化方式
      type: "gaussian" #高斯分佈
      std: 0.01 #標準差為0.01
    }
    bias_filler { #偏置項初始化方式
      type: "constant" #連續分佈
      value: 0
    }
  }
}
 

RELU

layer {
  name: "relu"
  type: "ReLU"
  bottom: "conv"
  top: "conv"
}
 

LRN

layer {
  name: "norm"
  type: "LRN"
  bottom: "conv"
  top: "norm"
  lrn_param {
    local_size: 5#對於cross channel LRN，表示需要求和的channel的數量；對於within channel LRN，表示需要求和的空間區域的邊長。預設為5
    alpha: 0.0001 #LRN公式中的引數alpha
    beta: 0.75 #LRN公式中的引數beta
  }
}

POOLING

layer {
  name: "pool"
  type: "Pooling"
  bottom: "norm1"
  top: "pool1"
  pooling_param {
    pool: MAX #有三種池化方式：MAX,AVG,STOCHASTIC
    kernel_size: 3 #卷積核大小;可設定長kernel_h與寬kernel_w
    stride: 2 #步長
  }
}

INNERPRODUCT

layer {
  name: "fc7"
  type: "InnerProduct"
  bottom: "fc6"
  top: "fc7"
  param { 
    lr_mult: 1
    decay_mult: 1
  }
  param { 
    lr_mult: 2
    decay_mult: 0
  }
  inner_product_param {
    num_output: 4096
    weight_filler {
      type: "gaussian"
      std: 0.005
    }
    bias_filler {
      type: "constant"
      value: 0.1
    }
  }
}

ACCURACY

layer {
  name: "accuracy"
  type: "Accuracy"
  bottom: "fc8"
  bottom: "label"
  top: "accuracy"
  include {phase: TEST}
}

accuracy_param {
    top_k: 5
  }

SOFTMAX_LOSS

layers {
  name: "loss"
  type: SOFTMAX_LOSS
  bottom: "pool3"
  bottom: "label"
  top: "loss"
 include: { phase: TRAIN }
}