caffe中的BatchNorm层

在训练一个小的分类网络时，发现加上BatchNorm层之后的检索效果相对于之前，效果会有提升，因此将该网络结构记录在这里，供以后查阅使用：

添加该层之前：

 layer {
   name: "conv1"
   type: "Convolution"
   bottom: "data"
   top: "conv1"
   param {
     lr_mult:
     decay_mult:
   }
   param {
     lr_mult:
     decay_mult:
   }
   convolution_param {
     num_output:
     kernel_size:
     stride:
     weight_filler {
       type: "gaussian"
       std: 0.01
     }
     bias_filler {
       type: "constant"
       value:
     }
   }
 }
 layer {
   name: "relu1"
   type: "ReLU"
   bottom: "conv1"
   top: "conv1"
 }
 layer {
   name: "pool1"
   type: "Pooling"
   bottom: "conv1"
   top: "pool1"
   pooling_param {
     pool: MAX
     kernel_size:
     stride:
   }
 }
 layer {
   name: "norm1"
   type: "LRN"
   bottom: "pool1"
   top: "norm1"
   lrn_param {
     local_size:
     alpha: 0.0001
     beta: 0.75
   }
 }
 layer {
   name: "conv2"
   type: "Convolution"
   bottom: "norm1"
   top: "conv2"
   param {
     lr_mult:
     decay_mult:
   }
   param {
     lr_mult:
     decay_mult:
   }
   convolution_param {
     num_output:
     pad:
     kernel_size:
     stride:
     group:
     weight_filler {
       type: "gaussian"
       std: 0.01
     }
     bias_filler {
       type: "constant"
       value:
     }
   }
 }
 layer {
   name: "relu2"
   type: "ReLU"
   bottom: "conv2"
   top: "conv2"
 }
 layer {
   name: "pool2"
   type: "Pooling"
   bottom: "conv2"
   top: "pool2"
   pooling_param {
     pool: MAX
     kernel_size:
     stride:
   }
 }
 layer {
   name: "norm2"
   type: "LRN"
   bottom: "pool2"
   top: "norm2"
   lrn_param {
     local_size:
     alpha: 0.0001
     beta: 0.75
   }
 }
 layer {
   name: "conv3"
   type: "Convolution"
   bottom: "norm2"
   top: "conv3"
   param {
     lr_mult:
     decay_mult:
   }
   param {
     lr_mult:
     decay_mult:
   }
   convolution_param {
     num_output:
     pad:
     stride:
     kernel_size:
     weight_filler {
       type: "gaussian"
       std: 0.01
     }
     bias_filler {
       type: "constant"
       value:
     }
   }
 }
 layer {
   name: "relu3"
   type: "ReLU"
   bottom: "conv3"
   top: "conv3"
 }
 layer {
   name: "conv4"
   type: "Convolution"
   bottom: "conv3"
   top: "conv4"
   param {
     lr_mult:
     decay_mult:
   }
   param {
     lr_mult:
     decay_mult:
   }
   convolution_param {
     num_output:
     pad:
     kernel_size:
     group:
     weight_filler {
       type: "gaussian"
       std: 0.01
     }
     bias_filler {
       type: "constant"
       value:
     }
   }
 }
 layer {
   name: "relu4"
   type: "ReLU"
   bottom: "conv4"
   top: "conv4"
 }
 layer {
   name: "conv5"
   type: "Convolution"
   bottom: "conv4"
   top: "conv5"
   param {
     lr_mult:
     decay_mult:
   }
   param {
     lr_mult:
     decay_mult:
   }
   convolution_param {
     num_output:
     pad:
     stride:
     kernel_size:
     group:
     weight_filler {
       type: "gaussian"
       std: 0.01
     }
     bias_filler {
       type: "constant"
       value:
     }
   }
 }
 layer {
   name: "relu5"
   type: "ReLU"
   bottom: "conv5"
   top: "conv5"
 }
 layer {
   name: "pool5"
   type: "Pooling"
   bottom: "conv5"
   top: "pool5"
   pooling_param {
     pool: MAX
     kernel_size:
     stride:
   }
 }
 layer {
   name: "fc6_srx"
   type: "InnerProduct"
   bottom: "pool5"
   top: "fc6"
   param {
     lr_mult:
     decay_mult:
   }
   param {
     lr_mult:
     decay_mult:
   }
   inner_product_param {
     num_output:
     weight_filler {
       type: "gaussian"
       std: 0.005
     }
     bias_filler {
       type: "constant"
       value:
     }
   }
 }
 layer {
   name: "relu7"
   type: "ReLU"
   bottom: "fc6"
   top: "fc6"
 }
 layer {
   name: "drop7"
   type: "Dropout"
   bottom: "fc6"
   top: "fc6"
   dropout_param {
     dropout_ratio: 0.5
   }
 }
 layer {
   name: "fc7_srx"
   type: "InnerProduct"
   bottom: "fc6"
   top: "fc7"
   param {
     lr_mult:
     decay_mult:
   }
   param {
     lr_mult:
     decay_mult:
   }
   inner_product_param {
     num_output:
     weight_filler {
       type: "gaussian"
       std: 0.01
     }
     bias_filler {
       type: "constant"
       value:
     }
   }
 }
 layer{
   name: "loss"
   type: "SoftmaxWithLoss"
   top: "SoftmaxWithLoss"
   bottom: "fc7"
   bottom: "label"
   include {
     phase: TRAIN
   }
 }
 layer {
   name: "accuracy"
   type: "Accuracy"
   bottom: "fc7"
   bottom: "label"
   top: "accuracy"
   include {
     phase: TEST
   }
 }

