IRG.prototxt

name: "resnet_cifar10_IRG"
layer {
  name: "Data1"
  type: "Data"
  top: "Data1"
  top: "Data2"
  include {
    phase: TRAIN
  }
  transform_param {
    mean_file: "yourpath/mean_pad.binaryproto"
    crop_size: 32
    mirror:true
  }
  data_param {
    source: "yourpath/cifar-10-train_lmdb"
    batch_size: 64 
    backend: LMDB
  }
}
layer {
  name: "Data1"
  type: "Data"
  top: "Data1"
  top: "Data2"
  include {
    phase: TEST
  }
  transform_param {
    mean_file: "yourpath/mean.binaryproto"
  }
  data_param {
    source: "yourpath/cifar10_test_lmdb"
    batch_size: 100
    backend: LMDB
  }
}
layer {
  name: "Convolution1"
  type: "Convolution"
  bottom: "Data1"
  top: "Convolution1"
  param {
    lr_mult: 0
    decay_mult: 0
  }
  param {
    lr_mult: 0
    decay_mult: 0
  }
  convolution_param {
    num_output: 16
    pad: 1
    kernel_size: 3
    stride: 1
    weight_filler {
      type: "msra"
    }
    bias_filler {
      type: "constant"
      value: 0
    }
  }
}
layer {
  name: "BatchNorm1"
  type: "BatchNorm"
  bottom: "Convolution1"
  top: "Convolution1"
  param {
    lr_mult: 0
    decay_mult: 0
  }
  param {
    lr_mult: 0
    decay_mult: 0
  }
  param {
    lr_mult: 0
    decay_mult: 0
  }
  batch_norm_param {
    use_global_stats: true
  }
}
layer {
  name: "Scale1"
  type: "Scale"
  bottom: "Convolution1"
  top: "Convolution1"
  param {
    lr_mult: 0
    decay_mult: 0
  }
  param {
    lr_mult: 0
    decay_mult: 0
  }
  scale_param {
    bias_term: true
  }
}
layer {
  name: "ReLU1"
  type: "ReLU"
  bottom: "Convolution1"
  top: "Convolution1"
}
layer {
  name: "Convolution2"
  type: "Convolution"
  bottom: "Convolution1"
  top: "Convolution2"
  param {
    lr_mult: 0
    decay_mult: 0
  }
  param {
    lr_mult: 0
    decay_mult: 0
  }
  convolution_param {
    num_output: 16
    pad: 1
    kernel_size: 3
    stride: 1
    weight_filler {
      type: "msra"
    }
    bias_filler {
      type: "constant"
      value: 0
    }
  }
}
layer {
  name: "BatchNorm2"
  type: "BatchNorm"
  bottom: "Convolution2"
  top: "Convolution2"
  param {
    lr_mult: 0
    decay_mult: 0
  }
  param {
    lr_mult: 0
    decay_mult: 0
  }
  param {
    lr_mult: 0
    decay_mult: 0
  }
  batch_norm_param {
    use_global_stats: true
  }
}
layer {
  name: "Scale2"
  type: "Scale"
  bottom: "Convolution2"
  top: "Convolution2"
  param {
    lr_mult: 0
    decay_mult: 0
  }
  param {
    lr_mult: 0
    decay_mult: 0
  }
  scale_param {
    bias_term: true
  }
}
layer {
  name: "ReLU2"
  type: "ReLU"
  bottom: "Convolution2"
  top: "Convolution2"
}
layer {
  name: "Convolution3"
  type: "Convolution"
  bottom: "Convolution2"
  top: "Convolution3"
  param {
    lr_mult: 0
    decay_mult: 0
  }
  param {
    lr_mult: 0
    decay_mult: 0
  }
  convolution_param {
    num_output: 16
    pad: 1
    kernel_size: 3
    stride: 1
    weight_filler {
      type: "msra"
    }
    bias_filler {
      type: "constant"
      value: 0
    }
  }
}
layer {
  name: "BatchNorm3"
  type: "BatchNorm"
  bottom: "Convolution3"
  top: "Convolution3"
  param {
    lr_mult: 0
    decay_mult: 0
  }
  param {
    lr_mult: 0
    decay_mult: 0
  }
  param {
    lr_mult: 0
    decay_mult: 0
  }
  batch_norm_param {
    use_global_stats: true
  }
}
layer {
  name: "Scale3"
  type: "Scale"
  bottom: "Convolution3"
  top: "Convolution3"
  param {
    lr_mult: 0
    decay_mult: 0
  }
  param {
    lr_mult: 0
    decay_mult: 0
  }
  scale_param {
    bias_term: true
  }
}
layer {
  name: "Eltwise1"
  type: "Eltwise"
  bottom: "Convolution1"
  bottom: "Convolution3"
  top: "Eltwise1"
  eltwise_param {
    operation: SUM
  }
}
layer {
  name: "ReLU3"
  type: "ReLU"
  bottom: "Eltwise1"
  top: "Eltwise1"
}
layer {
  name: "Convolution4"
  type: "Convolution"
  bottom: "Eltwise1"
  top: "Convolution4"
  param {
    lr_mult: 0
    decay_mult: 0
  }
  param {
    lr_mult: 0
    decay_mult: 0
  }
  convolution_param {
    num_output: 16
    pad: 1
    kernel_size: 3
    stride: 1
    weight_filler {
      type: "msra"
    }
    bias_filler {
      type: "constant"
      value: 0
    }
  }
}
layer {
  name: "BatchNorm4"
  type: "BatchNorm"
  bottom: "Convolution4"
  top: "Convolution4"
  param {
    lr_mult: 0
    decay_mult: 0
  }
  param {
    lr_mult: 0
    decay_mult: 0
  }
  param {
    lr_mult: 0
    decay_mult: 0
  }
  batch_norm_param {
    use_global_stats: true
  }
}
layer {
  name: "Scale4"
  type: "Scale"
  bottom: "Convolution4"
  top: "Convolution4"
  param {
    lr_mult: 0
    decay_mult: 0
  }
  param {
    lr_mult: 0
    decay_mult: 0
  }
  scale_param {
    bias_term: true
  }
}
layer {
  name: "ReLU4"
  type: "ReLU"
  bottom: "Convolution4"
  top: "Convolution4"
}
layer {
  name: "Convolution5"
  type: "Convolution"
  bottom: "Convolution4"
  top: "Convolution5"
  param {
    lr_mult: 0
    decay_mult: 0
  }
  param {
    lr_mult: 0
    decay_mult: 0
  }
  convolution_param {
    num_output: 16
    pad: 1
    kernel_size: 3
    stride: 1
    weight_filler {
      type: "msra"
    }
    bias_filler {
      type: "constant"
      value: 0
    }
  }
}
layer {
  name: "BatchNorm5"
  type: "BatchNorm"
  bottom: "Convolution5"
  top: "Convolution5"
  param {
    lr_mult: 0
    decay_mult: 0
  }
  param {
    lr_mult: 0
    decay_mult: 0
  }
  param {
    lr_mult: 0
    decay_mult: 0
  }
  batch_norm_param {
    use_global_stats: true
  }
}
layer {
  name: "Scale5"
  type: "Scale"
  bottom: "Convolution5"
  top: "Convolution5"
  param {
    lr_mult: 0
    decay_mult: 0
  }
  param {
    lr_mult: 0
    decay_mult: 0
  }
