inception_resnet_v2_tiny_train.prototxt

layer {
    name: "data"
    type: "Data"
    top: "data"
    top: "label"
    transform_param {
        mirror: true        
        mean_value: 127.5
        mean_value: 127.5
        mean_value: 127.5
        scale: 0.0078125
        crop_size: 128
    }
    data_param {
        source: "/home/lqy/caffe/examples/face_recog/face_recog_vggface2_lmdb/"
        batch_size: 128
        backend: LMDB
    }
}
layer {
  name: "conv1_3x3_s2"
  type: "Convolution"
  bottom: "data"
  top: "conv1_3x3_s2"
  param {
    lr_mult: 1
    decay_mult: 1
  }
  param {
    lr_mult: 2
    decay_mult: 0
  }
  convolution_param {
    num_output: 32
    pad: 0
    kernel_size: 3
    stride: 2
    weight_filler {
      type: "xavier"
      std: 0.01
    }
    bias_filler {
      type: "constant"
      value: 0.2
    }
  }
}
layer {
  name: "conv1_3x3_s2_bn"
  type: "BatchNorm"
  bottom: "conv1_3x3_s2"
  top: "conv1_3x3_s2"
    param {
        lr_mult: 0.0
        decay_mult: 0.0
    }
    param {
        lr_mult: 0.0
        decay_mult: 0.0
    }
    param {
        lr_mult: 0.0
        decay_mult: 0.0
    }
	batch_norm_param {
        use_global_stats: false
        moving_average_fraction: 0.95
	}
}
layer {
  name: "conv1_3x3_s2_scale"
  type: "Scale"
  bottom: "conv1_3x3_s2"
  top: "conv1_3x3_s2"
  scale_param {
    bias_term: true
  }
}
layer {
  name: "conv1_3x3_relu"
  type: "PReLU"
  bottom: "conv1_3x3_s2"
  top: "conv1_3x3_s2"
}
layer {
  name: "conv2_3x3_s1"
  type: "Convolution"
  bottom: "conv1_3x3_s2"
  top: "conv2_3x3_s1"
  param {
    lr_mult: 1
    decay_mult: 1
  }
  param {
    lr_mult: 2
    decay_mult: 0
  }
  convolution_param {
    num_output: 32
    pad: 0
    kernel_size: 3
    stride: 1
    weight_filler {
      type: "xavier"
      std: 0.01
    }
    bias_filler {
      type: "constant"
      value: 0.2
    }
  }
}
layer {
  name: "conv2_3x3_s1_bn"
  type: "BatchNorm"
  bottom: "conv2_3x3_s1"
  top: "conv2_3x3_s1"
    param {
        lr_mult: 0.0
        decay_mult: 0.0
    }
    param {
        lr_mult: 0.0
        decay_mult: 0.0
    }
    param {
        lr_mult: 0.0
        decay_mult: 0.0
    }
	batch_norm_param {
        use_global_stats: false
        moving_average_fraction: 0.95
	}
}
layer {
  name: "conv2_3x3_s1_scale"
  type: "Scale"
  bottom: "conv2_3x3_s1"
  top: "conv2_3x3_s1"
  scale_param {
    bias_term: true
  }
}
layer {
  name: "conv2_3x3_relu"
  type: "PReLU"
  bottom: "conv2_3x3_s1"
  top: "conv2_3x3_s1"
}
layer {
  name: "conv3_3x3_s1"
  type: "Convolution"
  bottom: "conv2_3x3_s1"
  top: "conv3_3x3_s1"
  param {
    lr_mult: 1
    decay_mult: 1
  }
  param {
    lr_mult: 2
    decay_mult: 0
  }
  convolution_param {
    num_output: 64
    pad: 1
    kernel_size: 3
    stride: 1
    weight_filler {
      type: "xavier"
      std: 0.01
    }
    bias_filler {
      type: "constant"
      value: 0.2
    }
  }
}
layer {
  name: "conv3_3x3_s1_bn"
  type: "BatchNorm"
  bottom: "conv3_3x3_s1"
  top: "conv3_3x3_s1"
    param {
        lr_mult: 0.0
        decay_mult: 0.0
    }
    param {
        lr_mult: 0.0
        decay_mult: 0.0
    }
    param {
        lr_mult: 0.0
        decay_mult: 0.0
    }
	batch_norm_param {
        use_global_stats: false
        moving_average_fraction: 0.95
	}
}
layer {
  name: "conv3_3x3_s1_scale"
  type: "Scale"
  bottom: "conv3_3x3_s1"
  top: "conv3_3x3_s1"
  scale_param {
    bias_term: true
  }
}
layer {
  name: "conv3_3x3_relu"
  type: "PReLU"
  bottom: "conv3_3x3_s1"
  top: "conv3_3x3_s1"
}
layer {
  name: "pool1_3x3_s2"
  type: "Pooling"
  bottom: "conv3_3x3_s1"
  top: "pool1_3x3_s2"
  pooling_param {
    pool: MAX
    kernel_size: 3
    stride: 2
  }
}
layer {
  name: "conv4_3x3_reduce"
  type: "Convolution"
  bottom: "pool1_3x3_s2"
  top: "conv4_3x3_reduce"
  param {
    lr_mult: 1
    decay_mult: 1
  }
  param {
    lr_mult: 2
    decay_mult: 0
  }
  convolution_param {
    num_output: 80
    pad: 0
    kernel_size: 1
    stride: 1
    weight_filler {
      type: "xavier"
      std: 0.01
    }
    bias_filler {
      type: "constant"
      value: 0.2
    }
  }
}
layer {
  name: "conv4_3x3_reduce_bn"
  type: "BatchNorm"
  bottom: "conv4_3x3_reduce"
  top: "conv4_3x3_reduce"
    param {
        lr_mult: 0.0
        decay_mult: 0.0
    }
    param {
        lr_mult: 0.0
        decay_mult: 0.0
    }
    param {
        lr_mult: 0.0
        decay_mult: 0.0
    }
	batch_norm_param {
        use_global_stats: false
        moving_average_fraction: 0.95
	}
}
layer {
  name: "conv4_3x3_reduce_scale"
  type: "Scale"
  bottom: "conv4_3x3_reduce"
  top: "conv4_3x3_reduce"
  scale_param {
    bias_term: true
  }
}
layer {
  name: "conv4_3x3_reduce_relu"
  type: "PReLU"
  bottom: "conv4_3x3_reduce"
  top: "conv4_3x3_reduce"
}
layer {
  name: "conv4_3x3"
  type: "Convolution"
  bottom: "conv4_3x3_reduce"
  top: "conv4_3x3"
  param {
    lr_mult: 1
    decay_mult: 1
  }
  param {
    lr_mult: 2
    decay_mult: 0
  }
  convolution_param {
    num_output: 192
    pad: 0
    kernel_size: 3
    stride: 1
    weight_filler {
      type: "xavier"
      std: 0.01
    }
    bias_filler {
      type: "constant"
      value: 0.2
    }
  }
}
layer {
  name: "conv4_3x3_bn"
  type: "BatchNorm"
  bottom: "conv4_3x3"
  top: "conv4_3x3"
    param {
        lr_mult: 0.0
        decay_mult: 0.0
    }
    param {
        lr_mult: 0.0
        decay_mult: 0.0
    }
    param {
        lr_mult: 0.0
        decay_mult: 0.0
    }
	batch_norm_param {
        use_global_stats: false
        moving_average_fraction: 0.95
	}
}
layer {
  name: "conv4_3x3_scale"
  type: "Scale"
  bottom: "conv4_3x3"
  top: "conv4_3x3"
  scale_param {
    bias_term: true
  }
}
layer {
  name: "conv4_relu_3x3"
  type: "PReLU"
  bottom: "conv4_3x3"
  top: "conv4_3x3"
}
layer {
  name: "pool2_3x3_s2"
  type: "Pooling"
  bottom: "conv4_3x3"
  top: "pool2_3x3_s2"
  pooling_param {
    pool: MAX
    kernel_size: 3
    stride: 2
  }
}
layer {
  name: "conv5_1x1"
  type: "Convolution"
  bottom: "pool2_3x3_s2"
  top: "conv5_1x1"
  param {
    lr_mult: 1
    decay_mult: 1
  }
  param {
    lr_mult: 2
    decay_mult: 0
  }
  convolution_param {
    num_output: 96
    pad: 0
    kernel_size: 1
    stride: 1
    weight_filler {
      type: "xavier"
      std: 0.01
    }
    bias_filler {
      type: "constant"
      value: 0.2
    }
  }
}
layer {
  name: "conv5_1x1_bn"
  type: "BatchNorm"
  bottom: "conv5_1x1"
  top: "conv5_1x1"
    param {
        lr_mult: 0.0
        decay_mult: 0.0
    }
    param {
        lr_mult: 0.0
        decay_mult: 0.0
    }
    param {
        lr_mult: 0.0
        decay_mult: 0.0
    }
	batch_norm_param {
        use_global_stats: false
        moving_average_fraction: 0.95
	}
}
layer {
  name: "conv5_1x1_scale"
  type: "Scale"
  bottom: "conv5_1x1"
  top: "conv5_1x1"
  scale_param {
    bias_term: true
  }
}
layer {
  name: "conv5_1x1_relu"
  type: "PReLU"
  bottom: "conv5_1x1"
  top: "conv5_1x1"
}
layer {
  name: "conv5_5x5_reduce"
  type: "Convolution"
  bottom: "pool2_3x3_s2"
  top: "conv5_5x5_reduce"
  param {
    lr_mult: 1
    decay_mult: 1
  }
  param {
    lr_mult: 2
    decay_mult: 0
  }
  convolution_param {
    num_output: 48
    pad: 0
    kernel_size: 1
    stride: 1
    weight_filler {
      type: "xavier"
      std: 0.01
    }
    bias_filler {
      type: "constant"
      value: 0.2
    }
  }
}
layer {
  name: "conv5_5x5_reduce_bn"
  type: "BatchNorm"
  bottom: "conv5_5x5_reduce"
  top: "conv5_5x5_reduce"
    param {
        lr_mult: 0.0
        decay_mult: 0.0
    }
    param {
        lr_mult: 0.0
        decay_mult: 0.0
    }
    param {
        lr_mult: 0.0
        decay_mult: 0.0
    }
	batch_norm_param {
        use_global_stats: false
        moving_average_fraction: 0.95
	}
}
layer {
  name: "conv5_5x5_reduce_scale"
  type: "Scale"
  bottom: "conv5_5x5_reduce"
  top: "conv5_5x5_reduce"
  scale_param {
    bias_term: true
  }
}
layer {
  name: "conv5_5x5_reduce_relu"
  type: "PReLU"
  bottom: "conv5_5x5_reduce"
  top: "conv5_5x5_reduce"
}
layer {
  name: "conv5_5x5"
  type: "Convolution"
  bottom: "conv5_5x5_reduce"
  top: "conv5_5x5"
  param {
    lr_mult: 1
    decay_mult: 1
  }
  param {
    lr_mult: 2
    decay_mult: 0
  }
  convolution_param {
    num_output: 64
    pad: 2
    kernel_size: 5
    stride: 1
    weight_filler {
      type: "xavier"
      std: 0.01
    }
    bias_filler {
      type: "constant"
      value: 0.2
    }
  }
}
layer {
  name: "conv5_5x5_bn"
  type: "BatchNorm"
  bottom: "conv5_5x5"
  top: "conv5_5x5"
    param {
        lr_mult: 0.0
        decay_mult: 0.0
    }
    param {
        lr_mult: 0.0
        decay_mult: 0.0
    }
    param {
        lr_mult: 0.0
        decay_mult: 0.0
    }
	batch_norm_param {
        use_global_stats: false
        moving_average_fraction: 0.95
	}
}
layer {
  name: "conv5_5x5_scale"
  type: "Scale"
  bottom: "conv5_5x5"
  top: "conv5_5x5"
  scale_param {
    bias_term: true
  }
}
layer {
  name: "conv5_5x5_relu"
  type: "PReLU"
  bottom: "conv5_5x5"
  top: "conv5_5x5"
}
layer {
  name: "conv5_3x3_reduce"
  type: "Convolution"
  bottom: "pool2_3x3_s2"
  top: "conv5_3x3_reduce"
  param {
    lr_mult: 1
    decay_mult: 1
  }
  param {
    lr_mult: 2
    decay_mult: 0
  }
  convolution_param {
    num_output: 64
    pad: 0
    kernel_size: 1
    stride: 1
    weight_filler {
      type: "xavier"
      std: 0.01
    }
    bias_filler {
      type: "constant"
      value: 0.2
    }
  }
}
layer {
  name: "conv5_3x3_reduce_bn"
  type: "BatchNorm"
  bottom: "conv5_3x3_reduce"
  top: "conv5_3x3_reduce"
    param {
        lr_mult: 0.0
        decay_mult: 0.0
    }
    param {
        lr_mult: 0.0
        decay_mult: 0.0
    }
    param {
        lr_mult: 0.0
        decay_mult: 0.0
    }
	batch_norm_param {
        use_global_stats: false
        moving_average_fraction: 0.95
	}
}
layer {
  name: "conv5_3x3_reduce_scale"
  type: "Scale"
  bottom: "conv5_3x3_reduce"
  top: "conv5_3x3_reduce"
  scale_param {
    bias_term: true
  }
}
layer {
  name: "conv5_3x3_reduce_relu"
  type: "PReLU"
  bottom: "conv5_3x3_reduce"
  top: "conv5_3x3_reduce"
}
layer {
  name: "conv5_3x3"
  type: "Convolution"
  bottom: "conv5_3x3_reduce"
  top: "conv5_3x3"
  param {
    lr_mult: 1
    decay_mult: 1
  }
  param {
    lr_mult: 2
    decay_mult: 0
  }
  convolution_param {
    num_output: 96
    pad: 1
    kernel_size: 3
    stride: 1
    weight_filler {
      type: "xavier"
      std: 0.01
    }
    bias_filler {
      type: "constant"
      value: 0.2
    }
  }
}
layer {
  name: "conv5_3x3_bn"
  type: "BatchNorm"
  bottom: "conv5_3x3"
  top: "conv5_3x3"
    param {
        lr_mult: 0.0
        decay_mult: 0.0
    }
    param {
        lr_mult: 0.0
        decay_mult: 0.0
    }
    param {
        lr_mult: 0.0
        decay_mult: 0.0
    }
	batch_norm_param {
        use_global_stats: false
        moving_average_fraction: 0.95
	}
}
layer {
  name: "conv5_3x3_scale"
  type: "Scale"
  bottom: "conv5_3x3"
  top: "conv5_3x3"
  scale_param {
    bias_term: true
  }
}
layer {
  name: "conv5_3x3_relu"
  type: "PReLU"
  bottom: "conv5_3x3"
  top: "conv5_3x3"
}
layer {
  name: "conv5_3x3_2"
  type: "Convolution"
  bottom: "conv5_3x3"
  top: "conv5_3x3_2"
  param {
    lr_mult: 1
    decay_mult: 1
  }
  param {
    lr_mult: 2
    decay_mult: 0
  }
  convolution_param {
    num_output: 96
    pad: 1
    kernel_size: 3
    stride: 1
    weight_filler {
      type: "xavier"
      std: 0.01
    }
    bias_filler {
      type: "constant"
      value: 0.2
    }
  }
}
layer {
  name: "conv5_3x3_2_bn"
  type: "BatchNorm"
  bottom: "conv5_3x3_2"
  top: "conv5_3x3_2"
    param {
        lr_mult: 0.0
        decay_mult: 0.0
    }
    param {
        lr_mult: 0.0
        decay_mult: 0.0
    }
    param {
        lr_mult: 0.0
        decay_mult: 0.0
    }
	batch_norm_param {
        use_global_stats: false
        moving_average_fraction: 0.95
	}
}
layer {
  name: "conv5_3x3_2_scale"
  type: "Scale"
  bottom: "conv5_3x3_2"
  top: "conv5_3x3_2"
  scale_param {
    bias_term: true
  }
}
layer {
  name: "conv5_3x3_2_relu"
  type: "PReLU"
  bottom: "conv5_3x3_2"
  top: "conv5_3x3_2"
}
layer {
  name: "ave_pool"
  type: "Pooling"
  bottom: "pool2_3x3_s2"
  top: "ave_pool"
  pooling_param {
    pool: AVE
    kernel_size: 3
    stride: 1
    pad: 1
  }
}
layer {
  name: "conv5_1x1_ave"
  type: "Convolution"
  bottom: "ave_pool"
  top: "conv5_1x1_ave"
  param {
    lr_mult: 1
    decay_mult: 1
  }
  param {
    lr_mult: 2
    decay_mult: 0
  }
  convolution_param {
    num_output: 64
    pad: 0
    kernel_size: 1
    stride: 1
    weight_filler {
      type: "xavier"
      std: 0.01
    }
    bias_filler {
      type: "constant"
      value: 0.2
    }
  }
}
layer {
  name: "conv5_1x1_ave_bn"
  type: "BatchNorm"
  bottom: "conv5_1x1_ave"
  top: "conv5_1x1_ave"
    param {
        lr_mult: 0.0
        decay_mult: 0.0
    }
    param {
        lr_mult: 0.0
        decay_mult: 0.0
    }
    param {
        lr_mult: 0.0
        decay_mult: 0.0
    }
	batch_norm_param {
        use_global_stats: false
        moving_average_fraction: 0.95
	}
}
layer {
  name: "conv5_1x1_ave_scale"
  type: "Scale"
  bottom: "conv5_1x1_ave"
  top: "conv5_1x1_ave"
  scale_param {
    bias_term: true
  }
}
layer {
  name: "conv5_1x1_ave_relu"
  type: "PReLU"
  bottom: "conv5_1x1_ave"
  top: "conv5_1x1_ave"
}
layer {
  name: "stem_concat"
  type: "Concat"
  bottom: "conv5_1x1"
  bottom: "conv5_5x5"
  bottom: "conv5_3x3_2"