添加该层之后：

 layer {
   name: "conv1"
   type: "Convolution"
   bottom: "data"
   top: "conv1"
   param {
     lr_mult:
     decay_mult:
   }
   param {
     lr_mult:
     decay_mult:
   }
   convolution_param {
     num_output:
     kernel_size:
     stride:
     weight_filler {
       type: "gaussian"
       std: 0.01
     }
     bias_filler {
       type: "constant"
       value:
     }
   }
 }
 ##############
 layer {
   bottom: "conv1"
   top: "conv1"
   name: "bn1"
   type: "BatchNorm"
   param {
     lr_mult:
   }
   param {
     lr_mult:
   }
   param {
     lr_mult:
   }
 }
 ##############
 layer {
   name: "relu1"
   type: "ReLU"
   bottom: "conv1"
   top: "conv1"
 }
 layer {
   name: "pool1"
   type: "Pooling"
   bottom: "conv1"
   top: "pool1"
   pooling_param {
     pool: MAX
     kernel_size:
     stride:
   }
 }

 layer {
   name: "conv2"
   type: "Convolution"
   bottom: "pool1"
   top: "conv2"
   param {
     lr_mult:
     decay_mult:
   }
   param {
     lr_mult:
     decay_mult:
   }
   convolution_param {
     num_output:
     pad:
     kernel_size:
     stride:
     group:
     weight_filler {
       type: "gaussian"
       std: 0.01
     }
     bias_filler {
       type: "constant"
       value:
     }
   }
 }
 ##############
 layer {
   bottom: "conv2"
   top: "conv2"
   name: "bn2"
   type: "BatchNorm"
   param {
     lr_mult:
   }
   param {
     lr_mult:
   }
   param {
     lr_mult:
   }
 }
 ##############
 layer {
   name: "relu2"
   type: "ReLU"
   bottom: "conv2"
   top: "conv2"
 }
 layer {
   name: "pool2"
   type: "Pooling"
   bottom: "conv2"
   top: "pool2"
   pooling_param {
     pool: MAX
     kernel_size:
     stride:
   }
 }