  scale_param {
    bias_term: true
  }
}
layer {
  name: "Eltwise2"
  type: "Eltwise"
  bottom: "Eltwise1"
  bottom: "Convolution5"
  top: "Eltwise2"
  eltwise_param {
    operation: SUM
  }
}
layer {
  name: "ReLU5"
  type: "ReLU"
  bottom: "Eltwise2"
  top: "Eltwise2"
}
layer {
  name: "Convolution6"
  type: "Convolution"
  bottom: "Eltwise2"
  top: "Convolution6"
  param {
    lr_mult: 0
    decay_mult: 0
  }
  param {
    lr_mult: 0
    decay_mult: 0
  }
  convolution_param {
    num_output: 16
    pad: 1
    kernel_size: 3
    stride: 1
    weight_filler {
      type: "msra"
    }
    bias_filler {
      type: "constant"
      value: 0
    }
  }
}
layer {
  name: "BatchNorm6"
  type: "BatchNorm"
  bottom: "Convolution6"
  top: "Convolution6"
  param {
    lr_mult: 0
    decay_mult: 0
  }
  param {
    lr_mult: 0
    decay_mult: 0
  }
  param {
    lr_mult: 0
    decay_mult: 0
  }
  batch_norm_param {
    use_global_stats: true
  }
}
layer {
  name: "Scale6"
  type: "Scale"
  bottom: "Convolution6"
  top: "Convolution6"
  param {
    lr_mult: 0
    decay_mult: 0
  }
  param {
    lr_mult: 0
    decay_mult: 0
  }
  scale_param {
    bias_term: true
  }
}
layer {
  name: "ReLU6"
  type: "ReLU"
  bottom: "Convolution6"
  top: "Convolution6"
}
layer {
  name: "Convolution7"
  type: "Convolution"
  bottom: "Convolution6"
  top: "Convolution7"
  param {
    lr_mult: 0
    decay_mult: 0
  }
  param {
    lr_mult: 0
    decay_mult: 0
  }
  convolution_param {
    num_output: 16
    pad: 1
    kernel_size: 3
    stride: 1
    weight_filler {
      type: "msra"
    }
    bias_filler {
      type: "constant"
      value: 0
    }
  }
}
layer {
  name: "BatchNorm7"
  type: "BatchNorm"
  bottom: "Convolution7"
  top: "Convolution7"
  param {
    lr_mult: 0
    decay_mult: 0
  }
  param {
    lr_mult: 0
    decay_mult: 0
  }
  param {
    lr_mult: 0
    decay_mult: 0
  }
  batch_norm_param {
    use_global_stats: true
  }
}
layer {
  name: "Scale7"
  type: "Scale"
  bottom: "Convolution7"
  top: "Convolution7"
  param {
    lr_mult: 0
    decay_mult: 0
  }
  param {
    lr_mult: 0
    decay_mult: 0
  }
  scale_param {
    bias_term: true
  }
}
layer {
  name: "Eltwise3"
  type: "Eltwise"
  bottom: "Eltwise2"
  bottom: "Convolution7"
  top: "Eltwise3"
  eltwise_param {
    operation: SUM
  }
}
layer {
  name: "ReLU7"
  type: "ReLU"
  bottom: "Eltwise3"
  top: "Eltwise3"
}
layer {
  name: "Convolution8"
  type: "Convolution"
  bottom: "Eltwise3"
  top: "Convolution8"
  param {
    lr_mult: 0
    decay_mult: 0
  }
  param {
    lr_mult: 0
    decay_mult: 0
  }
  convolution_param {
    num_output: 32
    pad: 0
    kernel_size: 1
    stride: 2
    weight_filler {
      type: "msra"
    }
    bias_filler {
      type: "constant"
      value: 0
    }
  }
}
layer {
  name: "BatchNorm8"
  type: "BatchNorm"
  bottom: "Convolution8"
  top: "Convolution8"
  param {
    lr_mult: 0
    decay_mult: 0
  }
  param {
    lr_mult: 0
    decay_mult: 0
  }
  param {
    lr_mult: 0
    decay_mult: 0
  }
  batch_norm_param {
    use_global_stats: true
  }
}
layer {
  name: "Scale8"
  type: "Scale"
  bottom: "Convolution8"
  top: "Convolution8"
  param {
    lr_mult: 0
    decay_mult: 0
  }
  param {
    lr_mult: 0
    decay_mult: 0
  }
  scale_param {
    bias_term: true
  }
}
layer {
  name: "Convolution9"
  type: "Convolution"
  bottom: "Eltwise3"
  top: "Convolution9"
  param {
    lr_mult: 0
    decay_mult: 0
  }
  param {
    lr_mult: 0
    decay_mult: 0
  }
  convolution_param {
    num_output: 32
    pad: 1
    kernel_size: 3
    stride: 2
    weight_filler {
      type: "msra"
    }
    bias_filler {
      type: "constant"
      value: 0
    }
  }
}
layer {
  name: "BatchNorm9"
  type: "BatchNorm"
  bottom: "Convolution9"
  top: "Convolution9"
  param {
    lr_mult: 0
    decay_mult: 0
  }
  param {
    lr_mult: 0
    decay_mult: 0
  }
  param {
    lr_mult: 0
    decay_mult: 0
  }
  batch_norm_param {
    use_global_stats: true
  }
}
layer {
  name: "Scale9"
  type: "Scale"
  bottom: "Convolution9"
  top: "Convolution9"
  param {
    lr_mult: 0
    decay_mult: 0
  }
  param {
    lr_mult: 0
    decay_mult: 0
  }
  scale_param {
    bias_term: true
  }
}
layer {
  name: "ReLU8"
  type: "ReLU"
  bottom: "Convolution9"
  top: "Convolution9"
}
layer {
  name: "Convolution10"
  type: "Convolution"
  bottom: "Convolution9"
  top: "Convolution10"
  param {
    lr_mult: 0
    decay_mult: 0
  }
  param {
    lr_mult: 0
    decay_mult: 0
  }
  convolution_param {
    num_output: 32
    pad: 1
    kernel_size: 3
    stride: 1
    weight_filler {
      type: "msra"
    }
    bias_filler {
      type: "constant"
      value: 0
    }
  }
}
layer {
  name: "BatchNorm10"
  type: "BatchNorm"
  bottom: "Convolution10"
  top: "Convolution10"
  param {
    lr_mult: 0
    decay_mult: 0
  }
  param {
    lr_mult: 0
    decay_mult: 0
  }
  param {
    lr_mult: 0
    decay_mult: 0
  }
  batch_norm_param {
    use_global_stats: true
  }
}
layer {
  name: "Scale10"
  type: "Scale"
  bottom: "Convolution10"
  top: "Convolution10"
  param {
    lr_mult: 0
    decay_mult: 0
  }
  param {
    lr_mult: 0
    decay_mult: 0
  }
  scale_param {
    bias_term: true
  }
}
layer {
  name: "Eltwise4"
  type: "Eltwise"
  bottom: "Convolution8"
  bottom: "Convolution10"
  top: "Eltwise4"
  eltwise_param {
    operation: SUM
  }
}
layer {
  name: "ReLU9"
  type: "ReLU"
  bottom: "Eltwise4"
  top: "Eltwise4"
}
layer {
  name: "Convolution11"
  type: "Convolution"
  bottom: "Eltwise4"
  top: "Convolution11"
  param {
    lr_mult: 0
    decay_mult: 0
  }
  param {