  bottom: "conv5_1x1_ave"
  top: "stem_concat"
}
layer {
  name: "inception_resnet_v2_a1_1x1"
  type: "Convolution"
  bottom: "stem_concat"
  top: "inception_resnet_v2_a1_1x1"
  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: 1
    weight_filler {
      type: "xavier"
      std: 0.01
    }
    bias_filler {
      type: "constant"
      value: 0.2
    }
  }
}
layer {
  name: "inception_resnet_v2_a1_1x1_bn"
  type: "BatchNorm"
  bottom: "inception_resnet_v2_a1_1x1"
  top: "inception_resnet_v2_a1_1x1"
    param {
        lr_mult: 0.0
        decay_mult: 0.0
    }
    param {
        lr_mult: 0.0
        decay_mult: 0.0
    }
    param {
        lr_mult: 0.0
        decay_mult: 0.0
    }
	batch_norm_param {
        use_global_stats: false
        moving_average_fraction: 0.95
	}
}
layer {
  name: "inception_resnet_v2_a1_1x1_scale"
  type: "Scale"
  bottom: "inception_resnet_v2_a1_1x1"
  top: "inception_resnet_v2_a1_1x1"
  scale_param {
    bias_term: true
  }
}
layer {
  name: "inception_resnet_v2_a1_1x1_relu"
  type: "PReLU"
  bottom: "inception_resnet_v2_a1_1x1"
  top: "inception_resnet_v2_a1_1x1"
}
layer {
  name: "inception_resnet_v2_a1_3x3_reduce"
  type: "Convolution"
  bottom: "stem_concat"
  top: "inception_resnet_v2_a1_3x3_reduce"
  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: 1
    weight_filler {
      type: "xavier"
      std: 0.01
    }
    bias_filler {
      type: "constant"
      value: 0.2
    }
  }
}
layer {
  name: "inception_resnet_v2_a1_3x3_reduce_bn"
  type: "BatchNorm"
  bottom: "inception_resnet_v2_a1_3x3_reduce"
  top: "inception_resnet_v2_a1_3x3_reduce"
    param {
        lr_mult: 0.0
        decay_mult: 0.0
    }
    param {
        lr_mult: 0.0
        decay_mult: 0.0
    }
    param {
        lr_mult: 0.0
        decay_mult: 0.0
    }
	batch_norm_param {
        use_global_stats: false
        moving_average_fraction: 0.95
	}
}
layer {
  name: "inception_resnet_v2_a1_3x3_reduce_scale"
  type: "Scale"
  bottom: "inception_resnet_v2_a1_3x3_reduce"
  top: "inception_resnet_v2_a1_3x3_reduce"
  scale_param {
    bias_term: true
  }
}
layer {
  name: "inception_resnet_v2_a1_3x3_reduce_relu"
  type: "PReLU"
  bottom: "inception_resnet_v2_a1_3x3_reduce"
  top: "inception_resnet_v2_a1_3x3_reduce"
}
layer {
  name: "inception_resnet_v2_a1_3x3"
  type: "Convolution"
  bottom: "inception_resnet_v2_a1_3x3_reduce"
  top: "inception_resnet_v2_a1_3x3"
  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: "xavier"
      std: 0.01
    }
    bias_filler {
      type: "constant"
      value: 0.2
    }
  }
}
layer {
  name: "inception_resnet_v2_a1_3x3_bn"
  type: "BatchNorm"
  bottom: "inception_resnet_v2_a1_3x3"
  top: "inception_resnet_v2_a1_3x3"
    param {
        lr_mult: 0.0
        decay_mult: 0.0
    }
    param {
        lr_mult: 0.0
        decay_mult: 0.0
    }
    param {
        lr_mult: 0.0
        decay_mult: 0.0
    }
	batch_norm_param {
        use_global_stats: false
        moving_average_fraction: 0.95
	}
}
layer {
  name: "inception_resnet_v2_a1_3x3_scale"
  type: "Scale"
  bottom: "inception_resnet_v2_a1_3x3"
  top: "inception_resnet_v2_a1_3x3"
  scale_param {
    bias_term: true
  }
}
layer {
  name: "inception_resnet_v2_a1_3x3_relu"
  type: "PReLU"
  bottom: "inception_resnet_v2_a1_3x3"
  top: "inception_resnet_v2_a1_3x3"
}
layer {
  name: "inception_resnet_v2_a1_3x3_2_reduce"
  type: "Convolution"
  bottom: "stem_concat"
  top: "inception_resnet_v2_a1_3x3_2_reduce"
  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: 1
    weight_filler {
      type: "xavier"
      std: 0.01
    }
    bias_filler {
      type: "constant"
      value: 0.2
    }
  }
}
layer {
  name: "inception_resnet_v2_a1_3x3_2_reduce_bn"
  type: "BatchNorm"
  bottom: "inception_resnet_v2_a1_3x3_2_reduce"
  top: "inception_resnet_v2_a1_3x3_2_reduce"
    param {
        lr_mult: 0.0
        decay_mult: 0.0
    }
    param {
        lr_mult: 0.0
        decay_mult: 0.0
    }
    param {
        lr_mult: 0.0
        decay_mult: 0.0
    }
	batch_norm_param {
        use_global_stats: false
        moving_average_fraction: 0.95
	}
}
layer {
  name: "inception_resnet_v2_a1_3x3_2_reduce_scale"
  type: "Scale"
  bottom: "inception_resnet_v2_a1_3x3_2_reduce"
  top: "inception_resnet_v2_a1_3x3_2_reduce"
  scale_param {
    bias_term: true
  }
}
layer {
  name: "inception_resnet_v2_a1_3x3_2_reduce_relu"
  type: "PReLU"
  bottom: "inception_resnet_v2_a1_3x3_2_reduce"
  top: "inception_resnet_v2_a1_3x3_2_reduce"
}
layer {
  name: "inception_resnet_v2_a1_3x3_2"
  type: "Convolution"
  bottom: "inception_resnet_v2_a1_3x3_2_reduce"
  top: "inception_resnet_v2_a1_3x3_2"
  param {
    lr_mult: 1
    decay_mult: 1
  }
  param {
    lr_mult: 2
    decay_mult: 0
  }
  convolution_param {
    num_output: 48
    pad: 1
    kernel_size: 3
    stride: 1
    weight_filler {
      type: "xavier"
      std: 0.01
    }
    bias_filler {
      type: "constant"
      value: 0.2
    }
  }
}
layer {
  name: "inception_resnet_v2_a1_3x3_2_bn"
  type: "BatchNorm"
  bottom: "inception_resnet_v2_a1_3x3_2"
  top: "inception_resnet_v2_a1_3x3_2"
    param {
        lr_mult: 0.0
        decay_mult: 0.0
    }
    param {
        lr_mult: 0.0
        decay_mult: 0.0
    }
    param {
        lr_mult: 0.0
        decay_mult: 0.0
    }
	batch_norm_param {
        use_global_stats: false
        moving_average_fraction: 0.95
	}
}
layer {
  name: "inception_resnet_v2_a1_3x3_2_scale"
  type: "Scale"
  bottom: "inception_resnet_v2_a1_3x3_2"
  top: "inception_resnet_v2_a1_3x3_2"
  scale_param {
    bias_term: true
  }
}
layer {
  name: "inception_resnet_v2_a1_3x3_2_relu"
  type: "PReLU"
  bottom: "inception_resnet_v2_a1_3x3_2"
  top: "inception_resnet_v2_a1_3x3_2"
}
layer {
  name: "inception_resnet_v2_a1_3x3_3"
  type: "Convolution"
  bottom: "inception_resnet_v2_a1_3x3_2"
  top: "inception_resnet_v2_a1_3x3_3"
  param {
    lr_mult: 1
    decay_mult: 1
  }
  param {
    lr_mult: 2
    decay_mult: 0
  }
  convolution_param {
    num_output: 64
    pad: 1
    kernel_size: 3
    stride: 1
    weight_filler {
      type: "xavier"
      std: 0.01
    }
    bias_filler {
      type: "constant"
      value: 0.2
    }
  }
}
layer {
  name: "inception_resnet_v2_a1_3x3_3_bn"
  type: "BatchNorm"
  bottom: "inception_resnet_v2_a1_3x3_3"
  top: "inception_resnet_v2_a1_3x3_3"
    param {
        lr_mult: 0.0
        decay_mult: 0.0
    }
    param {
        lr_mult: 0.0
        decay_mult: 0.0
    }
    param {
        lr_mult: 0.0
        decay_mult: 0.0
    }
	batch_norm_param {
        use_global_stats: false
        moving_average_fraction: 0.95
	}
}
layer {
  name: "inception_resnet_v2_a1_3x3_3_scale"
  type: "Scale"
  bottom: "inception_resnet_v2_a1_3x3_3"
  top: "inception_resnet_v2_a1_3x3_3"
  scale_param {
    bias_term: true
  }
}
layer {
  name: "inception_resnet_v2_a1_3x3_3_relu"
  type: "PReLU"
  bottom: "inception_resnet_v2_a1_3x3_3"
  top: "inception_resnet_v2_a1_3x3_3"
}
layer {
  name: "inception_resnet_v2_a1_concat"
  type: "Concat"
  bottom: "inception_resnet_v2_a1_1x1"
  bottom: "inception_resnet_v2_a1_3x3"
  bottom: "inception_resnet_v2_a1_3x3_3"
  top: "inception_resnet_v2_a1_concat"
}
layer {
  name: "inception_resnet_v2_a1_up"
  type: "Convolution"
  bottom: "inception_resnet_v2_a1_concat"
  top: "inception_resnet_v2_a1_up"
  param {
    lr_mult: 1
    decay_mult: 1
  }
  param {
    lr_mult: 2
    decay_mult: 0
  }
  convolution_param {
    num_output: 320
    pad: 0
    kernel_size: 1
    stride: 1
    weight_filler {
      type: "xavier"
      std: 0.01
    }
    bias_filler {
      type: "constant"
      value: 0.2
    }
  }
}
layer {
  name: "inception_resnet_v2_a1_up_bn"
  type: "BatchNorm"
  bottom: "inception_resnet_v2_a1_up"
  top: "inception_resnet_v2_a1_up"
    param {
        lr_mult: 0.0
        decay_mult: 0.0
    }
    param {
        lr_mult: 0.0
        decay_mult: 0.0
    }
    param {
        lr_mult: 0.0
        decay_mult: 0.0
    }
	batch_norm_param {
        use_global_stats: false
        moving_average_fraction: 0.95
	}
}
layer {
  name: "inception_resnet_v2_a1_up_scale"
  type: "Scale"
  bottom: "inception_resnet_v2_a1_up"
  top: "inception_resnet_v2_a1_up"
  scale_param {
    bias_term: true
  }
}
layer {
  name: "inception_resnet_v2_a1_residual_eltwise"
  type: "Eltwise"
  bottom: "stem_concat"
  bottom: "inception_resnet_v2_a1_up"
  top: "inception_resnet_v2_a1_residual_eltwise"
  eltwise_param {
    operation: SUM
  }
}
layer {
  name: "inception_resnet_v2_a1_residual_eltwise_relu"
  type: "PReLU"
  bottom: "inception_resnet_v2_a1_residual_eltwise"
  top: "inception_resnet_v2_a1_residual_eltwise"
}
layer {
  name: "inception_resnet_v2_a2_1x1"
  type: "Convolution"
  bottom: "inception_resnet_v2_a1_residual_eltwise"
  top: "inception_resnet_v2_a2_1x1"
  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: 1
    weight_filler {
      type: "xavier"
      std: 0.01
    }
    bias_filler {
      type: "constant"
      value: 0.2
    }
  }
}
layer {
  name: "inception_resnet_v2_a2_1x1_bn"
  type: "BatchNorm"
  bottom: "inception_resnet_v2_a2_1x1"
  top: "inception_resnet_v2_a2_1x1"
    param {
        lr_mult: 0.0
        decay_mult: 0.0
    }
    param {
        lr_mult: 0.0
        decay_mult: 0.0
    }
    param {
        lr_mult: 0.0
        decay_mult: 0.0
    }
	batch_norm_param {
        use_global_stats: false
        moving_average_fraction: 0.95
	}
}
layer {
  name: "inception_resnet_v2_a2_1x1_scale"
  type: "Scale"
  bottom: "inception_resnet_v2_a2_1x1"
  top: "inception_resnet_v2_a2_1x1"
  scale_param {
    bias_term: true
  }
}
layer {
  name: "inception_resnet_v2_a2_1x1_relu"
  type: "PReLU"
  bottom: "inception_resnet_v2_a2_1x1"
  top: "inception_resnet_v2_a2_1x1"
}
layer {
  name: "inception_resnet_v2_a2_3x3_reduce"
  type: "Convolution"
  bottom: "inception_resnet_v2_a1_residual_eltwise"
  top: "inception_resnet_v2_a2_3x3_reduce"
  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: 1
    weight_filler {
      type: "xavier"
      std: 0.01
    }
    bias_filler {
      type: "constant"
      value: 0.2
    }
  }
}
layer {
  name: "inception_resnet_v2_a2_3x3_reduce_bn"
  type: "BatchNorm"
  bottom: "inception_resnet_v2_a2_3x3_reduce"
  top: "inception_resnet_v2_a2_3x3_reduce"
    param {
        lr_mult: 0.0
        decay_mult: 0.0
    }
    param {
        lr_mult: 0.0
        decay_mult: 0.0
    }
    param {
        lr_mult: 0.0
        decay_mult: 0.0
    }
	batch_norm_param {
        use_global_stats: false
        moving_average_fraction: 0.95
	}
}
layer {
  name: "inception_resnet_v2_a2_3x3_reduce_scale"
  type: "Scale"
  bottom: "inception_resnet_v2_a2_3x3_reduce"
  top: "inception_resnet_v2_a2_3x3_reduce"
  scale_param {
    bias_term: true
  }
}
layer {
  name: "inception_resnet_v2_a2_3x3_reduce_relu"
  type: "PReLU"
  bottom: "inception_resnet_v2_a2_3x3_reduce"
  top: "inception_resnet_v2_a2_3x3_reduce"
}
layer {
  name: "inception_resnet_v2_a2_3x3"
  type: "Convolution"
  bottom: "inception_resnet_v2_a2_3x3_reduce"
  top: "inception_resnet_v2_a2_3x3"
  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: "xavier"
      std: 0.01
    }
    bias_filler {
      type: "constant"
      value: 0.2
    }
  }
}
layer {
  name: "inception_resnet_v2_a2_3x3_bn"
  type: "BatchNorm"
  bottom: "inception_resnet_v2_a2_3x3"
  top: "inception_resnet_v2_a2_3x3"
    param {
        lr_mult: 0.0
        decay_mult: 0.0
    }
    param {
        lr_mult: 0.0
        decay_mult: 0.0
    }
    param {
        lr_mult: 0.0
        decay_mult: 0.0
    }
	batch_norm_param {
        use_global_stats: false
        moving_average_fraction: 0.95
	}
}
layer {
  name: "inception_resnet_v2_a2_3x3_scale"
  type: "Scale"
  bottom: "inception_resnet_v2_a2_3x3"
  top: "inception_resnet_v2_a2_3x3"
  scale_param {
    bias_term: true
  }
}
layer {
  name: "inception_resnet_v2_a2_3x3_relu"
  type: "PReLU"
  bottom: "inception_resnet_v2_a2_3x3"
  top: "inception_resnet_v2_a2_3x3"
}
layer {
  name: "inception_resnet_v2_a2_3x3_2_reduce"
  type: "Convolution"
  bottom: "inception_resnet_v2_a1_residual_eltwise"
  top: "inception_resnet_v2_a2_3x3_2_reduce"
  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: 1
    weight_filler {
      type: "xavier"
      std: 0.01
    }
    bias_filler {
      type: "constant"
      value: 0.2
    }
  }
}
layer {
  name: "inception_resnet_v2_a2_3x3_2_reduce_bn"
  type: "BatchNorm"
  bottom: "inception_resnet_v2_a2_3x3_2_reduce"
  top: "inception_resnet_v2_a2_3x3_2_reduce"
    param {
        lr_mult: 0.0
        decay_mult: 0.0
    }
    param {
        lr_mult: 0.0
        decay_mult: 0.0
    }
    param {
        lr_mult: 0.0
        decay_mult: 0.0
    }
	batch_norm_param {
        use_global_stats: false
        moving_average_fraction: 0.95
	}
}
layer {
  name: "inception_resnet_v2_a2_3x3_2_reduce_scale"
  type: "Scale"
  bottom: "inception_resnet_v2_a2_3x3_2_reduce"
  top: "inception_resnet_v2_a2_3x3_2_reduce"
  scale_param {
    bias_term: true
  }
}
layer {
  name: "inception_resnet_v2_a2_3x3_2_reduce_relu"
  type: "PReLU"
  bottom: "inception_resnet_v2_a2_3x3_2_reduce"
  top: "inception_resnet_v2_a2_3x3_2_reduce"
}
layer {
  name: "inception_resnet_v2_a2_3x3_2"
  type: "Convolution"
  bottom: "inception_resnet_v2_a2_3x3_2_reduce"
  top: "inception_resnet_v2_a2_3x3_2"
  param {
    lr_mult: 1
    decay_mult: 1
  }
  param {
    lr_mult: 2
    decay_mult: 0
  }
  convolution_param {
    num_output: 48
    pad: 1
    kernel_size: 3