 layer {
   name: "conv3"
   type: "Convolution"
   bottom: "pool2"
   top: "conv3"
   param {
     lr_mult:
     decay_mult:
   }
   param {
     lr_mult:
     decay_mult:
   }
   convolution_param {
     num_output:
     pad:
     stride:
     kernel_size:
     weight_filler {
       type: "gaussian"
       std: 0.01
     }
     bias_filler {
       type: "constant"
       value:
     }
   }
 }
 ##############
 layer {
   bottom: "conv3"
   top: "conv3"
   name: "bn3"
   type: "BatchNorm"
   param {
     lr_mult:
   }
   param {
     lr_mult:
   }
   param {
     lr_mult:
   }
 }
 ##############
 layer {
   name: "relu3"
   type: "ReLU"
   bottom: "conv3"
   top: "conv3"
 }
 layer {
   name: "conv4"
   type: "Convolution"
   bottom: "conv3"
   top: "conv4"
   param {
     lr_mult:
     decay_mult:
   }
   param {
     lr_mult:
     decay_mult:
   }
   convolution_param {
     num_output:
     pad:
     kernel_size:
     group:
     weight_filler {
       type: "gaussian"
       std: 0.01
     }
     bias_filler {
       type: "constant"
       value:
     }
   }
 }
 ##############
 layer {
   bottom: "conv4"
   top: "conv4"
   name: "bn4"
   type: "BatchNorm"
   param {
     lr_mult:
   }
   param {
     lr_mult:
   }
   param {
     lr_mult:
   }
 }
 ##############
 layer {
   name: "relu4"
   type: "ReLU"
   bottom: "conv4"
   top: "conv4"
 }
 layer {
   name: "conv5"
   type: "Convolution"
   bottom: "conv4"
   top: "conv5"
   param {
     lr_mult:
     decay_mult:
   }
   param {
     lr_mult:
     decay_mult:
   }
   convolution_param {
     num_output:
     pad:
     stride:
     kernel_size:
     group:
     weight_filler {
       type: "gaussian"
       std: 0.01
     }
     bias_filler {
       type: "constant"
       value:
     }
   }
 }
 ##############
 layer {
   bottom: "conv5"
   top: "conv5"
   name: "bn5"
   type: "BatchNorm"
   param {
     lr_mult:
   }
   param {
     lr_mult:
   }
   param {
     lr_mult:
   }
 }
 ##############
 layer {
   name: "relu5"
   type: "ReLU"
   bottom: "conv5"
   top: "conv5"
 }
 layer {
   name: "pool5"
   type: "Pooling"
   bottom: "conv5"
   top: "pool5"
   pooling_param {
     pool: MAX
     kernel_size:
     stride:
   }
 }
 layer {
   name: "fc6_srx"
   type: "InnerProduct"
   bottom: "pool5"
   top: "fc6"
   param {
     lr_mult:
     decay_mult:
   }
   param {
     lr_mult:
     decay_mult:
   }
   inner_product_param {
     num_output:
     weight_filler {
       type: "gaussian"
       std: 0.005
     }
     bias_filler {
       type: "constant"
       value:
     }
   }
 }
 layer {
   name: "relu7"
   type: "ReLU"
   bottom: "fc6"
   top: "fc6"
 }
 layer {
   name: "drop7"
   type: "Dropout"
   bottom: "fc6"
   top: "fc6"
   dropout_param {
     dropout_ratio: 0.5
   }
 }
 layer {
   name: "fc7_srx"
   type: "InnerProduct"
   bottom: "fc6"
   top: "fc7"
   param {
     lr_mult:
     decay_mult:
   }
   param {
     lr_mult:
     decay_mult:
   }
   inner_product_param {
     num_output:
     weight_filler {
       type: "gaussian"
       std: 0.01
     }
     bias_filler {
       type: "constant"
       value:
     }
   }
 }
 layer{
   name: "loss"
   type: "SoftmaxWithLoss"
   top: "SoftmaxWithLoss"
   bottom: "fc7"
   bottom: "label"
   include {
     phase: TRAIN
   }
 }
 layer {
   name: "accuracy"
   type: "Accuracy"
   bottom: "fc7"
   bottom: "label"
   top: "accuracy"
   include {
     phase: TEST
   }
 }