    lr_mult: 0
    decay_mult: 0
  }
  convolution_param {
    num_output: 32
    pad: 1
    kernel_size: 3
    stride: 1
    weight_filler {
      type: "msra"
    }
    bias_filler {
      type: "constant"
      value: 0
    }
  }
}
layer {
  name: "BatchNorm11"
  type: "BatchNorm"
  bottom: "Convolution11"
  top: "Convolution11"
  param {
    lr_mult: 0
    decay_mult: 0
  }
  param {
    lr_mult: 0
    decay_mult: 0
  }
  param {
    lr_mult: 0
    decay_mult: 0
  }
  batch_norm_param {
    use_global_stats: true
  }
}
layer {
  name: "Scale11"
  type: "Scale"
  bottom: "Convolution11"
  top: "Convolution11"
  param {
    lr_mult: 0
    decay_mult: 0
  }
  param {
    lr_mult: 0
    decay_mult: 0
  }
  scale_param {
    bias_term: true
  }
}
layer {
  name: "ReLU10"
  type: "ReLU"
  bottom: "Convolution11"
  top: "Convolution11"
}
layer {
  name: "Convolution12"
  type: "Convolution"
  bottom: "Convolution11"
  top: "Convolution12"
  param {
    lr_mult: 0
    decay_mult: 0
  }
  param {
    lr_mult: 0
    decay_mult: 0
  }
  convolution_param {
    num_output: 32
    pad: 1
    kernel_size: 3
    stride: 1
    weight_filler {
      type: "msra"
    }
    bias_filler {
      type: "constant"
      value: 0
    }
  }
}
layer {
  name: "BatchNorm12"
  type: "BatchNorm"
  bottom: "Convolution12"
  top: "Convolution12"
  param {
    lr_mult: 0
    decay_mult: 0
  }
  param {
    lr_mult: 0
    decay_mult: 0
  }
  param {
    lr_mult: 0
    decay_mult: 0
  }
  batch_norm_param {
    use_global_stats: true
  }
}
layer {
  name: "Scale12"
  type: "Scale"
  bottom: "Convolution12"
  top: "Convolution12"
  param {
    lr_mult: 0
    decay_mult: 0
  }
  param {
    lr_mult: 0
    decay_mult: 0
  }
  scale_param {
    bias_term: true
  }
}
layer {
  name: "Eltwise5"
  type: "Eltwise"
  bottom: "Eltwise4"
  bottom: "Convolution12"
  top: "Eltwise5"
  eltwise_param {
    operation: SUM
  }
}
layer {
  name: "ReLU11"
  type: "ReLU"
  bottom: "Eltwise5"
  top: "Eltwise5"
}
layer {
  name: "Convolution13"
  type: "Convolution"
  bottom: "Eltwise5"
  top: "Convolution13"
  param {
    lr_mult: 0
    decay_mult: 0
  }
  param {
    lr_mult: 0
    decay_mult: 0
  }
  convolution_param {
    num_output: 32
    pad: 1
    kernel_size: 3
    stride: 1
    weight_filler {
      type: "msra"
    }
    bias_filler {
      type: "constant"
      value: 0
    }
  }
}
layer {
  name: "BatchNorm13"
  type: "BatchNorm"
  bottom: "Convolution13"
  top: "Convolution13"
  param {
    lr_mult: 0
    decay_mult: 0
  }
  param {
    lr_mult: 0
    decay_mult: 0
  }
  param {
    lr_mult: 0
    decay_mult: 0
  }
  batch_norm_param {
    use_global_stats: true
  }
}
layer {
  name: "Scale13"
  type: "Scale"
  bottom: "Convolution13"
  top: "Convolution13"
  param {
    lr_mult: 0
    decay_mult: 0
  }
  param {
    lr_mult: 0
    decay_mult: 0
  }
  scale_param {
    bias_term: true
  }
}
layer {
  name: "ReLU12"
  type: "ReLU"
  bottom: "Convolution13"
  top: "Convolution13"
}
layer {
  name: "Convolution14"
  type: "Convolution"
  bottom: "Convolution13"
  top: "Convolution14"
  param {
    lr_mult: 0
    decay_mult: 0
  }
  param {
    lr_mult: 0
    decay_mult: 0
  }
  convolution_param {
    num_output: 32
    pad: 1
    kernel_size: 3
    stride: 1
    weight_filler {
      type: "msra"
    }
    bias_filler {
      type: "constant"
      value: 0
    }
  }
}
layer {
  name: "BatchNorm14"
  type: "BatchNorm"
  bottom: "Convolution14"
  top: "Convolution14"
  param {
    lr_mult: 0
    decay_mult: 0
  }
  param {
    lr_mult: 0
    decay_mult: 0
  }
  param {
    lr_mult: 0
    decay_mult: 0
  }
  batch_norm_param {
    use_global_stats: true
  }
}
layer {
  name: "Scale14"
  type: "Scale"
  bottom: "Convolution14"
  top: "Convolution14"
  param {
    lr_mult: 0
    decay_mult: 0
  }
  param {
    lr_mult: 0
    decay_mult: 0
  }
  scale_param {
    bias_term: true
  }
}
layer {
  name: "Eltwise6"
  type: "Eltwise"
  bottom: "Eltwise5"
  bottom: "Convolution14"
  top: "Eltwise6"
  eltwise_param {
    operation: SUM
  }
}
layer {
  name: "ReLU13"
  type: "ReLU"
  bottom: "Eltwise6"
  top: "Eltwise6"
}
layer {
  name: "Convolution15"
  type: "Convolution"
  bottom: "Eltwise6"
  top: "Convolution15"
  param {
    lr_mult: 0
    decay_mult: 0
  }
  param {
    lr_mult: 0
    decay_mult: 0
  }
  convolution_param {
    num_output: 64
    pad: 0
    kernel_size: 1
    stride: 2
    weight_filler {
      type: "msra"
    }
    bias_filler {
      type: "constant"
      value: 0
    }
  }
}
layer {
  name: "BatchNorm15"
  type: "BatchNorm"
  bottom: "Convolution15"
  top: "Convolution15"
  param {
    lr_mult: 0
    decay_mult: 0
  }
  param {
    lr_mult: 0
    decay_mult: 0
  }
  param {
    lr_mult: 0
    decay_mult: 0
  }
  batch_norm_param {
    use_global_stats: true
  }
}
layer {
  name: "Scale15"
  type: "Scale"
  bottom: "Convolution15"
  top: "Convolution15"
  param {
    lr_mult: 0
    decay_mult: 0
  }
  param {
    lr_mult: 0
    decay_mult: 0
  }
  scale_param {
    bias_term: true
  }
}
layer {
  name: "Convolution16"
  type: "Convolution"
  bottom: "Eltwise6"
  top: "Convolution16"
  param {
    lr_mult: 0
    decay_mult: 0
  }
  param {
    lr_mult: 0
    decay_mult: 0
  }
  convolution_param {
    num_output: 64
    pad: 1
    kernel_size: 3
    stride: 2
    weight_filler {
      type: "msra"
    }
    bias_filler {
      type: "constant"
      value: 0
    }
  }
}
layer {
  name: "BatchNorm16"
  type: "BatchNorm"
  bottom: "Convolution16"
  top: "Convolution16"
  param {
    lr_mult: 0
    decay_mult: 0
  }