    stride: 1
    weight_filler {
      type: "xavier"
      std: 0.01
    }
    bias_filler {
      type: "constant"
      value: 0.2
    }
  }
}
layer {
  name: "inception_resnet_v2_a2_3x3_2_bn"
  type: "BatchNorm"
  bottom: "inception_resnet_v2_a2_3x3_2"
  top: "inception_resnet_v2_a2_3x3_2"
    param {
        lr_mult: 0.0
        decay_mult: 0.0
    }
    param {
        lr_mult: 0.0
        decay_mult: 0.0
    }
    param {
        lr_mult: 0.0
        decay_mult: 0.0
    }
	batch_norm_param {
        use_global_stats: false
        moving_average_fraction: 0.95
	}
}
layer {
  name: "inception_resnet_v2_a2_3x3_2_scale"
  type: "Scale"
  bottom: "inception_resnet_v2_a2_3x3_2"
  top: "inception_resnet_v2_a2_3x3_2"
  scale_param {
    bias_term: true
  }
}
layer {
  name: "inception_resnet_v2_a2_3x3_2_relu"
  type: "PReLU"
  bottom: "inception_resnet_v2_a2_3x3_2"
  top: "inception_resnet_v2_a2_3x3_2"
}
layer {
  name: "inception_resnet_v2_a2_3x3_3"
  type: "Convolution"
  bottom: "inception_resnet_v2_a2_3x3_2"
  top: "inception_resnet_v2_a2_3x3_3"
  param {
    lr_mult: 1
    decay_mult: 1
  }
  param {
    lr_mult: 2
    decay_mult: 0
  }
  convolution_param {
    num_output: 64
    pad: 1
    kernel_size: 3
    stride: 1
    weight_filler {
      type: "xavier"
      std: 0.01
    }
    bias_filler {
      type: "constant"
      value: 0.2
    }
  }
}
layer {
  name: "inception_resnet_v2_a2_3x3_3_bn"
  type: "BatchNorm"
  bottom: "inception_resnet_v2_a2_3x3_3"
  top: "inception_resnet_v2_a2_3x3_3"
    param {
        lr_mult: 0.0
        decay_mult: 0.0
    }
    param {
        lr_mult: 0.0
        decay_mult: 0.0
    }
    param {
        lr_mult: 0.0
        decay_mult: 0.0
    }
	batch_norm_param {
        use_global_stats: false
        moving_average_fraction: 0.95
	}
}
layer {
  name: "inception_resnet_v2_a2_3x3_3_scale"
  type: "Scale"
  bottom: "inception_resnet_v2_a2_3x3_3"
  top: "inception_resnet_v2_a2_3x3_3"
  scale_param {
    bias_term: true
  }
}
layer {
  name: "inception_resnet_v2_a2_3x3_3_relu"
  type: "PReLU"
  bottom: "inception_resnet_v2_a2_3x3_3"
  top: "inception_resnet_v2_a2_3x3_3"
}
layer {
  name: "inception_resnet_v2_a2_concat"
  type: "Concat"
  bottom: "inception_resnet_v2_a2_1x1"
  bottom: "inception_resnet_v2_a2_3x3"
  bottom: "inception_resnet_v2_a2_3x3_3"
  top: "inception_resnet_v2_a2_concat"
}
layer {
  name: "inception_resnet_v2_a2_up"
  type: "Convolution"
  bottom: "inception_resnet_v2_a2_concat"
  top: "inception_resnet_v2_a2_up"
  param {
    lr_mult: 1
    decay_mult: 1
  }
  param {
    lr_mult: 2
    decay_mult: 0
  }
  convolution_param {
    num_output: 320
    pad: 0
    kernel_size: 1
    stride: 1
    weight_filler {
      type: "xavier"
      std: 0.01
    }
    bias_filler {
      type: "constant"
      value: 0.2
    }
  }
}
layer {
  name: "inception_resnet_v2_a2_up_bn"
  type: "BatchNorm"
  bottom: "inception_resnet_v2_a2_up"
  top: "inception_resnet_v2_a2_up"
    param {
        lr_mult: 0.0
        decay_mult: 0.0
    }
    param {
        lr_mult: 0.0
        decay_mult: 0.0
    }
    param {
        lr_mult: 0.0
        decay_mult: 0.0
    }
	batch_norm_param {
        use_global_stats: false
        moving_average_fraction: 0.95
	}
}
layer {
  name: "inception_resnet_v2_a2_up_scale"
  type: "Scale"
  bottom: "inception_resnet_v2_a2_up"
  top: "inception_resnet_v2_a2_up"
  scale_param {
    bias_term: true
  }
}
layer {
  name: "inception_resnet_v2_a2_residual_eltwise"
  type: "Eltwise"
  bottom: "inception_resnet_v2_a1_residual_eltwise"
  bottom: "inception_resnet_v2_a2_up"
  top: "inception_resnet_v2_a2_residual_eltwise"
  eltwise_param {
    operation: SUM
  }
}
layer {
  name: "inception_resnet_v2_a2_residual_eltwise_relu"
  type: "PReLU"
  bottom: "inception_resnet_v2_a2_residual_eltwise"
  top: "inception_resnet_v2_a2_residual_eltwise"
}
layer {
  name: "inception_resnet_v2_a3_1x1"
  type: "Convolution"
  bottom: "inception_resnet_v2_a2_residual_eltwise"
  top: "inception_resnet_v2_a3_1x1"
  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: 1
    weight_filler {
      type: "xavier"
      std: 0.01
    }
    bias_filler {
      type: "constant"
      value: 0.2
    }
  }
}
layer {
  name: "inception_resnet_v2_a3_1x1_bn"
  type: "BatchNorm"
  bottom: "inception_resnet_v2_a3_1x1"
  top: "inception_resnet_v2_a3_1x1"
    param {
        lr_mult: 0.0
        decay_mult: 0.0
    }
    param {
        lr_mult: 0.0
        decay_mult: 0.0
    }
    param {
        lr_mult: 0.0
        decay_mult: 0.0
    }
	batch_norm_param {
        use_global_stats: false
        moving_average_fraction: 0.95
	}
}
layer {
  name: "inception_resnet_v2_a3_1x1_scale"
  type: "Scale"
  bottom: "inception_resnet_v2_a3_1x1"
  top: "inception_resnet_v2_a3_1x1"
  scale_param {
    bias_term: true
  }
}
layer {
  name: "inception_resnet_v2_a3_1x1_relu"
  type: "PReLU"
  bottom: "inception_resnet_v2_a3_1x1"
  top: "inception_resnet_v2_a3_1x1"
}
layer {
  name: "inception_resnet_v2_a3_3x3_reduce"
  type: "Convolution"
  bottom: "inception_resnet_v2_a2_residual_eltwise"
  top: "inception_resnet_v2_a3_3x3_reduce"
  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: 1
    weight_filler {
      type: "xavier"
      std: 0.01
    }
    bias_filler {
      type: "constant"
      value: 0.2
    }
  }
}
layer {
  name: "inception_resnet_v2_a3_3x3_reduce_bn"
  type: "BatchNorm"
  bottom: "inception_resnet_v2_a3_3x3_reduce"
  top: "inception_resnet_v2_a3_3x3_reduce"
    param {
        lr_mult: 0.0
        decay_mult: 0.0
    }
    param {
        lr_mult: 0.0
        decay_mult: 0.0
    }
    param {
        lr_mult: 0.0
        decay_mult: 0.0
    }
	batch_norm_param {
        use_global_stats: false
        moving_average_fraction: 0.95
	}
}
layer {
  name: "inception_resnet_v2_a3_3x3_reduce_scale"
  type: "Scale"
  bottom: "inception_resnet_v2_a3_3x3_reduce"
  top: "inception_resnet_v2_a3_3x3_reduce"
  scale_param {
    bias_term: true
  }
}
layer {
  name: "inception_resnet_v2_a3_3x3_reduce_relu"
  type: "PReLU"
  bottom: "inception_resnet_v2_a3_3x3_reduce"
  top: "inception_resnet_v2_a3_3x3_reduce"
}
layer {
  name: "inception_resnet_v2_a3_3x3"
  type: "Convolution"
  bottom: "inception_resnet_v2_a3_3x3_reduce"
  top: "inception_resnet_v2_a3_3x3"
  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: "xavier"
      std: 0.01
    }
    bias_filler {
      type: "constant"
      value: 0.2
    }
  }
}
layer {
  name: "inception_resnet_v2_a3_3x3_bn"
  type: "BatchNorm"
  bottom: "inception_resnet_v2_a3_3x3"
  top: "inception_resnet_v2_a3_3x3"
    param {
        lr_mult: 0.0
        decay_mult: 0.0
    }
    param {
        lr_mult: 0.0
        decay_mult: 0.0
    }
    param {
        lr_mult: 0.0
        decay_mult: 0.0
    }
	batch_norm_param {
        use_global_stats: false
        moving_average_fraction: 0.95
	}
}
layer {
  name: "inception_resnet_v2_a3_3x3_scale"
  type: "Scale"
  bottom: "inception_resnet_v2_a3_3x3"
  top: "inception_resnet_v2_a3_3x3"
  scale_param {
    bias_term: true
  }
}
layer {
  name: "inception_resnet_v2_a3_3x3_relu"
  type: "PReLU"
  bottom: "inception_resnet_v2_a3_3x3"
  top: "inception_resnet_v2_a3_3x3"
}
layer {
  name: "inception_resnet_v2_a3_3x3_2_reduce"
  type: "Convolution"
  bottom: "inception_resnet_v2_a2_residual_eltwise"
  top: "inception_resnet_v2_a3_3x3_2_reduce"
  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: 1
    weight_filler {
      type: "xavier"
      std: 0.01
    }
    bias_filler {
      type: "constant"
      value: 0.2
    }
  }
}
layer {
  name: "inception_resnet_v2_a3_3x3_2_reduce_bn"
  type: "BatchNorm"
  bottom: "inception_resnet_v2_a3_3x3_2_reduce"
  top: "inception_resnet_v2_a3_3x3_2_reduce"
    param {
        lr_mult: 0.0
        decay_mult: 0.0
    }
    param {
        lr_mult: 0.0
        decay_mult: 0.0
    }
    param {
        lr_mult: 0.0
        decay_mult: 0.0
    }
	batch_norm_param {
        use_global_stats: false
        moving_average_fraction: 0.95
	}
}
layer {
  name: "inception_resnet_v2_a3_3x3_2_reduce_scale"
  type: "Scale"
  bottom: "inception_resnet_v2_a3_3x3_2_reduce"
  top: "inception_resnet_v2_a3_3x3_2_reduce"
  scale_param {
    bias_term: true
  }
}
layer {
  name: "inception_resnet_v2_a3_3x3_2_reduce_relu"
  type: "PReLU"
  bottom: "inception_resnet_v2_a3_3x3_2_reduce"
  top: "inception_resnet_v2_a3_3x3_2_reduce"
}
layer {
  name: "inception_resnet_v2_a3_3x3_2"
  type: "Convolution"
  bottom: "inception_resnet_v2_a3_3x3_2_reduce"
  top: "inception_resnet_v2_a3_3x3_2"
  param {
    lr_mult: 1
    decay_mult: 1
  }
  param {
    lr_mult: 2
    decay_mult: 0
  }
  convolution_param {
    num_output: 48
    pad: 1
    kernel_size: 3
    stride: 1
    weight_filler {
      type: "xavier"
      std: 0.01
    }
    bias_filler {
      type: "constant"
      value: 0.2
    }
  }
}
layer {
  name: "inception_resnet_v2_a3_3x3_2_bn"
  type: "BatchNorm"
  bottom: "inception_resnet_v2_a3_3x3_2"
  top: "inception_resnet_v2_a3_3x3_2"
    param {
        lr_mult: 0.0
        decay_mult: 0.0
    }
    param {
        lr_mult: 0.0
        decay_mult: 0.0
    }
    param {
        lr_mult: 0.0
        decay_mult: 0.0
    }
	batch_norm_param {
        use_global_stats: false
        moving_average_fraction: 0.95
	}
}
layer {
  name: "inception_resnet_v2_a3_3x3_2_scale"
  type: "Scale"
  bottom: "inception_resnet_v2_a3_3x3_2"
  top: "inception_resnet_v2_a3_3x3_2"
  scale_param {
    bias_term: true
  }
}
layer {
  name: "inception_resnet_v2_a3_3x3_2_relu"
  type: "PReLU"
  bottom: "inception_resnet_v2_a3_3x3_2"
  top: "inception_resnet_v2_a3_3x3_2"
}
layer {
  name: "inception_resnet_v2_a3_3x3_3"
  type: "Convolution"
  bottom: "inception_resnet_v2_a3_3x3_2"
  top: "inception_resnet_v2_a3_3x3_3"
  param {
    lr_mult: 1
    decay_mult: 1
  }
  param {
    lr_mult: 2
    decay_mult: 0
  }
  convolution_param {
    num_output: 64
    pad: 1
    kernel_size: 3
    stride: 1
    weight_filler {
      type: "xavier"
      std: 0.01
    }
    bias_filler {
      type: "constant"
      value: 0.2
    }
  }
}
layer {
  name: "inception_resnet_v2_a3_3x3_3_bn"
  type: "BatchNorm"
  bottom: "inception_resnet_v2_a3_3x3_3"
  top: "inception_resnet_v2_a3_3x3_3"
    param {
        lr_mult: 0.0
        decay_mult: 0.0
    }
    param {
        lr_mult: 0.0
        decay_mult: 0.0
    }
    param {
        lr_mult: 0.0
        decay_mult: 0.0
    }
	batch_norm_param {
        use_global_stats: false
        moving_average_fraction: 0.95
	}
}
layer {
  name: "inception_resnet_v2_a3_3x3_3_scale"
  type: "Scale"
  bottom: "inception_resnet_v2_a3_3x3_3"
  top: "inception_resnet_v2_a3_3x3_3"
  scale_param {
    bias_term: true
  }
}
layer {
  name: "inception_resnet_v2_a3_3x3_3_relu"
  type: "PReLU"
  bottom: "inception_resnet_v2_a3_3x3_3"
  top: "inception_resnet_v2_a3_3x3_3"
}
layer {
  name: "inception_resnet_v2_a3_concat"
  type: "Concat"
  bottom: "inception_resnet_v2_a3_1x1"
  bottom: "inception_resnet_v2_a3_3x3"
  bottom: "inception_resnet_v2_a3_3x3_3"
  top: "inception_resnet_v2_a3_concat"
}
layer {
  name: "inception_resnet_v2_a3_up"
  type: "Convolution"
  bottom: "inception_resnet_v2_a3_concat"
  top: "inception_resnet_v2_a3_up"
  param {
    lr_mult: 1
    decay_mult: 1
  }
  param {
    lr_mult: 2
    decay_mult: 0
  }
  convolution_param {
    num_output: 320
    pad: 0
    kernel_size: 1
    stride: 1
    weight_filler {
      type: "xavier"
      std: 0.01
    }
    bias_filler {
      type: "constant"
      value: 0.2
    }
  }
}
layer {
  name: "inception_resnet_v2_a3_up_bn"
  type: "BatchNorm"
  bottom: "inception_resnet_v2_a3_up"
  top: "inception_resnet_v2_a3_up"
    param {
        lr_mult: 0.0
        decay_mult: 0.0
    }
    param {
        lr_mult: 0.0
        decay_mult: 0.0
    }
    param {
        lr_mult: 0.0
        decay_mult: 0.0
    }
	batch_norm_param {
        use_global_stats: false
        moving_average_fraction: 0.95
	}
}
layer {
  name: "inception_resnet_v2_a3_up_scale"
  type: "Scale"
  bottom: "inception_resnet_v2_a3_up"
  top: "inception_resnet_v2_a3_up"
  scale_param {
    bias_term: true
  }
}
layer {
  name: "inception_resnet_v2_a3_residual_eltwise"
  type: "Eltwise"
  bottom: "inception_resnet_v2_a2_residual_eltwise"
  bottom: "inception_resnet_v2_a3_up"
  top: "inception_resnet_v2_a3_residual_eltwise"
  eltwise_param {
    operation: SUM
  }
}
layer {
  name: "inception_resnet_v2_a3_residual_eltwise_relu"
  type: "PReLU"
  bottom: "inception_resnet_v2_a3_residual_eltwise"
  top: "inception_resnet_v2_a3_residual_eltwise"
}

layer {
  name: "reduction_a_3x3"
  type: "Convolution"
  bottom: "inception_resnet_v2_a3_residual_eltwise"
  top: "reduction_a_3x3"
  param {
    lr_mult: 1
    decay_mult: 1
  }
  param {
    lr_mult: 2
    decay_mult: 0
  }
  convolution_param {
    num_output: 384
    pad: 1
    kernel_size: 3
    stride: 2
    weight_filler {
      type: "xavier"
      std: 0.01
    }
    bias_filler {
      type: "constant"
      value: 0.2
    }
  }
}
layer {
  name: "reduction_a_3x3_bn"
  type: "BatchNorm"
  bottom: "reduction_a_3x3"
  top: "reduction_a_3x3"
    param {
        lr_mult: 0.0
        decay_mult: 0.0
    }
    param {
        lr_mult: 0.0
        decay_mult: 0.0
    }
    param {
        lr_mult: 0.0
        decay_mult: 0.0
    }
	batch_norm_param {
        use_global_stats: false
        moving_average_fraction: 0.95
	}
}
layer {
  name: "reduction_a_3x3_scale"
  type: "Scale"
  bottom: "reduction_a_3x3"