  param {
    lr_mult: 0
    decay_mult: 0
  }
  param {
    lr_mult: 0
    decay_mult: 0
  }
  batch_norm_param {
    use_global_stats: true
  }
}
layer {
  name: "Scale16"
  type: "Scale"
  bottom: "Convolution16"
  top: "Convolution16"
  param {
    lr_mult: 0
    decay_mult: 0
  }
  param {
    lr_mult: 0
    decay_mult: 0
  }
  scale_param {
    bias_term: true
  }
}
layer {
  name: "ReLU14"
  type: "ReLU"
  bottom: "Convolution16"
  top: "Convolution16"
}
layer {
  name: "Convolution17"
  type: "Convolution"
  bottom: "Convolution16"
  top: "Convolution17"
  param {
    lr_mult: 0
    decay_mult: 0
  }
  param {
    lr_mult: 0
    decay_mult: 0
  }
  convolution_param {
    num_output: 64
    pad: 1
    kernel_size: 3
    stride: 1
    weight_filler {
      type: "msra"
    }
    bias_filler {
      type: "constant"
      value: 0
    }
  }
}
layer {
  name: "BatchNorm17"
  type: "BatchNorm"
  bottom: "Convolution17"
  top: "Convolution17"
  param {
    lr_mult: 0
    decay_mult: 0
  }
  param {
    lr_mult: 0
    decay_mult: 0
  }
  param {
    lr_mult: 0
    decay_mult: 0
  }
  batch_norm_param {
    use_global_stats: true
  }
}
layer {
  name: "Scale17"
  type: "Scale"
  bottom: "Convolution17"
  top: "Convolution17"
  param {
    lr_mult: 0
    decay_mult: 0
  }
  param {
    lr_mult: 0
    decay_mult: 0
  }
  scale_param {
    bias_term: true
  }
}
layer {
  name: "Eltwise7"
  type: "Eltwise"
  bottom: "Convolution15"
  bottom: "Convolution17"
  top: "Eltwise7"
  eltwise_param {
    operation: SUM
  }
}
layer {
  name: "ReLU15"
  type: "ReLU"
  bottom: "Eltwise7"
  top: "Eltwise7"
}
layer {
  name: "Convolution18"
  type: "Convolution"
  bottom: "Eltwise7"
  top: "Convolution18"
  param {
    lr_mult: 0
    decay_mult: 0
  }
  param {
    lr_mult: 0
    decay_mult: 0
  }
  convolution_param {
    num_output: 64
    pad: 1
    kernel_size: 3
    stride: 1
    weight_filler {
      type: "msra"
    }
    bias_filler {
      type: "constant"
      value: 0
    }
  }
}
layer {
  name: "BatchNorm18"
  type: "BatchNorm"
  bottom: "Convolution18"
  top: "Convolution18"
  param {
    lr_mult: 0
    decay_mult: 0
  }
  param {
    lr_mult: 0
    decay_mult: 0
  }
  param {
    lr_mult: 0
    decay_mult: 0
  }
  batch_norm_param {
    use_global_stats: true
  }
}
layer {
  name: "Scale18"
  type: "Scale"
  bottom: "Convolution18"
  top: "Convolution18"
  param {
    lr_mult: 0
    decay_mult: 0
  }
  param {
    lr_mult: 0
    decay_mult: 0
  }
  scale_param {
    bias_term: true
  }
}
layer {
  name: "ReLU16"
  type: "ReLU"
  bottom: "Convolution18"
  top: "Convolution18"
}
layer {
  name: "Convolution19"
  type: "Convolution"
  bottom: "Convolution18"
  top: "Convolution19"
  param {
    lr_mult: 0
    decay_mult: 0
  }
  param {
    lr_mult: 0
    decay_mult: 0
  }
  convolution_param {
    num_output: 64
    pad: 1
    kernel_size: 3
    stride: 1
    weight_filler {
      type: "msra"
    }
    bias_filler {
      type: "constant"
      value: 0
    }
  }
}
layer {
  name: "BatchNorm19"
  type: "BatchNorm"
  bottom: "Convolution19"
  top: "Convolution19"
  param {
    lr_mult: 0
    decay_mult: 0
  }
  param {
    lr_mult: 0
    decay_mult: 0
  }
  param {
    lr_mult: 0
    decay_mult: 0
  }
  batch_norm_param {
    use_global_stats: true
  }
}
layer {
  name: "Scale19"
  type: "Scale"
  bottom: "Convolution19"
  top: "Convolution19"
  param {
    lr_mult: 0
    decay_mult: 0
  }
  param {
    lr_mult: 0
    decay_mult: 0
  }
  scale_param {
    bias_term: true
  }
}
layer {
  name: "Eltwise8"
  type: "Eltwise"
  bottom: "Eltwise7"
  bottom: "Convolution19"
  top: "Eltwise8"
  eltwise_param {
    operation: SUM
  }
}
layer {
  name: "ReLU17"
  type: "ReLU"
  bottom: "Eltwise8"
  top: "Eltwise8"
}
layer {
  name: "Convolution20"
  type: "Convolution"
  bottom: "Eltwise8"
  top: "Convolution20"
  param {
    lr_mult: 0
    decay_mult: 0
  }
  param {
    lr_mult: 0
    decay_mult: 0
  }
  convolution_param {
    num_output: 64
    pad: 1
    kernel_size: 3
    stride: 1
    weight_filler {
      type: "msra"
    }
    bias_filler {
      type: "constant"
      value: 0
    }
  }
}
layer {
  name: "BatchNorm20"
  type: "BatchNorm"
  bottom: "Convolution20"
  top: "Convolution20"
  param {
    lr_mult: 0
    decay_mult: 0
  }
  param {
    lr_mult: 0
    decay_mult: 0
  }
  param {
    lr_mult: 0
    decay_mult: 0
  }
  batch_norm_param {
    use_global_stats: true
  }
}
layer {
  name: "Scale20"
  type: "Scale"
  bottom: "Convolution20"
  top: "Convolution20"
  param {
    lr_mult: 0
    decay_mult: 0
  }
  param {
    lr_mult: 0
    decay_mult: 0
  }
  scale_param {
    bias_term: true
  }
}
layer {
  name: "ReLU18"
  type: "ReLU"
  bottom: "Convolution20"
  top: "Convolution20"
}
layer {
  name: "Convolution21"
  type: "Convolution"
  bottom: "Convolution20"
  top: "Convolution21"
  param {
    lr_mult: 0
    decay_mult: 0
  }
  param {
    lr_mult: 0
    decay_mult: 0
  }
  convolution_param {
    num_output: 64
    pad: 1
    kernel_size: 3
    stride: 1
    weight_filler {
      type: "msra"
    }
    bias_filler {
      type: "constant"
      value: 0
    }
  }
}
layer {
  name: "BatchNorm21"
  type: "BatchNorm"
  bottom: "Convolution21"
  top: "Convolution21"
  param {
    lr_mult: 0
    decay_mult: 0
  }
  param {
    lr_mult: 0
    decay_mult: 0
  }
  param {
    lr_mult: 0
    decay_mult: 0
  }
  batch_norm_param {
    use_global_stats: true
  }
}
layer {
  name: "Scale21"
  type: "Scale"
  bottom: "Convolution21"
  top: "Convolution21"
  param {
    lr_mult: 0
    decay_mult: 0
  }