  top: "reduction_a_3x3"
  scale_param {
    bias_term: true
  }
}
layer {
  name: "reduction_a_3x3_relu"
  type: "PReLU"
  bottom: "reduction_a_3x3"
  top: "reduction_a_3x3"
}
layer {
  name: "reduction_a_3x3_2_reduce"
  type: "Convolution"
  bottom: "inception_resnet_v2_a3_residual_eltwise"
  top: "reduction_a_3x3_2_reduce"
  param {
    lr_mult: 1
    decay_mult: 1
  }
  param {
    lr_mult: 2
    decay_mult: 0
  }
  convolution_param {
    num_output: 256
    pad: 0
    kernel_size: 1
    stride: 1
    weight_filler {
      type: "xavier"
      std: 0.01
    }
    bias_filler {
      type: "constant"
      value: 0.2
    }
  }
}
layer {
  name: "reduction_a_3x3_2_reduce_bn"
  type: "BatchNorm"
  bottom: "reduction_a_3x3_2_reduce"
  top: "reduction_a_3x3_2_reduce"
    param {
        lr_mult: 0.0
        decay_mult: 0.0
    }
    param {
        lr_mult: 0.0
        decay_mult: 0.0
    }
    param {
        lr_mult: 0.0
        decay_mult: 0.0
    }
	batch_norm_param {
        use_global_stats: false
        moving_average_fraction: 0.95
	}
}
layer {
  name: "reduction_a_3x3_2_reduce_scale"
  type: "Scale"
  bottom: "reduction_a_3x3_2_reduce"
  top: "reduction_a_3x3_2_reduce"
  scale_param {
    bias_term: true
  }
}
layer {
  name: "reduction_a_3x3_2_reduce_relu"
  type: "PReLU"
  bottom: "reduction_a_3x3_2_reduce"
  top: "reduction_a_3x3_2_reduce"
}
layer {
  name: "reduction_a_3x3_2"
  type: "Convolution"
  bottom: "reduction_a_3x3_2_reduce"
  top: "reduction_a_3x3_2"
  param {
    lr_mult: 1
    decay_mult: 1
  }
  param {
    lr_mult: 2
    decay_mult: 0
  }
  convolution_param {
    num_output: 256
    pad: 1
    kernel_size: 3
    stride: 1
    weight_filler {
      type: "xavier"
      std: 0.01
    }
    bias_filler {
      type: "constant"
      value: 0.2
    }
  }
}
layer {
  name: "reduction_a_3x3_2_bn"
  type: "BatchNorm"
  bottom: "reduction_a_3x3_2"
  top: "reduction_a_3x3_2"
    param {
        lr_mult: 0.0
        decay_mult: 0.0
    }
    param {
        lr_mult: 0.0
        decay_mult: 0.0
    }
    param {
        lr_mult: 0.0
        decay_mult: 0.0
    }
	batch_norm_param {
        use_global_stats: false
        moving_average_fraction: 0.95
	}
}
layer {
  name: "reduction_a_3x3_2_scale"
  type: "Scale"
  bottom: "reduction_a_3x3_2"
  top: "reduction_a_3x3_2"
  scale_param {
    bias_term: true
  }
}
layer {
  name: "reduction_a_3x3_2_relu"
  type: "PReLU"
  bottom: "reduction_a_3x3_2"
  top: "reduction_a_3x3_2"
}
layer {
  name: "reduction_a_3x3_3"
  type: "Convolution"
  bottom: "reduction_a_3x3_2"
  top: "reduction_a_3x3_3"
  param {
    lr_mult: 1
    decay_mult: 1
  }
  param {
    lr_mult: 2
    decay_mult: 0
  }
  convolution_param {
    num_output: 384
    pad: 1
    kernel_size: 3
    stride: 2
    weight_filler {
      type: "xavier"
      std: 0.01
    }
    bias_filler {
      type: "constant"
      value: 0.2
    }
  }
}
layer {
  name: "reduction_a_3x3_3_bn"
  type: "BatchNorm"
  bottom: "reduction_a_3x3_3"
  top: "reduction_a_3x3_3"
    param {
        lr_mult: 0.0
        decay_mult: 0.0
    }
    param {
        lr_mult: 0.0
        decay_mult: 0.0
    }
    param {
        lr_mult: 0.0
        decay_mult: 0.0
    }
	batch_norm_param {
        use_global_stats: false
        moving_average_fraction: 0.95
	}
}
layer {
  name: "reduction_a_3x3_3_scale"
  type: "Scale"
  bottom: "reduction_a_3x3_3"
  top: "reduction_a_3x3_3"
  scale_param {
    bias_term: true
  }
}
layer {
  name: "reduction_a_3x3_3_relu"
  type: "PReLU"
  bottom: "reduction_a_3x3_3"
  top: "reduction_a_3x3_3"
}
layer {
  name: "reduction_a_pool"
  type: "Pooling"
  bottom: "inception_resnet_v2_a3_residual_eltwise"
  top: "reduction_a_pool"
  pooling_param {
    pool: MAX
    kernel_size: 3
    stride: 2
  }
}
layer {
  name: "reduction_a_concat"
  type: "Concat"
  bottom: "reduction_a_3x3"
  bottom: "reduction_a_3x3_3"
  bottom: "reduction_a_pool"
  top: "reduction_a_concat"
}
layer {
  name: "inception_resnet_v2_b1_1x1"
  type: "Convolution"
  bottom: "reduction_a_concat"
  top: "inception_resnet_v2_b1_1x1"
  param {
    lr_mult: 1
    decay_mult: 1
  }
  param {
    lr_mult: 2
    decay_mult: 0
  }
  convolution_param {
    num_output: 192
    pad: 0
    kernel_size: 1
    stride: 1
    weight_filler {
      type: "xavier"
      std: 0.01
    }
    bias_filler {
      type: "constant"
      value: 0.2
    }
  }
}
layer {
  name: "inception_resnet_v2_b1_1x1_bn"
  type: "BatchNorm"
  bottom: "inception_resnet_v2_b1_1x1"
  top: "inception_resnet_v2_b1_1x1"
    param {
        lr_mult: 0.0
        decay_mult: 0.0
    }
    param {
        lr_mult: 0.0
        decay_mult: 0.0
    }
    param {
        lr_mult: 0.0
        decay_mult: 0.0
    }
	batch_norm_param {
        use_global_stats: false
        moving_average_fraction: 0.95
	}
}
layer {
  name: "inception_resnet_v2_b1_1x1_scale"
  type: "Scale"
  bottom: "inception_resnet_v2_b1_1x1"
  top: "inception_resnet_v2_b1_1x1"
  scale_param {
    bias_term: true
  }
}
layer {
  name: "inception_resnet_v2_b1_1x1_relu"
  type: "PReLU"
  bottom: "inception_resnet_v2_b1_1x1"
  top: "inception_resnet_v2_b1_1x1"
}
layer {
  name: "inception_resnet_v2_b1_1x7_reduce"
  type: "Convolution"
  bottom: "reduction_a_concat"
  top: "inception_resnet_v2_b1_1x7_reduce"
  param {
    lr_mult: 1
    decay_mult: 1
  }
  param {
    lr_mult: 2
    decay_mult: 0
  }
  convolution_param {
    num_output: 128
    pad: 0
    kernel_size: 1
    stride: 1
    weight_filler {
      type: "xavier"
      std: 0.01
    }
    bias_filler {
      type: "constant"
      value: 0.2
    }
  }
}
layer {
  name: "inception_resnet_v2_b1_1x7_reduce_bn"
  type: "BatchNorm"
  bottom: "inception_resnet_v2_b1_1x7_reduce"
  top: "inception_resnet_v2_b1_1x7_reduce"
    param {
        lr_mult: 0.0
        decay_mult: 0.0
    }
    param {
        lr_mult: 0.0
        decay_mult: 0.0
    }
    param {
        lr_mult: 0.0
        decay_mult: 0.0
    }
	batch_norm_param {
        use_global_stats: false
        moving_average_fraction: 0.95
	}
}
layer {
  name: "inception_resnet_v2_b1_1x7_reduce_scale"
  type: "Scale"
  bottom: "inception_resnet_v2_b1_1x7_reduce"
  top: "inception_resnet_v2_b1_1x7_reduce"
  scale_param {
    bias_term: true
  }
}
layer {
  name: "inception_resnet_v2_b1_1x7_reduce_relu"
  type: "PReLU"
  bottom: "inception_resnet_v2_b1_1x7_reduce"
  top: "inception_resnet_v2_b1_1x7_reduce"
}
layer {
  name: "inception_resnet_v2_b1_1x7"
  type: "Convolution"
  bottom: "inception_resnet_v2_b1_1x7_reduce"
  top: "inception_resnet_v2_b1_1x7"
  param {
    lr_mult: 1
    decay_mult: 1
  }
  param {
    lr_mult: 2
    decay_mult: 0
  }
  convolution_param {
    num_output: 160
    stride: 1
    weight_filler {
      type: "xavier"
      std: 0.01
    }
    bias_filler {
      type: "constant"
      value: 0.2
    }
    pad_h: 0
    pad_w: 3
    kernel_h: 1
    kernel_w: 7
  }
}
layer {
  name: "inception_resnet_v2_b1_1x7_bn"
  type: "BatchNorm"
  bottom: "inception_resnet_v2_b1_1x7"
  top: "inception_resnet_v2_b1_1x7"
    param {
        lr_mult: 0.0
        decay_mult: 0.0
    }
    param {
        lr_mult: 0.0
        decay_mult: 0.0
    }
    param {
        lr_mult: 0.0
        decay_mult: 0.0
    }
	batch_norm_param {
        use_global_stats: false
        moving_average_fraction: 0.95
	}
}
layer {
  name: "inception_resnet_v2_b1_1x7_scale"
  type: "Scale"
  bottom: "inception_resnet_v2_b1_1x7"
  top: "inception_resnet_v2_b1_1x7"
  scale_param {
    bias_term: true
  }
}
layer {
  name: "inception_resnet_v2_b1_1x7_relu"
  type: "PReLU"
  bottom: "inception_resnet_v2_b1_1x7"
  top: "inception_resnet_v2_b1_1x7"
}
layer {
  name: "inception_resnet_v2_b1_7x1"
  type: "Convolution"
  bottom: "inception_resnet_v2_b1_1x7"
  top: "inception_resnet_v2_b1_7x1"
  param {
    lr_mult: 1
    decay_mult: 1
  }
  param {
    lr_mult: 2
    decay_mult: 0
  }
  convolution_param {
    num_output: 192
    stride: 1
    weight_filler {
      type: "xavier"
      std: 0.01
    }
    bias_filler {
      type: "constant"
      value: 0.2
    }
    pad_h: 3
    pad_w: 0
    kernel_h: 7
    kernel_w: 1
  }
}
layer {
  name: "inception_resnet_v2_b1_7x1_bn"
  type: "BatchNorm"
  bottom: "inception_resnet_v2_b1_7x1"
  top: "inception_resnet_v2_b1_7x1"
    param {
        lr_mult: 0.0
        decay_mult: 0.0
    }
    param {
        lr_mult: 0.0
        decay_mult: 0.0
    }
    param {
        lr_mult: 0.0
        decay_mult: 0.0
    }
	batch_norm_param {
        use_global_stats: false
        moving_average_fraction: 0.95
	}
}
layer {
  name: "inception_resnet_v2_b1_7x1_scale"
  type: "Scale"
  bottom: "inception_resnet_v2_b1_7x1"
  top: "inception_resnet_v2_b1_7x1"
  scale_param {
    bias_term: true
  }
}
layer {
  name: "inception_resnet_v2_b1_7x1_relu"
  type: "PReLU"
  bottom: "inception_resnet_v2_b1_7x1"
  top: "inception_resnet_v2_b1_7x1"
}
layer {
  name: "inception_resnet_v2_b1_concat"
  type: "Concat"
  bottom: "inception_resnet_v2_b1_1x1"
  bottom: "inception_resnet_v2_b1_7x1"
  top: "inception_resnet_v2_b1_concat"
}
layer {
  name: "inception_resnet_v2_b1_up"
  type: "Convolution"
  bottom: "inception_resnet_v2_b1_concat"
  top: "inception_resnet_v2_b1_up"
  param {
    lr_mult: 1
    decay_mult: 1
  }
  param {
    lr_mult: 2
    decay_mult: 0
  }
  convolution_param {
    num_output: 1088
    pad: 0
    kernel_size: 1
    stride: 1
    weight_filler {
      type: "xavier"
      std: 0.01
    }
    bias_filler {
      type: "constant"
      value: 0.2
    }
  }
}
layer {
  name: "inception_resnet_v2_b1_up_bn"
  type: "BatchNorm"
  bottom: "inception_resnet_v2_b1_up"
  top: "inception_resnet_v2_b1_up"
    param {
        lr_mult: 0.0
        decay_mult: 0.0
    }
    param {
        lr_mult: 0.0
        decay_mult: 0.0
    }
    param {
        lr_mult: 0.0
        decay_mult: 0.0
    }
	batch_norm_param {
        use_global_stats: false
        moving_average_fraction: 0.95
	}
}
layer {
  name: "inception_resnet_v2_b1_up_scale"
  type: "Scale"
  bottom: "inception_resnet_v2_b1_up"
  top: "inception_resnet_v2_b1_up"
  scale_param {
    bias_term: true
  }
}
layer {
  name: "inception_resnet_v2_b1_residual_eltwise"
  type: "Eltwise"
  bottom: "reduction_a_concat"
  bottom: "inception_resnet_v2_b1_up"
  top: "inception_resnet_v2_b1_residual_eltwise"
  eltwise_param {
    operation: SUM
  }
}
layer {
  name: "inception_resnet_v2_b1_residual_eltwise_relu"
  type: "PReLU"
  bottom: "inception_resnet_v2_b1_residual_eltwise"
  top: "inception_resnet_v2_b1_residual_eltwise"
}
layer {
  name: "inception_resnet_v2_b2_1x1"
  type: "Convolution"
  bottom: "inception_resnet_v2_b1_residual_eltwise"
  top: "inception_resnet_v2_b2_1x1"
  param {
    lr_mult: 1
    decay_mult: 1
  }
  param {
    lr_mult: 2
    decay_mult: 0
  }
  convolution_param {
    num_output: 192
    pad: 0
    kernel_size: 1
    stride: 1
    weight_filler {
      type: "xavier"
      std: 0.01
    }
    bias_filler {
      type: "constant"
      value: 0.2
    }
  }
}
layer {
  name: "inception_resnet_v2_b2_1x1_bn"
  type: "BatchNorm"
  bottom: "inception_resnet_v2_b2_1x1"
  top: "inception_resnet_v2_b2_1x1"
    param {
        lr_mult: 0.0
        decay_mult: 0.0
    }
    param {
        lr_mult: 0.0
        decay_mult: 0.0
    }
    param {
        lr_mult: 0.0
        decay_mult: 0.0
    }
	batch_norm_param {
        use_global_stats: false
        moving_average_fraction: 0.95
	}
}
layer {
  name: "inception_resnet_v2_b2_1x1_scale"
  type: "Scale"
  bottom: "inception_resnet_v2_b2_1x1"
  top: "inception_resnet_v2_b2_1x1"
  scale_param {
    bias_term: true
  }
}
layer {
  name: "inception_resnet_v2_b2_1x1_relu"
  type: "PReLU"
  bottom: "inception_resnet_v2_b2_1x1"
  top: "inception_resnet_v2_b2_1x1"
}
layer {
  name: "inception_resnet_v2_b2_1x7_reduce"
  type: "Convolution"
  bottom: "inception_resnet_v2_b1_residual_eltwise"
  top: "inception_resnet_v2_b2_1x7_reduce"
  param {
    lr_mult: 1
    decay_mult: 1
  }
  param {
    lr_mult: 2
    decay_mult: 0
  }
  convolution_param {
    num_output: 128
    pad: 0
    kernel_size: 1
    stride: 1
    weight_filler {
      type: "xavier"
      std: 0.01
    }
    bias_filler {
      type: "constant"
      value: 0.2
    }
  }
}
layer {
  name: "inception_resnet_v2_b2_1x7_reduce_bn"
  type: "BatchNorm"
  bottom: "inception_resnet_v2_b2_1x7_reduce"
  top: "inception_resnet_v2_b2_1x7_reduce"
    param {
        lr_mult: 0.0
        decay_mult: 0.0
    }
    param {
        lr_mult: 0.0
        decay_mult: 0.0
    }
    param {
        lr_mult: 0.0
        decay_mult: 0.0
    }
	batch_norm_param {
        use_global_stats: false
        moving_average_fraction: 0.95
	}
}
layer {
  name: "inception_resnet_v2_b2_1x7_reduce_scale"
  type: "Scale"
  bottom: "inception_resnet_v2_b2_1x7_reduce"
  top: "inception_resnet_v2_b2_1x7_reduce"
  scale_param {
    bias_term: true
  }
}
layer {
  name: "inception_resnet_v2_b2_1x7_reduce_relu"
  type: "PReLU"
  bottom: "inception_resnet_v2_b2_1x7_reduce"
  top: "inception_resnet_v2_b2_1x7_reduce"
}
layer {
  name: "inception_resnet_v2_b2_1x7"
  type: "Convolution"
  bottom: "inception_resnet_v2_b2_1x7_reduce"
  top: "inception_resnet_v2_b2_1x7"
  param {
    lr_mult: 1
    decay_mult: 1
  }
  param {
    lr_mult: 2
    decay_mult: 0
  }
  convolution_param {
    num_output: 160
    stride: 1
    weight_filler {
      type: "xavier"
      std: 0.01
    }
    bias_filler {
      type: "constant"
      value: 0.2
    }