  param {
    lr_mult: 0
    decay_mult: 0
  }
  scale_param {
    bias_term: true
  }
}
layer {
  name: "Eltwise9"
  type: "Eltwise"
  bottom: "Eltwise8"
  bottom: "Convolution21"
  top: "Eltwise9"
  eltwise_param {
    operation: SUM
  }
}
layer {
  name: "ReLU19"
  type: "ReLU"
  bottom: "Eltwise9"
  top: "Eltwise9"
}
layer {
  name: "Pooling1"
  type: "Pooling"
  bottom: "Eltwise9"
  top: "Pooling1"
  pooling_param {
    pool: AVE
    global_pooling: true
  }
}
layer {
  name: "bem"
  type: "BatchEuclideanMap"
  bottom: "Pooling1"
  top: "bem"
}
layer {
  name: "InnerProduct1"
  type: "InnerProduct"
  bottom: "Pooling1"
  top: "InnerProduct1"
  param {
    lr_mult: 0
    decay_mult: 0
  }
  param {
    lr_mult: 0
    decay_mult: 0
  }
  inner_product_param {
    num_output: 10
    weight_filler {
      type: "msra"
    }
    bias_filler {
      type: "constant"
      value: 0
    }
  }
}
layer {
  name: "SoftmaxWithLoss1"
  type: "SoftmaxWithLoss"
  bottom: "InnerProduct1"
  bottom: "Data2"
  top: "SoftmaxWithLoss1"
  loss_weight: 0
}
layer {
  name: "Accuracy1"
  type: "Accuracy"
  bottom: "InnerProduct1"
  bottom: "Data2"
  top: "Accuracy1"
  include {
    phase: TEST
  }
}

layer {
  name: "stu_Convolution1"
  type: "Convolution"
  bottom: "Data1"
  top: "stu_Convolution1"
  param {
    lr_mult: 1
    decay_mult: 1
  }
  param {
    lr_mult: 2
    decay_mult: 0
  }
  convolution_param {
    num_output: 8
    pad: 1
    kernel_size: 3
    stride: 1
    weight_filler {
      type: "msra"
    }
    bias_filler {
      type: "constant"
      value: 0
    }
  }
}
layer {
  name: "stu_BatchNorm1"
  type: "BatchNorm"
  bottom: "stu_Convolution1"
  top: "stu_Convolution1"
  param {
    lr_mult: 0
    decay_mult: 0
  }
  param {
    lr_mult: 0
    decay_mult: 0
  }
  param {
    lr_mult: 0
    decay_mult: 0
  }
}
layer {
  name: "stu_Scale1"
  type: "Scale"
  bottom: "stu_Convolution1"
  top: "stu_Convolution1"
  scale_param {
    bias_term: true
  }
}
layer {
  name: "stu_ReLU1"
  type: "ReLU"
  bottom: "stu_Convolution1"
  top: "stu_Convolution1"
}
layer {
  name: "stu_Convolution2"
  type: "Convolution"
  bottom: "stu_Convolution1"
  top: "stu_Convolution2"
  param {
    lr_mult: 1
    decay_mult: 1
  }
  param {
    lr_mult: 2
    decay_mult: 0
  }
  convolution_param {
    num_output: 8
    pad: 1
    kernel_size: 3
    stride: 1
    weight_filler {
      type: "msra"
    }
    bias_filler {
      type: "constant"
      value: 0
    }
  }
}
layer {
  name: "stu_BatchNorm2"
  type: "BatchNorm"
  bottom: "stu_Convolution2"
  top: "stu_Convolution2"
  param {
    lr_mult: 0
    decay_mult: 0
  }
  param {
    lr_mult: 0
    decay_mult: 0
  }
  param {
    lr_mult: 0
    decay_mult: 0
  }
}
layer {
  name: "stu_Scale2"
  type: "Scale"
  bottom: "stu_Convolution2"
  top: "stu_Convolution2"
  scale_param {
    bias_term: true
  }
}
layer {
  name: "stu_ReLU2"
  type: "ReLU"
  bottom: "stu_Convolution2"
  top: "stu_Convolution2"
}
layer {
  name: "stu_Convolution3"
  type: "Convolution"
  bottom: "stu_Convolution2"
  top: "stu_Convolution3"
  param {
    lr_mult: 1
    decay_mult: 1
  }
  param {
    lr_mult: 2
    decay_mult: 0
  }
  convolution_param {
    num_output: 8
    pad: 1
    kernel_size: 3
    stride: 1
    weight_filler {
      type: "msra"
    }
    bias_filler {
      type: "constant"
      value: 0
    }
  }
}
layer {
  name: "stu_BatchNorm3"
  type: "BatchNorm"
  bottom: "stu_Convolution3"
  top: "stu_Convolution3"
  param {
    lr_mult: 0
    decay_mult: 0
  }
  param {
    lr_mult: 0
    decay_mult: 0
  }
  param {
    lr_mult: 0
    decay_mult: 0
  }
}
layer {
  name: "stu_Scale3"
  type: "Scale"
  bottom: "stu_Convolution3"
  top: "stu_Convolution3"
  scale_param {
    bias_term: true
  }
}
layer {
  name: "stu_Eltwise1"
  type: "Eltwise"
  bottom: "stu_Convolution1"
  bottom: "stu_Convolution3"
  top: "stu_Eltwise1"
  eltwise_param {
    operation: SUM
  }
}
layer {
  name: "stu_ReLU3"
  type: "ReLU"
  bottom: "stu_Eltwise1"
  top: "stu_Eltwise1"
}
layer {
  name: "stu_Convolution4"
  type: "Convolution"
  bottom: "stu_Eltwise1"
  top: "stu_Convolution4"
  param {
    lr_mult: 1
    decay_mult: 1
  }
  param {
    lr_mult: 2
    decay_mult: 0
  }
  convolution_param {
    num_output: 8
    pad: 1
    kernel_size: 3
    stride: 1
    weight_filler {
      type: "msra"
    }
    bias_filler {
      type: "constant"
      value: 0
    }
  }
}
layer {
  name: "stu_BatchNorm4"
  type: "BatchNorm"
  bottom: "stu_Convolution4"
  top: "stu_Convolution4"
  param {
    lr_mult: 0
    decay_mult: 0
  }
  param {
    lr_mult: 0
    decay_mult: 0
  }
  param {
    lr_mult: 0
    decay_mult: 0
  }
}
layer {
  name: "stu_Scale4"
  type: "Scale"
  bottom: "stu_Convolution4"
  top: "stu_Convolution4"
  scale_param {
    bias_term: true
  }
}
layer {
  name: "stu_ReLU4"
  type: "ReLU"
  bottom: "stu_Convolution4"
  top: "stu_Convolution4"
}
layer {
  name: "stu_Convolution5"
  type: "Convolution"
  bottom: "stu_Convolution4"
  top: "stu_Convolution5"
  param {
    lr_mult: 1
    decay_mult: 1
  }
  param {
    lr_mult: 2
    decay_mult: 0
  }
  convolution_param {
    num_output: 8
    pad: 1
    kernel_size: 3
    stride: 1
    weight_filler {
      type: "msra"
    }
    bias_filler {
      type: "constant"
      value: 0
    }
  }
}
layer {
  name: "stu_BatchNorm5"
  type: "BatchNorm"
  bottom: "stu_Convolution5"
  top: "stu_Convolution5"
  param {
    lr_mult: 0
    decay_mult: 0
  }
  param {
    lr_mult: 0
    decay_mult: 0
  }
  param {
    lr_mult: 0
    decay_mult: 0
  }
}
layer {