    pad_h: 0
    pad_w: 3
    kernel_h: 1
    kernel_w: 7
  }
}
layer {
  name: "inception_resnet_v2_b2_1x7_bn"
  type: "BatchNorm"
  bottom: "inception_resnet_v2_b2_1x7"
  top: "inception_resnet_v2_b2_1x7"
    param {
        lr_mult: 0.0
        decay_mult: 0.0
    }
    param {
        lr_mult: 0.0
        decay_mult: 0.0
    }
    param {
        lr_mult: 0.0
        decay_mult: 0.0
    }
	batch_norm_param {
        use_global_stats: false
        moving_average_fraction: 0.95
	}
}
layer {
  name: "inception_resnet_v2_b2_1x7_scale"
  type: "Scale"
  bottom: "inception_resnet_v2_b2_1x7"
  top: "inception_resnet_v2_b2_1x7"
  scale_param {
    bias_term: true
  }
}
layer {
  name: "inception_resnet_v2_b2_1x7_relu"
  type: "PReLU"
  bottom: "inception_resnet_v2_b2_1x7"
  top: "inception_resnet_v2_b2_1x7"
}
layer {
  name: "inception_resnet_v2_b2_7x1"
  type: "Convolution"
  bottom: "inception_resnet_v2_b2_1x7"
  top: "inception_resnet_v2_b2_7x1"
  param {
    lr_mult: 1
    decay_mult: 1
  }
  param {
    lr_mult: 2
    decay_mult: 0
  }
  convolution_param {
    num_output: 192
    stride: 1
    weight_filler {
      type: "xavier"
      std: 0.01
    }
    bias_filler {
      type: "constant"
      value: 0.2
    }
    pad_h: 3
    pad_w: 0
    kernel_h: 7
    kernel_w: 1
  }
}
layer {
  name: "inception_resnet_v2_b2_7x1_bn"
  type: "BatchNorm"
  bottom: "inception_resnet_v2_b2_7x1"
  top: "inception_resnet_v2_b2_7x1"
    param {
        lr_mult: 0.0
        decay_mult: 0.0
    }
    param {
        lr_mult: 0.0
        decay_mult: 0.0
    }
    param {
        lr_mult: 0.0
        decay_mult: 0.0
    }
	batch_norm_param {
        use_global_stats: false
        moving_average_fraction: 0.95
	}
}
layer {
  name: "inception_resnet_v2_b2_7x1_scale"
  type: "Scale"
  bottom: "inception_resnet_v2_b2_7x1"
  top: "inception_resnet_v2_b2_7x1"
  scale_param {
    bias_term: true
  }
}
layer {
  name: "inception_resnet_v2_b2_7x1_relu"
  type: "PReLU"
  bottom: "inception_resnet_v2_b2_7x1"
  top: "inception_resnet_v2_b2_7x1"
}
layer {
  name: "inception_resnet_v2_b2_concat"
  type: "Concat"
  bottom: "inception_resnet_v2_b2_1x1"
  bottom: "inception_resnet_v2_b2_7x1"
  top: "inception_resnet_v2_b2_concat"
}
layer {
  name: "inception_resnet_v2_b2_up"
  type: "Convolution"
  bottom: "inception_resnet_v2_b2_concat"
  top: "inception_resnet_v2_b2_up"
  param {
    lr_mult: 1
    decay_mult: 1
  }
  param {
    lr_mult: 2
    decay_mult: 0
  }
  convolution_param {
    num_output: 1088
    pad: 0
    kernel_size: 1
    stride: 1
    weight_filler {
      type: "xavier"
      std: 0.01
    }
    bias_filler {
      type: "constant"
      value: 0.2
    }
  }
}
layer {
  name: "inception_resnet_v2_b2_up_bn"
  type: "BatchNorm"
  bottom: "inception_resnet_v2_b2_up"
  top: "inception_resnet_v2_b2_up"
    param {
        lr_mult: 0.0
        decay_mult: 0.0
    }
    param {
        lr_mult: 0.0
        decay_mult: 0.0
    }
    param {
        lr_mult: 0.0
        decay_mult: 0.0
    }
	batch_norm_param {
        use_global_stats: false
        moving_average_fraction: 0.95
	}
}
layer {
  name: "inception_resnet_v2_b2_up_scale"
  type: "Scale"
  bottom: "inception_resnet_v2_b2_up"
  top: "inception_resnet_v2_b2_up"
  scale_param {
    bias_term: true
  }
}
layer {
  name: "inception_resnet_v2_b2_residual_eltwise"
  type: "Eltwise"
  bottom: "inception_resnet_v2_b1_residual_eltwise"
  bottom: "inception_resnet_v2_b2_up"
  top: "inception_resnet_v2_b2_residual_eltwise"
  eltwise_param {
    operation: SUM
  }
}
layer {
  name: "inception_resnet_v2_b2_residual_eltwise_relu"
  type: "PReLU"
  bottom: "inception_resnet_v2_b2_residual_eltwise"
  top: "inception_resnet_v2_b2_residual_eltwise"
}
layer {
  name: "inception_resnet_v2_b3_1x1"
  type: "Convolution"
  bottom: "inception_resnet_v2_b2_residual_eltwise"
  top: "inception_resnet_v2_b3_1x1"
  param {
    lr_mult: 1
    decay_mult: 1
  }
  param {
    lr_mult: 2
    decay_mult: 0
  }
  convolution_param {
    num_output: 192
    pad: 0
    kernel_size: 1
    stride: 1
    weight_filler {
      type: "xavier"
      std: 0.01
    }
    bias_filler {
      type: "constant"
      value: 0.2
    }
  }
}
layer {
  name: "inception_resnet_v2_b3_1x1_bn"
  type: "BatchNorm"
  bottom: "inception_resnet_v2_b3_1x1"
  top: "inception_resnet_v2_b3_1x1"
    param {
        lr_mult: 0.0
        decay_mult: 0.0
    }
    param {
        lr_mult: 0.0
        decay_mult: 0.0
    }
    param {
        lr_mult: 0.0
        decay_mult: 0.0
    }
	batch_norm_param {
        use_global_stats: false
        moving_average_fraction: 0.95
	}
}
layer {
  name: "inception_resnet_v2_b3_1x1_scale"
  type: "Scale"
  bottom: "inception_resnet_v2_b3_1x1"
  top: "inception_resnet_v2_b3_1x1"
  scale_param {
    bias_term: true
  }
}
layer {
  name: "inception_resnet_v2_b3_1x1_relu"
  type: "PReLU"
  bottom: "inception_resnet_v2_b3_1x1"
  top: "inception_resnet_v2_b3_1x1"
}
layer {
  name: "inception_resnet_v2_b3_1x7_reduce"
  type: "Convolution"
  bottom: "inception_resnet_v2_b2_residual_eltwise"
  top: "inception_resnet_v2_b3_1x7_reduce"
  param {
    lr_mult: 1
    decay_mult: 1
  }
  param {
    lr_mult: 2
    decay_mult: 0
  }
  convolution_param {
    num_output: 128
    pad: 0
    kernel_size: 1
    stride: 1
    weight_filler {
      type: "xavier"
      std: 0.01
    }
    bias_filler {
      type: "constant"
      value: 0.2
    }
  }
}
layer {
  name: "inception_resnet_v2_b3_1x7_reduce_bn"
  type: "BatchNorm"
  bottom: "inception_resnet_v2_b3_1x7_reduce"
  top: "inception_resnet_v2_b3_1x7_reduce"
    param {
        lr_mult: 0.0
        decay_mult: 0.0
    }
    param {
        lr_mult: 0.0
        decay_mult: 0.0
    }
    param {
        lr_mult: 0.0
        decay_mult: 0.0
    }
	batch_norm_param {
        use_global_stats: false
        moving_average_fraction: 0.95
	}
}
layer {
  name: "inception_resnet_v2_b3_1x7_reduce_scale"
  type: "Scale"
  bottom: "inception_resnet_v2_b3_1x7_reduce"
  top: "inception_resnet_v2_b3_1x7_reduce"
  scale_param {
    bias_term: true
  }
}
layer {
  name: "inception_resnet_v2_b3_1x7_reduce_relu"
  type: "PReLU"
  bottom: "inception_resnet_v2_b3_1x7_reduce"
  top: "inception_resnet_v2_b3_1x7_reduce"
}
layer {
  name: "inception_resnet_v2_b3_1x7"
  type: "Convolution"
  bottom: "inception_resnet_v2_b3_1x7_reduce"
  top: "inception_resnet_v2_b3_1x7"
  param {
    lr_mult: 1
    decay_mult: 1
  }
  param {
    lr_mult: 2
    decay_mult: 0
  }
  convolution_param {
    num_output: 160
    stride: 1
    weight_filler {
      type: "xavier"
      std: 0.01
    }
    bias_filler {
      type: "constant"
      value: 0.2
    }
    pad_h: 0
    pad_w: 3
    kernel_h: 1
    kernel_w: 7
  }
}
layer {
  name: "inception_resnet_v2_b3_1x7_bn"
  type: "BatchNorm"
  bottom: "inception_resnet_v2_b3_1x7"
  top: "inception_resnet_v2_b3_1x7"
    param {
        lr_mult: 0.0
        decay_mult: 0.0
    }
    param {
        lr_mult: 0.0
        decay_mult: 0.0
    }
    param {
        lr_mult: 0.0
        decay_mult: 0.0
    }
	batch_norm_param {
        use_global_stats: false
        moving_average_fraction: 0.95
	}
}
layer {
  name: "inception_resnet_v2_b3_1x7_scale"
  type: "Scale"
  bottom: "inception_resnet_v2_b3_1x7"
  top: "inception_resnet_v2_b3_1x7"
  scale_param {
    bias_term: true
  }
}
layer {
  name: "inception_resnet_v2_b3_1x7_relu"
  type: "PReLU"
  bottom: "inception_resnet_v2_b3_1x7"
  top: "inception_resnet_v2_b3_1x7"
}
layer {
  name: "inception_resnet_v2_b3_7x1"
  type: "Convolution"
  bottom: "inception_resnet_v2_b3_1x7"
  top: "inception_resnet_v2_b3_7x1"
  param {
    lr_mult: 1
    decay_mult: 1
  }
  param {
    lr_mult: 2
    decay_mult: 0
  }
  convolution_param {
    num_output: 192
    stride: 1
    weight_filler {
      type: "xavier"
      std: 0.01
    }
    bias_filler {
      type: "constant"
      value: 0.2
    }
    pad_h: 3
    pad_w: 0
    kernel_h: 7
    kernel_w: 1
  }
}
layer {
  name: "inception_resnet_v2_b3_7x1_bn"
  type: "BatchNorm"
  bottom: "inception_resnet_v2_b3_7x1"
  top: "inception_resnet_v2_b3_7x1"
    param {
        lr_mult: 0.0
        decay_mult: 0.0
    }
    param {
        lr_mult: 0.0
        decay_mult: 0.0
    }
    param {
        lr_mult: 0.0
        decay_mult: 0.0
    }
	batch_norm_param {
        use_global_stats: false
        moving_average_fraction: 0.95
	}
}
layer {
  name: "inception_resnet_v2_b3_7x1_scale"
  type: "Scale"
  bottom: "inception_resnet_v2_b3_7x1"
  top: "inception_resnet_v2_b3_7x1"
  scale_param {
    bias_term: true
  }
}
layer {
  name: "inception_resnet_v2_b3_7x1_relu"
  type: "PReLU"
  bottom: "inception_resnet_v2_b3_7x1"
  top: "inception_resnet_v2_b3_7x1"
}
layer {
  name: "inception_resnet_v2_b3_concat"
  type: "Concat"
  bottom: "inception_resnet_v2_b3_1x1"
  bottom: "inception_resnet_v2_b3_7x1"
  top: "inception_resnet_v2_b3_concat"
}
layer {
  name: "inception_resnet_v2_b3_up"
  type: "Convolution"
  bottom: "inception_resnet_v2_b3_concat"
  top: "inception_resnet_v2_b3_up"
  param {
    lr_mult: 1
    decay_mult: 1
  }
  param {
    lr_mult: 2
    decay_mult: 0
  }
  convolution_param {
    num_output: 1088
    pad: 0
    kernel_size: 1
    stride: 1
    weight_filler {
      type: "xavier"
      std: 0.01
    }
    bias_filler {
      type: "constant"
      value: 0.2
    }
  }
}
layer {
  name: "inception_resnet_v2_b3_up_bn"
  type: "BatchNorm"
  bottom: "inception_resnet_v2_b3_up"
  top: "inception_resnet_v2_b3_up"
    param {
        lr_mult: 0.0
        decay_mult: 0.0
    }
    param {
        lr_mult: 0.0
        decay_mult: 0.0
    }
    param {
        lr_mult: 0.0
        decay_mult: 0.0
    }
	batch_norm_param {
        use_global_stats: false
        moving_average_fraction: 0.95
	}
}
layer {
  name: "inception_resnet_v2_b3_up_scale"
  type: "Scale"
  bottom: "inception_resnet_v2_b3_up"
  top: "inception_resnet_v2_b3_up"
  scale_param {
    bias_term: true
  }
}
layer {
  name: "inception_resnet_v2_b3_residual_eltwise"
  type: "Eltwise"
  bottom: "inception_resnet_v2_b2_residual_eltwise"
  bottom: "inception_resnet_v2_b3_up"
  top: "inception_resnet_v2_b3_residual_eltwise"
  eltwise_param {
    operation: SUM
  }
}
layer {
  name: "inception_resnet_v2_b3_residual_eltwise_relu"
  type: "PReLU"
  bottom: "inception_resnet_v2_b3_residual_eltwise"
  top: "inception_resnet_v2_b3_residual_eltwise"
}
layer {
  name: "inception_resnet_v2_b4_1x1"
  type: "Convolution"
  bottom: "inception_resnet_v2_b3_residual_eltwise"
  top: "inception_resnet_v2_b4_1x1"
  param {
    lr_mult: 1
    decay_mult: 1
  }
  param {
    lr_mult: 2
    decay_mult: 0
  }
  convolution_param {
    num_output: 192
    pad: 0
    kernel_size: 1
    stride: 1
    weight_filler {
      type: "xavier"
      std: 0.01
    }
    bias_filler {
      type: "constant"
      value: 0.2
    }
  }
}
layer {
  name: "inception_resnet_v2_b4_1x1_bn"
  type: "BatchNorm"
  bottom: "inception_resnet_v2_b4_1x1"
  top: "inception_resnet_v2_b4_1x1"
    param {
        lr_mult: 0.0
        decay_mult: 0.0
    }
    param {
        lr_mult: 0.0
        decay_mult: 0.0
    }
    param {
        lr_mult: 0.0
        decay_mult: 0.0
    }
	batch_norm_param {
        use_global_stats: false
        moving_average_fraction: 0.95
	}
}
layer {
  name: "inception_resnet_v2_b4_1x1_scale"
  type: "Scale"
  bottom: "inception_resnet_v2_b4_1x1"
  top: "inception_resnet_v2_b4_1x1"
  scale_param {
    bias_term: true
  }
}
layer {
  name: "inception_resnet_v2_b4_1x1_relu"
  type: "PReLU"
  bottom: "inception_resnet_v2_b4_1x1"
  top: "inception_resnet_v2_b4_1x1"
}
layer {
  name: "inception_resnet_v2_b4_1x7_reduce"
  type: "Convolution"
  bottom: "inception_resnet_v2_b3_residual_eltwise"
  top: "inception_resnet_v2_b4_1x7_reduce"
  param {
    lr_mult: 1
    decay_mult: 1
  }
  param {
    lr_mult: 2
    decay_mult: 0
  }
  convolution_param {
    num_output: 128
    pad: 0
    kernel_size: 1
    stride: 1
    weight_filler {
      type: "xavier"
      std: 0.01
    }
    bias_filler {
      type: "constant"
      value: 0.2
    }
  }
}
layer {
  name: "inception_resnet_v2_b4_1x7_reduce_bn"
  type: "BatchNorm"
  bottom: "inception_resnet_v2_b4_1x7_reduce"
  top: "inception_resnet_v2_b4_1x7_reduce"
    param {
        lr_mult: 0.0
        decay_mult: 0.0
    }
    param {
        lr_mult: 0.0
        decay_mult: 0.0
    }
    param {
        lr_mult: 0.0
        decay_mult: 0.0
    }
	batch_norm_param {
        use_global_stats: false
        moving_average_fraction: 0.95
	}
}
layer {
  name: "inception_resnet_v2_b4_1x7_reduce_scale"
  type: "Scale"
  bottom: "inception_resnet_v2_b4_1x7_reduce"
  top: "inception_resnet_v2_b4_1x7_reduce"
  scale_param {
    bias_term: true
  }
}
layer {
  name: "inception_resnet_v2_b4_1x7_reduce_relu"
  type: "PReLU"
  bottom: "inception_resnet_v2_b4_1x7_reduce"
  top: "inception_resnet_v2_b4_1x7_reduce"
}
layer {
  name: "inception_resnet_v2_b4_1x7"
  type: "Convolution"
  bottom: "inception_resnet_v2_b4_1x7_reduce"
  top: "inception_resnet_v2_b4_1x7"
  param {
    lr_mult: 1
    decay_mult: 1
  }
  param {
    lr_mult: 2
    decay_mult: 0
  }
  convolution_param {
    num_output: 160
    stride: 1
    weight_filler {
      type: "xavier"
      std: 0.01
    }
    bias_filler {
      type: "constant"
      value: 0.2
    }
    pad_h: 0
    pad_w: 3
    kernel_h: 1
    kernel_w: 7
  }
}
layer {
  name: "inception_resnet_v2_b4_1x7_bn"
  type: "BatchNorm"
  bottom: "inception_resnet_v2_b4_1x7"