  name: "stu_Scale5"
  type: "Scale"
  bottom: "stu_Convolution5"
  top: "stu_Convolution5"
  scale_param {
    bias_term: true
  }
}
layer {
  name: "stu_Eltwise2"
  type: "Eltwise"
  bottom: "stu_Eltwise1"
  bottom: "stu_Convolution5"
  top: "stu_Eltwise2"
  eltwise_param {
    operation: SUM
  }
}
layer {
  name: "stu_ReLU5"
  type: "ReLU"
  bottom: "stu_Eltwise2"
  top: "stu_Eltwise2"
}
layer {
  name: "stu_Convolution6"
  type: "Convolution"
  bottom: "stu_Eltwise2"
  top: "stu_Convolution6"
  param {
    lr_mult: 1
    decay_mult: 1
  }
  param {
    lr_mult: 2
    decay_mult: 0
  }
  convolution_param {
    num_output: 8
    pad: 1
    kernel_size: 3
    stride: 1
    weight_filler {
      type: "msra"
    }
    bias_filler {
      type: "constant"
      value: 0
    }
  }
}
layer {
  name: "stu_BatchNorm6"
  type: "BatchNorm"
  bottom: "stu_Convolution6"
  top: "stu_Convolution6"
  param {
    lr_mult: 0
    decay_mult: 0
  }
  param {
    lr_mult: 0
    decay_mult: 0
  }
  param {
    lr_mult: 0
    decay_mult: 0
  }
}
layer {
  name: "stu_Scale6"
  type: "Scale"
  bottom: "stu_Convolution6"
  top: "stu_Convolution6"
  scale_param {
    bias_term: true
  }
}
layer {
  name: "stu_ReLU6"
  type: "ReLU"
  bottom: "stu_Convolution6"
  top: "stu_Convolution6"
}
layer {
  name: "stu_Convolution7"
  type: "Convolution"
  bottom: "stu_Convolution6"
  top: "stu_Convolution7"
  param {
    lr_mult: 1
    decay_mult: 1
  }
  param {
    lr_mult: 2
    decay_mult: 0
  }
  convolution_param {
    num_output: 8
    pad: 1
    kernel_size: 3
    stride: 1
    weight_filler {
      type: "msra"
    }
    bias_filler {
      type: "constant"
      value: 0
    }
  }
}
layer {
  name: "stu_BatchNorm7"
  type: "BatchNorm"
  bottom: "stu_Convolution7"
  top: "stu_Convolution7"
  param {
    lr_mult: 0
    decay_mult: 0
  }
  param {
    lr_mult: 0
    decay_mult: 0
  }
  param {
    lr_mult: 0
    decay_mult: 0
  }
}
layer {
  name: "stu_Scale7"
  type: "Scale"
  bottom: "stu_Convolution7"
  top: "stu_Convolution7"
  scale_param {
    bias_term: true
  }
}
layer {
  name: "stu_Eltwise3"
  type: "Eltwise"
  bottom: "stu_Eltwise2"
  bottom: "stu_Convolution7"
  top: "stu_Eltwise3"
  eltwise_param {
    operation: SUM
  }
}
layer {
  name: "stu_ReLU7"
  type: "ReLU"
  bottom: "stu_Eltwise3"
  top: "stu_Eltwise3"
}
layer {
  name: "stu_Convolution8"
  type: "Convolution"
  bottom: "stu_Eltwise3"
  top: "stu_Convolution8"
  param {
    lr_mult: 1
    decay_mult: 1
  }
  param {
    lr_mult: 2
    decay_mult: 0
  }
  convolution_param {
    num_output: 16
    pad: 0
    kernel_size: 1
    stride: 2
    weight_filler {
      type: "msra"
    }
    bias_filler {
      type: "constant"
      value: 0
    }
  }
}
layer {
  name: "stu_BatchNorm8"
  type: "BatchNorm"
  bottom: "stu_Convolution8"
  top: "stu_Convolution8"
  param {
    lr_mult: 0
    decay_mult: 0
  }
  param {
    lr_mult: 0
    decay_mult: 0
  }
  param {
    lr_mult: 0
    decay_mult: 0
  }
}
layer {
  name: "stu_Scale8"
  type: "Scale"
  bottom: "stu_Convolution8"
  top: "stu_Convolution8"
  scale_param {
    bias_term: true
  }
}
layer {
  name: "stu_Convolution9"
  type: "Convolution"
  bottom: "stu_Eltwise3"
  top: "stu_Convolution9"
  param {
    lr_mult: 1
    decay_mult: 1
  }
  param {
    lr_mult: 2
    decay_mult: 0
  }
  convolution_param {
    num_output: 16
    pad: 1
    kernel_size: 3
    stride: 2
    weight_filler {
      type: "msra"
    }
    bias_filler {
      type: "constant"
      value: 0
    }
  }
}
layer {
  name: "stu_BatchNorm9"
  type: "BatchNorm"
  bottom: "stu_Convolution9"
  top: "stu_Convolution9"
  param {
    lr_mult: 0
    decay_mult: 0
  }
  param {
    lr_mult: 0
    decay_mult: 0
  }
  param {
    lr_mult: 0
    decay_mult: 0
  }
}
layer {
  name: "stu_Scale9"
  type: "Scale"
  bottom: "stu_Convolution9"
  top: "stu_Convolution9"
  scale_param {
    bias_term: true
  }
}
layer {
  name: "stu_ReLU8"
  type: "ReLU"
  bottom: "stu_Convolution9"
  top: "stu_Convolution9"
}
layer {
  name: "stu_Convolution10"
  type: "Convolution"
  bottom: "stu_Convolution9"
  top: "stu_Convolution10"
  param {
    lr_mult: 1
    decay_mult: 1
  }
  param {
    lr_mult: 2
    decay_mult: 0
  }
  convolution_param {
    num_output: 16
    pad: 1
    kernel_size: 3
    stride: 1
    weight_filler {
      type: "msra"
    }
    bias_filler {
      type: "constant"
      value: 0
    }
  }
}
layer {
  name: "stu_BatchNorm10"
  type: "BatchNorm"
  bottom: "stu_Convolution10"
  top: "stu_Convolution10"
  param {
    lr_mult: 0
    decay_mult: 0
  }
  param {
    lr_mult: 0
    decay_mult: 0
  }
  param {
    lr_mult: 0
    decay_mult: 0
  }
}
layer {
  name: "stu_Scale10"
  type: "Scale"
  bottom: "stu_Convolution10"
  top: "stu_Convolution10"
  scale_param {
    bias_term: true
  }
}
layer {
  name: "stu_Eltwise4"
  type: "Eltwise"
  bottom: "stu_Convolution8"
  bottom: "stu_Convolution10"
  top: "stu_Eltwise4"
  eltwise_param {
    operation: SUM
  }
}
layer {
  name: "stu_ReLU9"
  type: "ReLU"
  bottom: "stu_Eltwise4"
  top: "stu_Eltwise4"
}
layer {
  name: "stu_Convolution11"
  type: "Convolution"
  bottom: "stu_Eltwise4"
  top: "stu_Convolution11"
  param {
    lr_mult: 1
    decay_mult: 1
  }
  param {
    lr_mult: 2
    decay_mult: 0
  }
  convolution_param {
    num_output: 16
    pad: 1
    kernel_size: 3
    stride: 1
    weight_filler {
      type: "msra"
    }
    bias_filler {
      type: "constant"
      value: 0
    }
  }
}
layer {
  name: "stu_BatchNorm11"
  type: "BatchNorm"
  bottom: "stu_Convolution11"
  top: "stu_Convolution11"