  top: "inception_resnet_v2_b4_1x7"
    param {
        lr_mult: 0.0
        decay_mult: 0.0
    }
    param {
        lr_mult: 0.0
        decay_mult: 0.0
    }
    param {
        lr_mult: 0.0
        decay_mult: 0.0
    }
	batch_norm_param {
        use_global_stats: false
        moving_average_fraction: 0.95
	}
}
layer {
  name: "inception_resnet_v2_b4_1x7_scale"
  type: "Scale"
  bottom: "inception_resnet_v2_b4_1x7"
  top: "inception_resnet_v2_b4_1x7"
  scale_param {
    bias_term: true
  }
}
layer {
  name: "inception_resnet_v2_b4_1x7_relu"
  type: "PReLU"
  bottom: "inception_resnet_v2_b4_1x7"
  top: "inception_resnet_v2_b4_1x7"
}
layer {
  name: "inception_resnet_v2_b4_7x1"
  type: "Convolution"
  bottom: "inception_resnet_v2_b4_1x7"
  top: "inception_resnet_v2_b4_7x1"
  param {
    lr_mult: 1
    decay_mult: 1
  }
  param {
    lr_mult: 2
    decay_mult: 0
  }
  convolution_param {
    num_output: 192
    stride: 1
    weight_filler {
      type: "xavier"
      std: 0.01
    }
    bias_filler {
      type: "constant"
      value: 0.2
    }
    pad_h: 3
    pad_w: 0
    kernel_h: 7
    kernel_w: 1
  }
}
layer {
  name: "inception_resnet_v2_b4_7x1_bn"
  type: "BatchNorm"
  bottom: "inception_resnet_v2_b4_7x1"
  top: "inception_resnet_v2_b4_7x1"
    param {
        lr_mult: 0.0
        decay_mult: 0.0
    }
    param {
        lr_mult: 0.0
        decay_mult: 0.0
    }
    param {
        lr_mult: 0.0
        decay_mult: 0.0
    }
	batch_norm_param {
        use_global_stats: false
        moving_average_fraction: 0.95
	}
}
layer {
  name: "inception_resnet_v2_b4_7x1_scale"
  type: "Scale"
  bottom: "inception_resnet_v2_b4_7x1"
  top: "inception_resnet_v2_b4_7x1"
  scale_param {
    bias_term: true
  }
}
layer {
  name: "inception_resnet_v2_b4_7x1_relu"
  type: "PReLU"
  bottom: "inception_resnet_v2_b4_7x1"
  top: "inception_resnet_v2_b4_7x1"
}
layer {
  name: "inception_resnet_v2_b4_concat"
  type: "Concat"
  bottom: "inception_resnet_v2_b4_1x1"
  bottom: "inception_resnet_v2_b4_7x1"
  top: "inception_resnet_v2_b4_concat"
}
layer {
  name: "inception_resnet_v2_b4_up"
  type: "Convolution"
  bottom: "inception_resnet_v2_b4_concat"
  top: "inception_resnet_v2_b4_up"
  param {
    lr_mult: 1
    decay_mult: 1
  }
  param {
    lr_mult: 2
    decay_mult: 0
  }
  convolution_param {
    num_output: 1088
    pad: 0
    kernel_size: 1
    stride: 1
    weight_filler {
      type: "xavier"
      std: 0.01
    }
    bias_filler {
      type: "constant"
      value: 0.2
    }
  }
}
layer {
  name: "inception_resnet_v2_b4_up_bn"
  type: "BatchNorm"
  bottom: "inception_resnet_v2_b4_up"
  top: "inception_resnet_v2_b4_up"
    param {
        lr_mult: 0.0
        decay_mult: 0.0
    }
    param {
        lr_mult: 0.0
        decay_mult: 0.0
    }
    param {
        lr_mult: 0.0
        decay_mult: 0.0
    }
	batch_norm_param {
        use_global_stats: false
        moving_average_fraction: 0.95
	}
}
layer {
  name: "inception_resnet_v2_b4_up_scale"
  type: "Scale"
  bottom: "inception_resnet_v2_b4_up"
  top: "inception_resnet_v2_b4_up"
  scale_param {
    bias_term: true
  }
}
layer {
  name: "inception_resnet_v2_b4_residual_eltwise"
  type: "Eltwise"
  bottom: "inception_resnet_v2_b3_residual_eltwise"
  bottom: "inception_resnet_v2_b4_up"
  top: "inception_resnet_v2_b4_residual_eltwise"
  eltwise_param {
    operation: SUM
  }
}
layer {
  name: "inception_resnet_v2_b4_residual_eltwise_relu"
  type: "PReLU"
  bottom: "inception_resnet_v2_b4_residual_eltwise"
  top: "inception_resnet_v2_b4_residual_eltwise"
}
layer {
  name: "inception_resnet_v2_b5_1x1"
  type: "Convolution"
  bottom: "inception_resnet_v2_b4_residual_eltwise"
  top: "inception_resnet_v2_b5_1x1"
  param {
    lr_mult: 1
    decay_mult: 1
  }
  param {
    lr_mult: 2
    decay_mult: 0
  }
  convolution_param {
    num_output: 192
    pad: 0
    kernel_size: 1
    stride: 1
    weight_filler {
      type: "xavier"
      std: 0.01
    }
    bias_filler {
      type: "constant"
      value: 0.2
    }
  }
}
layer {
  name: "inception_resnet_v2_b5_1x1_bn"
  type: "BatchNorm"
  bottom: "inception_resnet_v2_b5_1x1"
  top: "inception_resnet_v2_b5_1x1"
    param {
        lr_mult: 0.0
        decay_mult: 0.0
    }
    param {
        lr_mult: 0.0
        decay_mult: 0.0
    }
    param {
        lr_mult: 0.0
        decay_mult: 0.0
    }
	batch_norm_param {
        use_global_stats: false
        moving_average_fraction: 0.95
	}
}
layer {
  name: "inception_resnet_v2_b5_1x1_scale"
  type: "Scale"
  bottom: "inception_resnet_v2_b5_1x1"
  top: "inception_resnet_v2_b5_1x1"
  scale_param {
    bias_term: true
  }
}
layer {
  name: "inception_resnet_v2_b5_1x1_relu"
  type: "PReLU"
  bottom: "inception_resnet_v2_b5_1x1"
  top: "inception_resnet_v2_b5_1x1"
}
layer {
  name: "inception_resnet_v2_b5_1x7_reduce"
  type: "Convolution"
  bottom: "inception_resnet_v2_b4_residual_eltwise"
  top: "inception_resnet_v2_b5_1x7_reduce"
  param {
    lr_mult: 1
    decay_mult: 1
  }
  param {
    lr_mult: 2
    decay_mult: 0
  }
  convolution_param {
    num_output: 128
    pad: 0
    kernel_size: 1
    stride: 1
    weight_filler {
      type: "xavier"
      std: 0.01
    }
    bias_filler {
      type: "constant"
      value: 0.2
    }
  }
}
layer {
  name: "inception_resnet_v2_b5_1x7_reduce_bn"
  type: "BatchNorm"
  bottom: "inception_resnet_v2_b5_1x7_reduce"
  top: "inception_resnet_v2_b5_1x7_reduce"
    param {
        lr_mult: 0.0
        decay_mult: 0.0
    }
    param {
        lr_mult: 0.0
        decay_mult: 0.0
    }
    param {
        lr_mult: 0.0
        decay_mult: 0.0
    }
	batch_norm_param {
        use_global_stats: false
        moving_average_fraction: 0.95
	}
}
layer {
  name: "inception_resnet_v2_b5_1x7_reduce_scale"
  type: "Scale"
  bottom: "inception_resnet_v2_b5_1x7_reduce"
  top: "inception_resnet_v2_b5_1x7_reduce"
  scale_param {
    bias_term: true
  }
}
layer {
  name: "inception_resnet_v2_b5_1x7_reduce_relu"
  type: "PReLU"
  bottom: "inception_resnet_v2_b5_1x7_reduce"
  top: "inception_resnet_v2_b5_1x7_reduce"
}
layer {
  name: "inception_resnet_v2_b5_1x7"
  type: "Convolution"
  bottom: "inception_resnet_v2_b5_1x7_reduce"
  top: "inception_resnet_v2_b5_1x7"
  param {
    lr_mult: 1
    decay_mult: 1
  }
  param {
    lr_mult: 2
    decay_mult: 0
  }
  convolution_param {
    num_output: 160
    stride: 1
    weight_filler {
      type: "xavier"
      std: 0.01
    }
    bias_filler {
      type: "constant"
      value: 0.2
    }
    pad_h: 0
    pad_w: 3
    kernel_h: 1
    kernel_w: 7
  }
}
layer {
  name: "inception_resnet_v2_b5_1x7_bn"
  type: "BatchNorm"
  bottom: "inception_resnet_v2_b5_1x7"
  top: "inception_resnet_v2_b5_1x7"
    param {
        lr_mult: 0.0
        decay_mult: 0.0
    }
    param {
        lr_mult: 0.0
        decay_mult: 0.0
    }
    param {
        lr_mult: 0.0
        decay_mult: 0.0
    }
	batch_norm_param {
        use_global_stats: false
        moving_average_fraction: 0.95
	}
}
layer {
  name: "inception_resnet_v2_b5_1x7_scale"
  type: "Scale"
  bottom: "inception_resnet_v2_b5_1x7"
  top: "inception_resnet_v2_b5_1x7"
  scale_param {
    bias_term: true
  }
}
layer {
  name: "inception_resnet_v2_b5_1x7_relu"
  type: "PReLU"
  bottom: "inception_resnet_v2_b5_1x7"
  top: "inception_resnet_v2_b5_1x7"
}
layer {
  name: "inception_resnet_v2_b5_7x1"
  type: "Convolution"
  bottom: "inception_resnet_v2_b5_1x7"
  top: "inception_resnet_v2_b5_7x1"
  param {
    lr_mult: 1
    decay_mult: 1
  }
  param {
    lr_mult: 2
    decay_mult: 0
  }
  convolution_param {
    num_output: 192
    stride: 1
    weight_filler {
      type: "xavier"
      std: 0.01
    }
    bias_filler {
      type: "constant"
      value: 0.2
    }
    pad_h: 3
    pad_w: 0
    kernel_h: 7
    kernel_w: 1
  }
}
layer {
  name: "inception_resnet_v2_b5_7x1_bn"
  type: "BatchNorm"
  bottom: "inception_resnet_v2_b5_7x1"
  top: "inception_resnet_v2_b5_7x1"
    param {
        lr_mult: 0.0
        decay_mult: 0.0
    }
    param {
        lr_mult: 0.0
        decay_mult: 0.0
    }
    param {
        lr_mult: 0.0
        decay_mult: 0.0
    }
	batch_norm_param {
        use_global_stats: false
        moving_average_fraction: 0.95
	}
}
layer {
  name: "inception_resnet_v2_b5_7x1_scale"
  type: "Scale"
  bottom: "inception_resnet_v2_b5_7x1"
  top: "inception_resnet_v2_b5_7x1"
  scale_param {
    bias_term: true
  }
}
layer {
  name: "inception_resnet_v2_b5_7x1_relu"
  type: "PReLU"
  bottom: "inception_resnet_v2_b5_7x1"
  top: "inception_resnet_v2_b5_7x1"
}
layer {
  name: "inception_resnet_v2_b5_concat"
  type: "Concat"
  bottom: "inception_resnet_v2_b5_1x1"
  bottom: "inception_resnet_v2_b5_7x1"
  top: "inception_resnet_v2_b5_concat"
}
layer {
  name: "inception_resnet_v2_b5_up"
  type: "Convolution"
  bottom: "inception_resnet_v2_b5_concat"
  top: "inception_resnet_v2_b5_up"
  param {
    lr_mult: 1
    decay_mult: 1
  }
  param {
    lr_mult: 2
    decay_mult: 0
  }
  convolution_param {
    num_output: 1088
    pad: 0
    kernel_size: 1
    stride: 1
    weight_filler {
      type: "xavier"
      std: 0.01
    }
    bias_filler {
      type: "constant"
      value: 0.2
    }
  }
}
layer {
  name: "inception_resnet_v2_b5_up_bn"
  type: "BatchNorm"
  bottom: "inception_resnet_v2_b5_up"
  top: "inception_resnet_v2_b5_up"
    param {
        lr_mult: 0.0
        decay_mult: 0.0
    }
    param {
        lr_mult: 0.0
        decay_mult: 0.0
    }
    param {
        lr_mult: 0.0
        decay_mult: 0.0
    }
	batch_norm_param {
        use_global_stats: false
        moving_average_fraction: 0.95
	}
}
layer {
  name: "inception_resnet_v2_b5_up_scale"
  type: "Scale"
  bottom: "inception_resnet_v2_b5_up"
  top: "inception_resnet_v2_b5_up"
  scale_param {
    bias_term: true
  }
}
layer {
  name: "inception_resnet_v2_b5_residual_eltwise"
  type: "Eltwise"
  bottom: "inception_resnet_v2_b4_residual_eltwise"
  bottom: "inception_resnet_v2_b5_up"
  top: "inception_resnet_v2_b5_residual_eltwise"
  eltwise_param {
    operation: SUM
  }
}
layer {
  name: "inception_resnet_v2_b5_residual_eltwise_relu"
  type: "PReLU"
  bottom: "inception_resnet_v2_b5_residual_eltwise"
  top: "inception_resnet_v2_b5_residual_eltwise"
}

layer {
  name: "reduction_b_3x3_reduce"
  type: "Convolution"
  bottom: "inception_resnet_v2_b5_residual_eltwise"
  top: "reduction_b_3x3_reduce"
  param {
    lr_mult: 1
    decay_mult: 1
  }
  param {
    lr_mult: 2
    decay_mult: 0
  }
  convolution_param {
    num_output: 256
    pad: 0
    kernel_size: 1
    stride: 1
    weight_filler {
      type: "xavier"
      std: 0.01
    }
    bias_filler {
      type: "constant"
      value: 0.2
    }
  }
}
layer {
  name: "reduction_b_3x3_reduce_bn"
  type: "BatchNorm"
  bottom: "reduction_b_3x3_reduce"
  top: "reduction_b_3x3_reduce"
    param {
        lr_mult: 0.0
        decay_mult: 0.0
    }
    param {
        lr_mult: 0.0
        decay_mult: 0.0
    }
    param {
        lr_mult: 0.0
        decay_mult: 0.0
    }
	batch_norm_param {
        use_global_stats: false
        moving_average_fraction: 0.95
	}
}
layer {
  name: "reduction_b_3x3_reduce_scale"
  type: "Scale"
  bottom: "reduction_b_3x3_reduce"
  top: "reduction_b_3x3_reduce"
  scale_param {
    bias_term: true
  }
}
layer {
  name: "reduction_b_3x3_reduce_relu"
  type: "PReLU"
  bottom: "reduction_b_3x3_reduce"
  top: "reduction_b_3x3_reduce"
}
layer {
  name: "reduction_b_3x3"
  type: "Convolution"
  bottom: "reduction_b_3x3_reduce"
  top: "reduction_b_3x3"
  param {
    lr_mult: 1
    decay_mult: 1
  }
  param {
    lr_mult: 2
    decay_mult: 0
  }
  convolution_param {
    num_output: 384
    pad: 0
    kernel_size: 3
    stride: 2
    weight_filler {
      type: "xavier"
      std: 0.01
    }
    bias_filler {
      type: "constant"
      value: 0.2
    }
  }
}
layer {
  name: "reduction_b_3x3_bn"
  type: "BatchNorm"
  bottom: "reduction_b_3x3"
  top: "reduction_b_3x3"
    param {
        lr_mult: 0.0
        decay_mult: 0.0
    }
    param {
        lr_mult: 0.0
        decay_mult: 0.0
    }
    param {
        lr_mult: 0.0
        decay_mult: 0.0
    }
	batch_norm_param {
        use_global_stats: false
        moving_average_fraction: 0.95
	}
}
layer {
  name: "reduction_b_3x3_scale"
  type: "Scale"
  bottom: "reduction_b_3x3"
  top: "reduction_b_3x3"
  scale_param {
    bias_term: true
  }
}
layer {
  name: "reduction_b_3x3_relu"
  type: "PReLU"
  bottom: "reduction_b_3x3"
  top: "reduction_b_3x3"
}
layer {
  name: "reduction_b_3x3_2_reduce"
  type: "Convolution"
  bottom: "inception_resnet_v2_b5_residual_eltwise"
  top: "reduction_b_3x3_2_reduce"
  param {
    lr_mult: 1
    decay_mult: 1
  }
  param {
    lr_mult: 2
    decay_mult: 0
  }
  convolution_param {
    num_output: 256
    pad: 0
    kernel_size: 1
    stride: 1
    weight_filler {
      type: "xavier"
      std: 0.01
    }
    bias_filler {
      type: "constant"
      value: 0.2
    }
  }
}
layer {
  name: "reduction_b_3x3_2_reduce_bn"
  type: "BatchNorm"
  bottom: "reduction_b_3x3_2_reduce"
  top: "reduction_b_3x3_2_reduce"
    param {
        lr_mult: 0.0
        decay_mult: 0.0
    }
    param {
        lr_mult: 0.0
        decay_mult: 0.0
    }
    param {
        lr_mult: 0.0
        decay_mult: 0.0
    }
	batch_norm_param {
        use_global_stats: false
        moving_average_fraction: 0.95
	}
}
layer {
  name: "reduction_b_3x3_2_reduce_scale"
  type: "Scale"
  bottom: "reduction_b_3x3_2_reduce"
  top: "reduction_b_3x3_2_reduce"
  scale_param {
    bias_term: true
  }
}
layer {
  name: "reduction_b_3x3_2_reduce_relu"
  type: "PReLU"
  bottom: "reduction_b_3x3_2_reduce"
  top: "reduction_b_3x3_2_reduce"
}
layer {
  name: "reduction_b_3x3_2"
  type: "Convolution"
  bottom: "reduction_b_3x3_2_reduce"
  top: "reduction_b_3x3_2"
  param {
    lr_mult: 1
    decay_mult: 1
  }
  param {
    lr_mult: 2
    decay_mult: 0
  }
  convolution_param {
    num_output: 288
    pad: 0
    kernel_size: 3
    stride: 2
    weight_filler {
      type: "xavier"
      std: 0.01
    }
    bias_filler {
      type: "constant"
      value: 0.2
    }
  }
}
layer {
  name: "reduction_b_3x3_2_bn"
  type: "BatchNorm"
  bottom: "reduction_b_3x3_2"
  top: "reduction_b_3x3_2"
    param {
        lr_mult: 0.0
        decay_mult: 0.0
    }
    param {
        lr_mult: 0.0
        decay_mult: 0.0
    }
    param {
        lr_mult: 0.0
        decay_mult: 0.0
    }
	batch_norm_param {
        use_global_stats: false
        moving_average_fraction: 0.95
	}
}
layer {
  name: "reduction_b_3x3_2_scale"
  type: "Scale"
  bottom: "reduction_b_3x3_2"
  top: "reduction_b_3x3_2"
  scale_param {
    bias_term: true
  }
}
layer {
  name: "reduction_b_3x3_2_relu"
  type: "PReLU"
  bottom: "reduction_b_3x3_2"
  top: "reduction_b_3x3_2"
}
layer {
  name: "reduction_b_3x3_3_reduce"
  type: "Convolution"
  bottom: "inception_resnet_v2_b5_residual_eltwise"
  top: "reduction_b_3x3_3_reduce"
  param {
    lr_mult: 1
    decay_mult: 1
  }
  param {
    lr_mult: 2
    decay_mult: 0
  }
  convolution_param {
    num_output: 256
    pad: 0
    kernel_size: 1
    stride: 1
    weight_filler {
      type: "xavier"
      std: 0.01
    }
    bias_filler {
      type: "constant"
      value: 0.2
    }
  }
}
layer {
  name: "reduction_b_3x3_3_reduce_bn"
  type: "BatchNorm"
  bottom: "reduction_b_3x3_3_reduce"
  top: "reduction_b_3x3_3_reduce"
    param {
        lr_mult: 0.0
        decay_mult: 0.0
    }
    param {
        lr_mult: 0.0
        decay_mult: 0.0
    }
    param {
        lr_mult: 0.0
        decay_mult: 0.0
    }
	batch_norm_param {
        use_global_stats: false
        moving_average_fraction: 0.95
	}
}
layer {
  name: "reduction_b_3x3_3_reduce_scale"
  type: "Scale"
  bottom: "reduction_b_3x3_3_reduce"
  top: "reduction_b_3x3_3_reduce"
  scale_param {
    bias_term: true
  }
}
layer {
  name: "reduction_b_3x3_3_reduce_relu"
  type: "PReLU"
  bottom: "reduction_b_3x3_3_reduce"
  top: "reduction_b_3x3_3_reduce"
}
layer {
  name: "reduction_b_3x3_3"
  type: "Convolution"
  bottom: "reduction_b_3x3_3_reduce"
  top: "reduction_b_3x3_3"
  param {
    lr_mult: 1
    decay_mult: 1
  }
  param {
    lr_mult: 2
    decay_mult: 0
  }
  convolution_param {
    num_output: 288
    pad: 1
    kernel_size: 3
    stride: 1
    weight_filler {
      type: "xavier"
      std: 0.01
    }
    bias_filler {
      type: "constant"
      value: 0.2
    }
  }
}
layer {
  name: "reduction_b_3x3_3_bn"
  type: "BatchNorm"
  bottom: "reduction_b_3x3_3"
  top: "reduction_b_3x3_3"
    param {
        lr_mult: 0.0
        decay_mult: 0.0
    }
    param {
        lr_mult: 0.0
        decay_mult: 0.0
    }
    param {
        lr_mult: 0.0
        decay_mult: 0.0
    }
	batch_norm_param {
        use_global_stats: false
        moving_average_fraction: 0.95
	}
}
layer {
  name: "reduction_b_3x3_3_scale"
  type: "Scale"
  bottom: "reduction_b_3x3_3"
  top: "reduction_b_3x3_3"
  scale_param {
    bias_term: true
  }
}
layer {
  name: "reduction_b_3x3_3_relu"
  type: "PReLU"
  bottom: "reduction_b_3x3_3"
  top: "reduction_b_3x3_3"
}
layer {
  name: "reduction_b_3x3_4"
  type: "Convolution"
  bottom: "reduction_b_3x3_3"
  top: "reduction_b_3x3_4"
  param {
    lr_mult: 1
    decay_mult: 1
  }
  param {
    lr_mult: 2
    decay_mult: 0
  }
  convolution_param {
    num_output: 320
    pad: 0
    kernel_size: 3
    stride: 2
    weight_filler {
      type: "xavier"
      std: 0.01
    }
    bias_filler {
      type: "constant"
      value: 0.2
    }
  }
}
layer {
  name: "reduction_b_3x3_4_bn"
  type: "BatchNorm"
  bottom: "reduction_b_3x3_4"
  top: "reduction_b_3x3_4"
    param {
        lr_mult: 0.0
        decay_mult: 0.0
    }
    param {
        lr_mult: 0.0
        decay_mult: 0.0
    }
    param {
        lr_mult: 0.0
        decay_mult: 0.0
    }
	batch_norm_param {
        use_global_stats: false
        moving_average_fraction: 0.95
	}
}
layer {
  name: "reduction_b_3x3_4_scale"
  type: "Scale"
  bottom: "reduction_b_3x3_4"
  top: "reduction_b_3x3_4"
  scale_param {
    bias_term: true
  }
}
layer {
  name: "reduction_b_3x3_4_relu"
  type: "PReLU"
  bottom: "reduction_b_3x3_4"
  top: "reduction_b_3x3_4"
}
layer {
  name: "reduction_b_pool"
  type: "Pooling"
  bottom: "inception_resnet_v2_b5_residual_eltwise"
  top: "reduction_b_pool"
  pooling_param {
    pool: MAX
    kernel_size: 3
    stride: 2
  }
}
layer {
  name: "reduction_b_concat"
  type: "Concat"
  bottom: "reduction_b_3x3"
  bottom: "reduction_b_3x3_2"
  bottom: "reduction_b_3x3_4"
  bottom: "reduction_b_pool"
  top: "reduction_b_concat"
}
layer {
  name: "inception_resnet_v2_c1_1x1"
  type: "Convolution"
  bottom: "reduction_b_concat"
  top: "inception_resnet_v2_c1_1x1"
  param {
    lr_mult: 1
    decay_mult: 1
  }
  param {
    lr_mult: 2
    decay_mult: 0
  }
  convolution_param {
    num_output: 192
    pad: 0
    kernel_size: 1
    stride: 1
    weight_filler {
      type: "xavier"
      std: 0.01
    }
    bias_filler {
      type: "constant"
      value: 0.2
    }
  }
}
layer {
  name: "inception_resnet_v2_c1_1x1_bn"
  type: "BatchNorm"
  bottom: "inception_resnet_v2_c1_1x1"
  top: "inception_resnet_v2_c1_1x1"
    param {
        lr_mult: 0.0
        decay_mult: 0.0
    }
    param {
        lr_mult: 0.0
        decay_mult: 0.0
    }
    param {
        lr_mult: 0.0
        decay_mult: 0.0
    }
	batch_norm_param {
        use_global_stats: false
        moving_average_fraction: 0.95
	}
}
layer {
  name: "inception_resnet_v2_c1_1x1_scale"
  type: "Scale"
  bottom: "inception_resnet_v2_c1_1x1"
  top: "inception_resnet_v2_c1_1x1"
  scale_param {
    bias_term: true
  }
}
layer {
  name: "inception_resnet_v2_c1_1x1_relu"
  type: "PReLU"
  bottom: "inception_resnet_v2_c1_1x1"
  top: "inception_resnet_v2_c1_1x1"
}
layer {
  name: "inception_resnet_v2_c1_1x3_reduce"
  type: "Convolution"
  bottom: "reduction_b_concat"
  top: "inception_resnet_v2_c1_1x3_reduce"
  param {
    lr_mult: 1
    decay_mult: 1
  }
  param {
    lr_mult: 2
    decay_mult: 0
  }
  convolution_param {
    num_output: 192
    pad: 0
    kernel_size: 1
    stride: 1
    weight_filler {
      type: "xavier"
      std: 0.01
    }
    bias_filler {
      type: "constant"
      value: 0.2
    }
  }
}
layer {
  name: "inception_resnet_v2_c1_1x3_reduce_bn"
  type: "BatchNorm"
  bottom: "inception_resnet_v2_c1_1x3_reduce"
  top: "inception_resnet_v2_c1_1x3_reduce"
    param {
        lr_mult: 0.0
        decay_mult: 0.0
    }
    param {
        lr_mult: 0.0
        decay_mult: 0.0
    }
    param {
        lr_mult: 0.0
        decay_mult: 0.0
    }
	batch_norm_param {
        use_global_stats: false
        moving_average_fraction: 0.95
	}
}
layer {
  name: "inception_resnet_v2_c1_1x3_reduce_scale"
  type: "Scale"
  bottom: "inception_resnet_v2_c1_1x3_reduce"
  top: "inception_resnet_v2_c1_1x3_reduce"
  scale_param {
    bias_term: true
  }
}
layer {
  name: "inception_resnet_v2_c1_1x3_reduce_relu"
  type: "PReLU"
  bottom: "inception_resnet_v2_c1_1x3_reduce"
  top: "inception_resnet_v2_c1_1x3_reduce"
}
layer {
  name: "inception_resnet_v2_c1_1x3"
  type: "Convolution"
  bottom: "inception_resnet_v2_c1_1x3_reduce"
  top: "inception_resnet_v2_c1_1x3"
  param {
    lr_mult: 1
    decay_mult: 1
  }
  param {
    lr_mult: 2
    decay_mult: 0
  }
  convolution_param {
    num_output: 224
    stride: 1
    weight_filler {
      type: "xavier"
      std: 0.01
    }
    bias_filler {
      type: "constant"
      value: 0.2
    }
    pad_h: 0
    pad_w: 1
    kernel_h: 1
    kernel_w: 3
  }
}
layer {
  name: "inception_resnet_v2_c1_1x3_bn"
  type: "BatchNorm"
  bottom: "inception_resnet_v2_c1_1x3"
  top: "inception_resnet_v2_c1_1x3"
    param {
        lr_mult: 0.0
        decay_mult: 0.0
    }
    param {
        lr_mult: 0.0
        decay_mult: 0.0
    }
    param {
        lr_mult: 0.0
        decay_mult: 0.0
    }
	batch_norm_param {
        use_global_stats: false
        moving_average_fraction: 0.95
	}
}
layer {
  name: "inception_resnet_v2_c1_1x3_scale"
  type: "Scale"
  bottom: "inception_resnet_v2_c1_1x3"
  top: "inception_resnet_v2_c1_1x3"
  scale_param {
    bias_term: true
  }
}
layer {
  name: "inception_resnet_v2_c1_1x3_relu"
  type: "PReLU"
  bottom: "inception_resnet_v2_c1_1x3"
  top: "inception_resnet_v2_c1_1x3"
}
layer {
  name: "inception_resnet_v2_c1_3x1"
  type: "Convolution"
  bottom: "inception_resnet_v2_c1_1x3"
  top: "inception_resnet_v2_c1_3x1"
  param {
    lr_mult: 1
    decay_mult: 1
  }
  param {
    lr_mult: 2
    decay_mult: 0
  }
  convolution_param {
    num_output: 256
    stride: 1
    weight_filler {
      type: "xavier"
      std: 0.01
    }
    bias_filler {
      type: "constant"
      value: 0.2
    }
    pad_h: 1
    pad_w: 0
    kernel_h: 3
    kernel_w: 1
  }
}
layer {
  name: "inception_resnet_v2_c1_3x1_bn"
  type: "BatchNorm"
  bottom: "inception_resnet_v2_c1_3x1"
  top: "inception_resnet_v2_c1_3x1"
    param {
        lr_mult: 0.0
        decay_mult: 0.0
    }
    param {
        lr_mult: 0.0
        decay_mult: 0.0
    }
    param {
        lr_mult: 0.0
        decay_mult: 0.0
    }
	batch_norm_param {
        use_global_stats: false
        moving_average_fraction: 0.95
	}
}
layer {
  name: "inception_resnet_v2_c1_3x1_scale"
  type: "Scale"
  bottom: "inception_resnet_v2_c1_3x1"
  top: "inception_resnet_v2_c1_3x1"
  scale_param {
    bias_term: true
  }
}
layer {
  name: "inception_resnet_v2_c1_3x1_relu"
  type: "PReLU"
  bottom: "inception_resnet_v2_c1_3x1"
  top: "inception_resnet_v2_c1_3x1"
}
layer {
  name: "inception_resnet_v2_c1_concat"
  type: "Concat"
  bottom: "inception_resnet_v2_c1_1x1"
  bottom: "inception_resnet_v2_c1_3x1"
  top: "inception_resnet_v2_c1_concat"
}
layer {
  name: "inception_resnet_v2_c1_up"
  type: "Convolution"
  bottom: "inception_resnet_v2_c1_concat"
  top: "inception_resnet_v2_c1_up"
  param {
    lr_mult: 1
    decay_mult: 1
  }
  param {
    lr_mult: 2
    decay_mult: 0
  }
  convolution_param {
    num_output: 2080
    pad: 0
    kernel_size: 1
    stride: 1
    weight_filler {
      type: "xavier"
      std: 0.01
    }
    bias_filler {
      type: "constant"
      value: 0.2
    }
  }
}
layer {
  name: "inception_resnet_v2_c1_up_bn"
  type: "BatchNorm"
  bottom: "inception_resnet_v2_c1_up"
  top: "inception_resnet_v2_c1_up"
    param {
        lr_mult: 0.0
        decay_mult: 0.0
    }
    param {
        lr_mult: 0.0
        decay_mult: 0.0
    }
    param {
        lr_mult: 0.0
        decay_mult: 0.0
    }
	batch_norm_param {
        use_global_stats: false
        moving_average_fraction: 0.95
	}
}
layer {
  name: "inception_resnet_v2_c1_up_scale"
  type: "Scale"
  bottom: "inception_resnet_v2_c1_up"
  top: "inception_resnet_v2_c1_up"
  scale_param {
    bias_term: true
  }
}
layer {
  name: "inception_resnet_v2_c1_residual_eltwise"
  type: "Eltwise"
  bottom: "reduction_b_concat"
  bottom: "inception_resnet_v2_c1_up"
  top: "inception_resnet_v2_c1_residual_eltwise"
  eltwise_param {
    operation: SUM
  }
}
layer {
  name: "inception_resnet_v2_c1_residual_eltwise_relu"
  type: "PReLU"
  bottom: "inception_resnet_v2_c1_residual_eltwise"
  top: "inception_resnet_v2_c1_residual_eltwise"
}
layer {
  name: "inception_resnet_v2_c2_1x1"
  type: "Convolution"
  bottom: "inception_resnet_v2_c1_residual_eltwise"
  top: "inception_resnet_v2_c2_1x1"
  param {
    lr_mult: 1
    decay_mult: 1
  }
  param {
    lr_mult: 2
    decay_mult: 0
  }
  convolution_param {
    num_output: 192
    pad: 0
    kernel_size: 1
    stride: 1
    weight_filler {
      type: "xavier"
      std: 0.01
    }
    bias_filler {
      type: "constant"
      value: 0.2
    }
  }
}
layer {
  name: "inception_resnet_v2_c2_1x1_bn"
  type: "BatchNorm"
  bottom: "inception_resnet_v2_c2_1x1"
  top: "inception_resnet_v2_c2_1x1"
    param {
        lr_mult: 0.0
        decay_mult: 0.0
    }
    param {
        lr_mult: 0.0
        decay_mult: 0.0
    }
    param {
        lr_mult: 0.0
        decay_mult: 0.0
    }
	batch_norm_param {
        use_global_stats: false
        moving_average_fraction: 0.95
	}
}
layer {
  name: "inception_resnet_v2_c2_1x1_scale"
  type: "Scale"
  bottom: "inception_resnet_v2_c2_1x1"
  top: "inception_resnet_v2_c2_1x1"
  scale_param {
    bias_term: true
  }
}
layer {
  name: "inception_resnet_v2_c2_1x1_relu"
  type: "PReLU"
  bottom: "inception_resnet_v2_c2_1x1"
  top: "inception_resnet_v2_c2_1x1"
}
layer {
  name: "inception_resnet_v2_c2_1x3_reduce"
  type: "Convolution"
  bottom: "inception_resnet_v2_c1_residual_eltwise"
  top: "inception_resnet_v2_c2_1x3_reduce"
  param {
    lr_mult: 1
    decay_mult: 1
  }
  param {
    lr_mult: 2
    decay_mult: 0
  }
  convolution_param {
    num_output: 192
    pad: 0
    kernel_size: 1
    stride: 1
    weight_filler {
      type: "xavier"
      std: 0.01
    }
    bias_filler {
      type: "constant"
      value: 0.2
    }
  }
}
layer {
  name: "inception_resnet_v2_c2_1x3_reduce_bn"
  type: "BatchNorm"