  param {
    lr_mult: 0
    decay_mult: 0
  }
  param {
    lr_mult: 0
    decay_mult: 0
  }
  param {
    lr_mult: 0
    decay_mult: 0
  }
}
layer {
  name: "stu_Scale11"
  type: "Scale"
  bottom: "stu_Convolution11"
  top: "stu_Convolution11"
  scale_param {
    bias_term: true
  }
}
layer {
  name: "stu_ReLU10"
  type: "ReLU"
  bottom: "stu_Convolution11"
  top: "stu_Convolution11"
}
layer {
  name: "stu_Convolution12"
  type: "Convolution"
  bottom: "stu_Convolution11"
  top: "stu_Convolution12"
  param {
    lr_mult: 1
    decay_mult: 1
  }
  param {
    lr_mult: 2
    decay_mult: 0
  }
  convolution_param {
    num_output: 16
    pad: 1
    kernel_size: 3
    stride: 1
    weight_filler {
      type: "msra"
    }
    bias_filler {
      type: "constant"
      value: 0
    }
  }
}
layer {
  name: "stu_BatchNorm12"
  type: "BatchNorm"
  bottom: "stu_Convolution12"
  top: "stu_Convolution12"
  param {
    lr_mult: 0
    decay_mult: 0
  }
  param {
    lr_mult: 0
    decay_mult: 0
  }
  param {
    lr_mult: 0
    decay_mult: 0
  }
}
layer {
  name: "stu_Scale12"
  type: "Scale"
  bottom: "stu_Convolution12"
  top: "stu_Convolution12"
  scale_param {
    bias_term: true
  }
}
layer {
  name: "stu_Eltwise5"
  type: "Eltwise"
  bottom: "stu_Eltwise4"
  bottom: "stu_Convolution12"
  top: "stu_Eltwise5"
  eltwise_param {
    operation: SUM
  }
}
layer {
  name: "stu_ReLU11"
  type: "ReLU"
  bottom: "stu_Eltwise5"
  top: "stu_Eltwise5"
}
layer {
  name: "stu_Convolution13"
  type: "Convolution"
  bottom: "stu_Eltwise5"
  top: "stu_Convolution13"
  param {
    lr_mult: 1
    decay_mult: 1
  }
  param {
    lr_mult: 2
    decay_mult: 0
  }
  convolution_param {
    num_output: 16
    pad: 1
    kernel_size: 3
    stride: 1
    weight_filler {
      type: "msra"
    }
    bias_filler {
      type: "constant"
      value: 0
    }
  }
}
layer {
  name: "stu_BatchNorm13"
  type: "BatchNorm"
  bottom: "stu_Convolution13"
  top: "stu_Convolution13"
  param {
    lr_mult: 0
    decay_mult: 0
  }
  param {
    lr_mult: 0
    decay_mult: 0
  }
  param {
    lr_mult: 0
    decay_mult: 0
  }
}
layer {
  name: "stu_Scale13"
  type: "Scale"
  bottom: "stu_Convolution13"
  top: "stu_Convolution13"
  scale_param {
    bias_term: true
  }
}
layer {
  name: "stu_ReLU12"
  type: "ReLU"
  bottom: "stu_Convolution13"
  top: "stu_Convolution13"
}
layer {
  name: "stu_Convolution14"
  type: "Convolution"
  bottom: "stu_Convolution13"
  top: "stu_Convolution14"
  param {
    lr_mult: 1
    decay_mult: 1
  }
  param {
    lr_mult: 2
    decay_mult: 0
  }
  convolution_param {
    num_output: 16
    pad: 1
    kernel_size: 3
    stride: 1
    weight_filler {
      type: "msra"
    }
    bias_filler {
      type: "constant"
      value: 0
    }
  }
}
layer {
  name: "stu_BatchNorm14"
  type: "BatchNorm"
  bottom: "stu_Convolution14"
  top: "stu_Convolution14"
  param {
    lr_mult: 0
    decay_mult: 0
  }
  param {
    lr_mult: 0
    decay_mult: 0
  }
  param {
    lr_mult: 0
    decay_mult: 0
  }
}
layer {
  name: "stu_Scale14"
  type: "Scale"
  bottom: "stu_Convolution14"
  top: "stu_Convolution14"
  scale_param {
    bias_term: true
  }
}
layer {
  name: "stu_Eltwise6"
  type: "Eltwise"
  bottom: "stu_Eltwise5"
  bottom: "stu_Convolution14"
  top: "stu_Eltwise6"
  eltwise_param {
    operation: SUM
  }
}
layer {
  name: "stu_ReLU13"
  type: "ReLU"
  bottom: "stu_Eltwise6"
  top: "stu_Eltwise6"
}
layer {
  name: "stu_Convolution15"
  type: "Convolution"
  bottom: "stu_Eltwise6"
  top: "stu_Convolution15"
  param {
    lr_mult: 1
    decay_mult: 1
  }
  param {
    lr_mult: 2
    decay_mult: 0
  }
  convolution_param {
    num_output: 32
    pad: 0
    kernel_size: 1
    stride: 2
    weight_filler {
      type: "msra"
    }
    bias_filler {
      type: "constant"
      value: 0
    }
  }
}
layer {
  name: "stu_BatchNorm15"
  type: "BatchNorm"
  bottom: "stu_Convolution15"
  top: "stu_Convolution15"
  param {
    lr_mult: 0
    decay_mult: 0
  }
  param {
    lr_mult: 0
    decay_mult: 0
  }
  param {
    lr_mult: 0
    decay_mult: 0
  }
}
layer {
  name: "stu_Scale15"
  type: "Scale"
  bottom: "stu_Convolution15"
  top: "stu_Convolution15"
  scale_param {
    bias_term: true
  }
}
layer {
  name: "stu_Convolution16"
  type: "Convolution"
  bottom: "stu_Eltwise6"
  top: "stu_Convolution16"
  param {
    lr_mult: 1
    decay_mult: 1
  }
  param {
    lr_mult: 2
    decay_mult: 0
  }
  convolution_param {
    num_output: 32
    pad: 1
    kernel_size: 3
    stride: 2
    weight_filler {
      type: "msra"
    }
    bias_filler {
      type: "constant"
      value: 0
    }
  }
}
layer {
  name: "stu_BatchNorm16"
  type: "BatchNorm"
  bottom: "stu_Convolution16"
  top: "stu_Convolution16"
  param {
    lr_mult: 0
    decay_mult: 0
  }
  param {
    lr_mult: 0
    decay_mult: 0
  }
  param {
    lr_mult: 0
    decay_mult: 0
  }
}
layer {
  name: "stu_Scale16"
  type: "Scale"
  bottom: "stu_Convolution16"
  top: "stu_Convolution16"
  scale_param {
    bias_term: true
  }
}
layer {
  name: "stu_ReLU14"
  type: "ReLU"
  bottom: "stu_Convolution16"
  top: "stu_Convolution16"
}
layer {
  name: "stu_Convolution17"
  type: "Convolution"
  bottom: "stu_Convolution16"
  top: "stu_Convolution17"
  param {
    lr_mult: 1
    decay_mult: 1
  }
  param {
    lr_mult: 2
    decay_mult: 0
  }
  convolution_param {
    num_output: 32
    pad: 1
    kernel_size: 3
    stride: 1
    weight_filler {
      type: "msra"
    }
    bias_filler {
      type: "constant"
      value: 0
    }
  }
}
layer {
  name: "stu_BatchNorm17"
  type: "BatchNorm"
  bottom: "stu_Convolution17"
  top: "stu_Convolution17"
  param {
    lr_mult: 0
    decay_mult: 0
  }
  param {
    lr_mult: 0
    decay_mult: 0
  }
  param {
    lr_mult: 0
    decay_mult: 0
  }
}
layer {
  name: "stu_Scale17"
  type: "Scale"
  bottom: "stu_Convolution17"
  top: "stu_Convolution17"
  scale_param {
    bias_term: true
  }
}
layer {
  name: "stu_Eltwise7"
  type: "Eltwise"
  bottom: "stu_Convolution15"
  bottom: "stu_Convolution17"
  top: "stu_Eltwise7"
  eltwise_param {
    operation: SUM
  }
}
layer {
  name: "stu_ReLU15"
  type: "ReLU"
  bottom: "stu_Eltwise7"
  top: "stu_Eltwise7"
}
layer {
  name: "stu_Convolution18"
  type: "Convolution"
  bottom: "stu_Eltwise7"
  top: "stu_Convolution18"
  param {
    lr_mult: 1
    decay_mult: 1
  }
  param {
    lr_mult: 2
    decay_mult: 0
  }
  convolution_param {
    num_output: 32
    pad: 1
    kernel_size: 3
    stride: 1
    weight_filler {
      type: "msra"
    }
    bias_filler {
      type: "constant"
      value: 0
    }
  }
}
layer {
  name: "stu_BatchNorm18"
  type: "BatchNorm"
  bottom: "stu_Convolution18"
  top: "stu_Convolution18"
  param {
    lr_mult: 0
    decay_mult: 0
  }
  param {
    lr_mult: 0
    decay_mult: 0
  }
  param {
    lr_mult: 0
    decay_mult: 0
  }
}
layer {
  name: "stu_Scale18"
  type: "Scale"
  bottom: "stu_Convolution18"
  top: "stu_Convolution18"
  scale_param {
    bias_term: true
  }
}
layer {
  name: "stu_ReLU16"
  type: "ReLU"
  bottom: "stu_Convolution18"
  top: "stu_Convolution18"
}
layer {
  name: "stu_Convolution19"
  type: "Convolution"
  bottom: "stu_Convolution18"
  top: "stu_Convolution19"
  param {
    lr_mult: 1
    decay_mult: 1
  }
  param {
    lr_mult: 2
    decay_mult: 0
  }
  convolution_param {
    num_output: 32
    pad: 1
    kernel_size: 3
    stride: 1
    weight_filler {
      type: "msra"
    }
    bias_filler {
      type: "constant"
      value: 0
    }
  }
}
layer {
  name: "stu_BatchNorm19"
  type: "BatchNorm"
  bottom: "stu_Convolution19"
  top: "stu_Convolution19"
  param {
    lr_mult: 0
    decay_mult: 0
  }
  param {
    lr_mult: 0
    decay_mult: 0
  }
  param {
    lr_mult: 0
    decay_mult: 0
  }
}
layer {
  name: "stu_Scale19"
  type: "Scale"
  bottom: "stu_Convolution19"
  top: "stu_Convolution19"
  scale_param {
    bias_term: true
  }
}
layer {
  name: "stu_Eltwise8"
  type: "Eltwise"
  bottom: "stu_Eltwise7"
  bottom: "stu_Convolution19"
  top: "stu_Eltwise8"
  eltwise_param {
    operation: SUM
  }
}
layer {
  name: "stu_ReLU17"
  type: "ReLU"
  bottom: "stu_Eltwise8"
  top: "stu_Eltwise8"
}
layer {
  name: "stu_Convolution20"
  type: "Convolution"
  bottom: "stu_Eltwise8"
  top: "stu_Convolution20"
  param {
    lr_mult: 1
    decay_mult: 1
  }
  param {
    lr_mult: 2
    decay_mult: 0
  }
  convolution_param {
    num_output: 32
    pad: 1
    kernel_size: 3
    stride: 1
    weight_filler {
      type: "msra"
    }
    bias_filler {
      type: "constant"
      value: 0
    }
  }
}
layer {
  name: "stu_BatchNorm20"
  type: "BatchNorm"
  bottom: "stu_Convolution20"
  top: "stu_Convolution20"
  param {
    lr_mult: 0
    decay_mult: 0
  }
  param {
    lr_mult: 0
    decay_mult: 0
  }
  param {
    lr_mult: 0
    decay_mult: 0
  }
}
layer {
  name: "stu_Scale20"
  type: "Scale"
  bottom: "stu_Convolution20"
  top: "stu_Convolution20"
  scale_param {
    bias_term: true
  }
}
layer {
  name: "stu_ReLU18"
  type: "ReLU"
  bottom: "stu_Convolution20"
  top: "stu_Convolution20"
}
layer {
  name: "stu_Convolution21"
  type: "Convolution"
  bottom: "stu_Convolution20"
  top: "stu_Convolution21"
  param {
    lr_mult: 1
    decay_mult: 1
  }
  param {
    lr_mult: 2
    decay_mult: 0
  }
  convolution_param {
    num_output: 32
    pad: 1
    kernel_size: 3
    stride: 1
    weight_filler {
      type: "msra"
    }
    bias_filler {
      type: "constant"
      value: 0
    }
  }
}
layer {
  name: "stu_BatchNorm21"
  type: "BatchNorm"
  bottom: "stu_Convolution21"
  top: "stu_Convolution21"
  param {
    lr_mult: 0
    decay_mult: 0
  }
  param {
    lr_mult: 0
    decay_mult: 0
  }
  param {
    lr_mult: 0
    decay_mult: 0
  }
}
layer {
  name: "stu_Scale21"
  type: "Scale"
  bottom: "stu_Convolution21"
  top: "stu_Convolution21"
  scale_param {
    bias_term: true
  }
}
layer {
  name: "stu_Eltwise9"
  type: "Eltwise"
  bottom: "stu_Eltwise8"
  bottom: "stu_Convolution21"
  top: "stu_Eltwise9"
  eltwise_param {
    operation: SUM
  }
}
layer {
  name: "stu_ReLU19"
  type: "ReLU"
  bottom: "stu_Eltwise9"
  top: "stu_Eltwise9"
}
layer {
  name: "stu_Pooling1"
  type: "Pooling"
  bottom: "stu_Eltwise9"
  top: "stu_Pooling1"
  pooling_param {
    pool: AVE
    global_pooling: true
  }
}
layer {
  name: "stu_bem"
  type: "BatchEuclideanMap"
  bottom: "stu_Pooling1"
  top: "stu_bem"
}
layer {
  name: "bem_loss"
  type: "EuclideanLoss"
  bottom: "bem"
  bottom: "stu_bem"
  top: "bem_loss"
  loss_weight: 0.005
}
layer {
  name: "stu_InnerProduct1"
  type: "InnerProduct"
  bottom: "stu_Pooling1"
  top: "stu_InnerProduct1"
  param {
    lr_mult: 1
    decay_mult: 1
  }
  param {
    lr_mult: 2
    decay_mult: 1
  }
  inner_product_param {
    num_output: 10
    weight_filler {
      type: "msra"
    }
    bias_filler {
      type: "constant"
      value: 0
    }
  }
}
layer {
  name: "stu_SoftmaxWithLoss1"
  type: "SoftmaxWithLoss"
  bottom: "stu_InnerProduct1"
  bottom: "Data2"
  top: "stu_SoftmaxWithLoss1"
}
layer {
  name: "stu_Accuracy1"
  type: "Accuracy"
  bottom: "stu_InnerProduct1"
  bottom: "Data2"
  top: "stu_Accuracy1"
  include {
    phase: TEST
  }
}