  bottom: "inception_resnet_v2_c2_1x3_reduce"
  top: "inception_resnet_v2_c2_1x3_reduce"
    param {
        lr_mult: 0.0
        decay_mult: 0.0
    }
    param {
        lr_mult: 0.0
        decay_mult: 0.0
    }
    param {
        lr_mult: 0.0
        decay_mult: 0.0
    }
	batch_norm_param {
        use_global_stats: false
        moving_average_fraction: 0.95
	}
}
layer {
  name: "inception_resnet_v2_c2_1x3_reduce_scale"
  type: "Scale"
  bottom: "inception_resnet_v2_c2_1x3_reduce"
  top: "inception_resnet_v2_c2_1x3_reduce"
  scale_param {
    bias_term: true
  }
}
layer {
  name: "inception_resnet_v2_c2_1x3_reduce_relu"
  type: "PReLU"
  bottom: "inception_resnet_v2_c2_1x3_reduce"
  top: "inception_resnet_v2_c2_1x3_reduce"
}
layer {
  name: "inception_resnet_v2_c2_1x3"
  type: "Convolution"
  bottom: "inception_resnet_v2_c2_1x3_reduce"
  top: "inception_resnet_v2_c2_1x3"
  param {
    lr_mult: 1
    decay_mult: 1
  }
  param {
    lr_mult: 2
    decay_mult: 0
  }
  convolution_param {
    num_output: 224
    stride: 1
    weight_filler {
      type: "xavier"
      std: 0.01
    }
    bias_filler {
      type: "constant"
      value: 0.2
    }
    pad_h: 0
    pad_w: 1
    kernel_h: 1
    kernel_w: 3
  }
}
layer {
  name: "inception_resnet_v2_c2_1x3_bn"
  type: "BatchNorm"
  bottom: "inception_resnet_v2_c2_1x3"
  top: "inception_resnet_v2_c2_1x3"
    param {
        lr_mult: 0.0
        decay_mult: 0.0
    }
    param {
        lr_mult: 0.0
        decay_mult: 0.0
    }
    param {
        lr_mult: 0.0
        decay_mult: 0.0
    }
	batch_norm_param {
        use_global_stats: false
        moving_average_fraction: 0.95
	}
}
layer {
  name: "inception_resnet_v2_c2_1x3_scale"
  type: "Scale"
  bottom: "inception_resnet_v2_c2_1x3"
  top: "inception_resnet_v2_c2_1x3"
  scale_param {
    bias_term: true
  }
}
layer {
  name: "inception_resnet_v2_c2_1x3_relu"
  type: "PReLU"
  bottom: "inception_resnet_v2_c2_1x3"
  top: "inception_resnet_v2_c2_1x3"
}
layer {
  name: "inception_resnet_v2_c2_3x1"
  type: "Convolution"
  bottom: "inception_resnet_v2_c2_1x3"
  top: "inception_resnet_v2_c2_3x1"
  param {
    lr_mult: 1
    decay_mult: 1
  }
  param {
    lr_mult: 2
    decay_mult: 0
  }
  convolution_param {
    num_output: 256
    stride: 1
    weight_filler {
      type: "xavier"
      std: 0.01
    }
    bias_filler {
      type: "constant"
      value: 0.2
    }
    pad_h: 1
    pad_w: 0
    kernel_h: 3
    kernel_w: 1
  }
}
layer {
  name: "inception_resnet_v2_c2_3x1_bn"
  type: "BatchNorm"
  bottom: "inception_resnet_v2_c2_3x1"
  top: "inception_resnet_v2_c2_3x1"
    param {
        lr_mult: 0.0
        decay_mult: 0.0
    }
    param {
        lr_mult: 0.0
        decay_mult: 0.0
    }
    param {
        lr_mult: 0.0
        decay_mult: 0.0
    }
	batch_norm_param {
        use_global_stats: false
        moving_average_fraction: 0.95
	}
}
layer {
  name: "inception_resnet_v2_c2_3x1_scale"
  type: "Scale"
  bottom: "inception_resnet_v2_c2_3x1"
  top: "inception_resnet_v2_c2_3x1"
  scale_param {
    bias_term: true
  }
}
layer {
  name: "inception_resnet_v2_c2_3x1_relu"
  type: "PReLU"
  bottom: "inception_resnet_v2_c2_3x1"
  top: "inception_resnet_v2_c2_3x1"
}
layer {
  name: "inception_resnet_v2_c2_concat"
  type: "Concat"
  bottom: "inception_resnet_v2_c2_1x1"
  bottom: "inception_resnet_v2_c2_3x1"
  top: "inception_resnet_v2_c2_concat"
}
layer {
  name: "inception_resnet_v2_c2_up"
  type: "Convolution"
  bottom: "inception_resnet_v2_c2_concat"
  top: "inception_resnet_v2_c2_up"
  param {
    lr_mult: 1
    decay_mult: 1
  }
  param {
    lr_mult: 2
    decay_mult: 0
  }
  convolution_param {
    num_output: 2080
    pad: 0
    kernel_size: 1
    stride: 1
    weight_filler {
      type: "xavier"
      std: 0.01
    }
    bias_filler {
      type: "constant"
      value: 0.2
    }
  }
}
layer {
  name: "inception_resnet_v2_c2_up_bn"
  type: "BatchNorm"
  bottom: "inception_resnet_v2_c2_up"
  top: "inception_resnet_v2_c2_up"
    param {
        lr_mult: 0.0
        decay_mult: 0.0
    }
    param {
        lr_mult: 0.0
        decay_mult: 0.0
    }
    param {
        lr_mult: 0.0
        decay_mult: 0.0
    }
	batch_norm_param {
        use_global_stats: false
        moving_average_fraction: 0.95
	}
}
layer {
  name: "inception_resnet_v2_c2_up_scale"
  type: "Scale"
  bottom: "inception_resnet_v2_c2_up"
  top: "inception_resnet_v2_c2_up"
  scale_param {
    bias_term: true
  }
}
layer {
  name: "inception_resnet_v2_c2_residual_eltwise"
  type: "Eltwise"
  bottom: "inception_resnet_v2_c1_residual_eltwise"
  bottom: "inception_resnet_v2_c2_up"
  top: "inception_resnet_v2_c2_residual_eltwise"
  eltwise_param {
    operation: SUM
  }
}
layer {
  name: "inception_resnet_v2_c2_residual_eltwise_relu"
  type: "PReLU"
  bottom: "inception_resnet_v2_c2_residual_eltwise"
  top: "inception_resnet_v2_c2_residual_eltwise"
}
layer {
  name: "inception_resnet_v2_c3_1x1"
  type: "Convolution"
  bottom: "inception_resnet_v2_c2_residual_eltwise"
  top: "inception_resnet_v2_c3_1x1"
  param {
    lr_mult: 1
    decay_mult: 1
  }
  param {
    lr_mult: 2
    decay_mult: 0
  }
  convolution_param {
    num_output: 192
    pad: 0
    kernel_size: 1
    stride: 1
    weight_filler {
      type: "xavier"
      std: 0.01
    }
    bias_filler {
      type: "constant"
      value: 0.2
    }
  }
}
layer {
  name: "inception_resnet_v2_c3_1x1_bn"
  type: "BatchNorm"
  bottom: "inception_resnet_v2_c3_1x1"
  top: "inception_resnet_v2_c3_1x1"
    param {
        lr_mult: 0.0
        decay_mult: 0.0
    }
    param {
        lr_mult: 0.0
        decay_mult: 0.0
    }
    param {
        lr_mult: 0.0
        decay_mult: 0.0
    }
	batch_norm_param {
        use_global_stats: false
        moving_average_fraction: 0.95
	}
}
layer {
  name: "inception_resnet_v2_c3_1x1_scale"
  type: "Scale"
  bottom: "inception_resnet_v2_c3_1x1"
  top: "inception_resnet_v2_c3_1x1"
  scale_param {
    bias_term: true
  }
}
layer {
  name: "inception_resnet_v2_c3_1x1_relu"
  type: "PReLU"
  bottom: "inception_resnet_v2_c3_1x1"
  top: "inception_resnet_v2_c3_1x1"
}
layer {
  name: "inception_resnet_v2_c3_1x3_reduce"
  type: "Convolution"
  bottom: "inception_resnet_v2_c2_residual_eltwise"
  top: "inception_resnet_v2_c3_1x3_reduce"
  param {
    lr_mult: 1
    decay_mult: 1
  }
  param {
    lr_mult: 2
    decay_mult: 0
  }
  convolution_param {
    num_output: 192
    pad: 0
    kernel_size: 1
    stride: 1
    weight_filler {
      type: "xavier"
      std: 0.01
    }
    bias_filler {
      type: "constant"
      value: 0.2
    }
  }
}
layer {
  name: "inception_resnet_v2_c3_1x3_reduce_bn"
  type: "BatchNorm"
  bottom: "inception_resnet_v2_c3_1x3_reduce"
  top: "inception_resnet_v2_c3_1x3_reduce"
    param {
        lr_mult: 0.0
        decay_mult: 0.0
    }
    param {
        lr_mult: 0.0
        decay_mult: 0.0
    }
    param {
        lr_mult: 0.0
        decay_mult: 0.0
    }
	batch_norm_param {
        use_global_stats: false
        moving_average_fraction: 0.95
	}
}
layer {
  name: "inception_resnet_v2_c3_1x3_reduce_scale"
  type: "Scale"
  bottom: "inception_resnet_v2_c3_1x3_reduce"
  top: "inception_resnet_v2_c3_1x3_reduce"
  scale_param {
    bias_term: true
  }
}
layer {
  name: "inception_resnet_v2_c3_1x3_reduce_relu"
  type: "PReLU"
  bottom: "inception_resnet_v2_c3_1x3_reduce"
  top: "inception_resnet_v2_c3_1x3_reduce"
}
layer {
  name: "inception_resnet_v2_c3_1x3"
  type: "Convolution"
  bottom: "inception_resnet_v2_c3_1x3_reduce"
  top: "inception_resnet_v2_c3_1x3"
  param {
    lr_mult: 1
    decay_mult: 1
  }
  param {
    lr_mult: 2
    decay_mult: 0
  }
  convolution_param {
    num_output: 224
    stride: 1
    weight_filler {
      type: "xavier"
      std: 0.01
    }
    bias_filler {
      type: "constant"
      value: 0.2
    }
    pad_h: 0
    pad_w: 1
    kernel_h: 1
    kernel_w: 3
  }
}
layer {
  name: "inception_resnet_v2_c3_1x3_bn"
  type: "BatchNorm"
  bottom: "inception_resnet_v2_c3_1x3"
  top: "inception_resnet_v2_c3_1x3"
    param {
        lr_mult: 0.0
        decay_mult: 0.0
    }
    param {
        lr_mult: 0.0
        decay_mult: 0.0
    }
    param {
        lr_mult: 0.0
        decay_mult: 0.0
    }
	batch_norm_param {
        use_global_stats: false
        moving_average_fraction: 0.95
	}
}
layer {
  name: "inception_resnet_v2_c3_1x3_scale"
  type: "Scale"
  bottom: "inception_resnet_v2_c3_1x3"
  top: "inception_resnet_v2_c3_1x3"
  scale_param {
    bias_term: true
  }
}
layer {
  name: "inception_resnet_v2_c3_1x3_relu"
  type: "PReLU"
  bottom: "inception_resnet_v2_c3_1x3"
  top: "inception_resnet_v2_c3_1x3"
}
layer {
  name: "inception_resnet_v2_c3_3x1"
  type: "Convolution"
  bottom: "inception_resnet_v2_c3_1x3"
  top: "inception_resnet_v2_c3_3x1"
  param {
    lr_mult: 1
    decay_mult: 1
  }
  param {
    lr_mult: 2
    decay_mult: 0
  }
  convolution_param {
    num_output: 256
    stride: 1
    weight_filler {
      type: "xavier"
      std: 0.01
    }
    bias_filler {
      type: "constant"
      value: 0.2
    }
    pad_h: 1
    pad_w: 0
    kernel_h: 3
    kernel_w: 1
  }
}
layer {
  name: "inception_resnet_v2_c3_3x1_bn"
  type: "BatchNorm"
  bottom: "inception_resnet_v2_c3_3x1"
  top: "inception_resnet_v2_c3_3x1"
    param {
        lr_mult: 0.0
        decay_mult: 0.0
    }
    param {
        lr_mult: 0.0
        decay_mult: 0.0
    }
    param {
        lr_mult: 0.0
        decay_mult: 0.0
    }
	batch_norm_param {
        use_global_stats: false
        moving_average_fraction: 0.95
	}
}
layer {
  name: "inception_resnet_v2_c3_3x1_scale"
  type: "Scale"
  bottom: "inception_resnet_v2_c3_3x1"
  top: "inception_resnet_v2_c3_3x1"
  scale_param {
    bias_term: true
  }
}
layer {
  name: "inception_resnet_v2_c3_3x1_relu"
  type: "PReLU"
  bottom: "inception_resnet_v2_c3_3x1"
  top: "inception_resnet_v2_c3_3x1"
}
layer {
  name: "inception_resnet_v2_c3_concat"
  type: "Concat"
  bottom: "inception_resnet_v2_c3_1x1"
  bottom: "inception_resnet_v2_c3_3x1"
  top: "inception_resnet_v2_c3_concat"
}
layer {
  name: "inception_resnet_v2_c3_up"
  type: "Convolution"
  bottom: "inception_resnet_v2_c3_concat"
  top: "inception_resnet_v2_c3_up"
  param {
    lr_mult: 1
    decay_mult: 1
  }
  param {
    lr_mult: 2
    decay_mult: 0
  }
  convolution_param {
    num_output: 2080
    pad: 0
    kernel_size: 1
    stride: 1
    weight_filler {
      type: "xavier"
      std: 0.01
    }
    bias_filler {
      type: "constant"
      value: 0.2
    }
  }
}
layer {
  name: "inception_resnet_v2_c3_up_bn"
  type: "BatchNorm"
  bottom: "inception_resnet_v2_c3_up"
  top: "inception_resnet_v2_c3_up"
    param {
        lr_mult: 0.0
        decay_mult: 0.0
    }
    param {
        lr_mult: 0.0
        decay_mult: 0.0
    }
    param {
        lr_mult: 0.0
        decay_mult: 0.0
    }
	batch_norm_param {
        use_global_stats: false
        moving_average_fraction: 0.95
	}
}
layer {
  name: "inception_resnet_v2_c3_up_scale"
  type: "Scale"
  bottom: "inception_resnet_v2_c3_up"
  top: "inception_resnet_v2_c3_up"
  scale_param {
    bias_term: true
  }
}
layer {
  name: "inception_resnet_v2_c3_residual_eltwise"
  type: "Eltwise"
  bottom: "inception_resnet_v2_c2_residual_eltwise"
  bottom: "inception_resnet_v2_c3_up"
  top: "inception_resnet_v2_c3_residual_eltwise"
  eltwise_param {
    operation: SUM
  }
}

layer {
  name: "conv6_1x1"
  type: "Convolution"
  bottom: "inception_resnet_v2_c3_residual_eltwise"
  top: "conv6_1x1"
  param {
    lr_mult: 1
    decay_mult: 1
  }
  param {
    lr_mult: 2
    decay_mult: 0
  }
  convolution_param {
    num_output: 1536
    pad: 0
    kernel_size: 1
    stride: 1
    weight_filler {
      type: "xavier"
      std: 0.01
    }
    bias_filler {
      type: "constant"
      value: 0.2
    }
  }
}
layer {
  name: "conv6_1x1_bn"
  type: "BatchNorm"
  bottom: "conv6_1x1"
  top: "conv6_1x1"
    param {
        lr_mult: 0.0
        decay_mult: 0.0
    }
    param {
        lr_mult: 0.0
        decay_mult: 0.0
    }
    param {
        lr_mult: 0.0
        decay_mult: 0.0
    }
	batch_norm_param {
        use_global_stats: false
        moving_average_fraction: 0.95
	}
}
layer {
  name: "conv6_1x1_scale"
  type: "Scale"
  bottom: "conv6_1x1"
  top: "conv6_1x1"
  scale_param {
    bias_term: true
  }
}
layer {
  name: "conv6_1x1_relu"
  type: "ReLU"
  bottom: "conv6_1x1"
  top: "conv6_1x1"
}
layer {
  name: "pool_3x3_s1"
  type: "Pooling"
  bottom: "conv6_1x1"
  top: "pool_3x3_s1"
  pooling_param {
    pool: AVE
    global_pooling: true
  }
}
layer {
  name: "pool_3x3_s1_drop"
  type: "Dropout"
  bottom: "pool_3x3_s1"
  top: "pool_3x3_s1_drop"
  dropout_param {
    dropout_ratio: 0.2
  }
}
layer {
  name: "fc5"
  type: "InnerProduct"
  bottom: "pool_3x3_s1_drop"
  top: "fc5"
  param {
    lr_mult: 1
    decay_mult: 1
  }
  param {
    lr_mult: 2
    decay_mult: 0
  }
  inner_product_param {
    num_output: 128
    weight_filler {
      type: "xavier"
    }
    bias_filler {
      type: "constant"
      value: 0
    }
  }
}
layer {
  name: "classifier"
  type: "InnerProduct"
  bottom: "fc5"
  top: "classifier"
  param {
    lr_mult: 1
    decay_mult: 1
  }
  param {
    lr_mult: 2
    decay_mult: 0
  }
  inner_product_param {
    num_output: 8631
    weight_filler {
      type: "xavier"
    }
    bias_filler {
      type: "constant"
      value: 0
    }
  }
}
layer {
  name: "loss"
  type: "SoftmaxWithLoss"
  bottom: "classifier"
  bottom: "label"
  top: "loss"
}