layer {
  name: "data"
  type: "Data"
  top: "data"
  transform_param {
    mean_value: 32.97
    mean_value: 35.68
    mean_value: 40.7
  }
  data_param {
    source: "/mnt/datagrid/personal/bartom40/gisat_data_tst.lmdb"
    batch_size: 25
    backend: LMDB
  }
}
layer {
  name: "label"
  type: "Data"
  top: "label"
  data_param {
    source: "/mnt/datagrid/personal/bartom40/gisat_mask_tst.lmdb"
    batch_size: 25
    backend: LMDB
  }
}
layer {
  name: "conv1_1"
  type: "Convolution"
  bottom: "data"
  top: "conv1_1"
  param {
    lr_mult: 0.5
    decay_mult: 1
  }
  convolution_param {
    num_output: 64
    pad: 1
    kernel_size: 3
    weight_filler {
      type: "msra"
    }
  }
}
layer {
  name: "conv1_1_bn"
  type: "BatchNorm"
  bottom: "conv1_1"
  top: "conv1_1"
}
layer {
  name: "conv1_1_scale"
  type: "Scale"
  bottom: "conv1_1"
  top: "conv1_1"
  param {
    lr_mult: 0.5
  }
  param {
    lr_mult: 1.0
  }
  scale_param {
    bias_term: true
  }
}
layer {
  name: "conv1_1_relu"
  type: "ReLU"
  bottom: "conv1_1"
  top: "conv1_1"
}
layer {
  name: "conv1_2"
  type: "Convolution"
  bottom: "conv1_1"
  top: "conv1_2"
  param {
    lr_mult: 0.5
    decay_mult: 1
  }
  convolution_param {
    num_output: 64
    pad: 1
    kernel_size: 3
    weight_filler {
      type: "msra"
    }
  }
}
layer {
  name: "conv1_2_bn"
  type: "BatchNorm"
  bottom: "conv1_2"
  top: "conv1_2"
}
layer {
  name: "conv1_2_scale"
  type: "Scale"
  bottom: "conv1_2"
  top: "conv1_2"
  param {
    lr_mult: 0.5
  }
  param {
    lr_mult: 1.0
  }
  scale_param {
    bias_term: true
  }
}
layer {
  name: "conv1_2_relu"
  type: "ReLU"
  bottom: "conv1_2"
  top: "conv1_2"
}
layer {
  name: "pool1"
  type: "Pooling"
  bottom: "conv1_2"
  top: "pool1"
  top: "pool1_mask"
  pooling_param {
    pool: MAX
    kernel_size: 2
    stride: 2
  }
}
layer {
  name: "conv2_1"
  type: "Convolution"
  bottom: "pool1"
  top: "conv2_1"
  param {
    lr_mult: 0.5
    decay_mult: 1
  }
  convolution_param {
    num_output: 128
    pad: 1
    kernel_size: 3
    weight_filler {
      type: "msra"
    }
  }
}
layer {
  name: "conv2_1_bn"
  type: "BatchNorm"
  bottom: "conv2_1"
  top: "conv2_1"
}
layer {
  name: "conv2_1_scale"
  type: "Scale"
  bottom: "conv2_1"
  top: "conv2_1"
  param {
    lr_mult: 0.5
  }
  param {
    lr_mult: 1.0
  }
  scale_param {
    bias_term: true
  }
}
layer {
  name: "conv2_1_relu"
  type: "ReLU"
  bottom: "conv2_1"
  top: "conv2_1"
}
layer {
  name: "conv2_2"
  type: "Convolution"
  bottom: "conv2_1"
  top: "conv2_2"
  param {
    lr_mult: 0.5
    decay_mult: 1
  }
  convolution_param {
    num_output: 128
    pad: 1
    kernel_size: 3
    weight_filler {
      type: "msra"
    }
  }
}
layer {
  name: "conv2_2_bn"
  type: "BatchNorm"
  bottom: "conv2_2"
  top: "conv2_2"
}
layer {
  name: "conv2_2_scale"
  type: "Scale"
  bottom: "conv2_2"
  top: "conv2_2"
  param {
    lr_mult: 0.5
  }
  param {
    lr_mult: 1.0
  }
  scale_param {
    bias_term: true
  }
}
layer {
  name: "conv2_2_relu"
  type: "ReLU"
  bottom: "conv2_2"
  top: "conv2_2"
}
layer {
  name: "pool2"
  type: "Pooling"
  bottom: "conv2_2"
  top: "pool2"
  top: "pool2_mask"
  pooling_param {
    pool: MAX
    kernel_size: 2
    stride: 2
  }
}
layer {
  name: "conv3_1"
  type: "Convolution"
  bottom: "pool2"
  top: "conv3_1"
  param {
    lr_mult: 0.5
    decay_mult: 1
  }
  convolution_param {
    num_output: 256
    pad: 1
    kernel_size: 3
    weight_filler {
      type: "msra"
    }
  }
}
layer {
  name: "conv3_1_bn"
  type: "BatchNorm"
  bottom: "conv3_1"
  top: "conv3_1"
}
layer {
  name: "conv3_1_scale"
  type: "Scale"
  bottom: "conv3_1"
  top: "conv3_1"
  param {
    lr_mult: 0.5
  }
  param {
    lr_mult: 1.0
  }
  scale_param {
    bias_term: true
  }
}
layer {
  name: "conv3_1_relu"
  type: "ReLU"
  bottom: "conv3_1"
  top: "conv3_1"
}
layer {
  name: "conv3_2"
  type: "Convolution"
  bottom: "conv3_1"
  top: "conv3_2"
  param {
    lr_mult: 0.5
    decay_mult: 1
  }
  convolution_param {
    num_output: 256
    pad: 1
    kernel_size: 3
    weight_filler {
      type: "msra"
    }
  }
}
layer {
  name: "conv3_2_bn"
  type: "BatchNorm"
  bottom: "conv3_2"
  top: "conv3_2"
}
layer {
  name: "conv3_2_scale"
  type: "Scale"
  bottom: "conv3_2"
  top: "conv3_2"
  param {
    lr_mult: 0.5
  }
  param {
    lr_mult: 1.0
  }
  scale_param {
    bias_term: true
  }
}
layer {
  name: "conv3_2_relu"
  type: "ReLU"
  bottom: "conv3_2"
  top: "conv3_2"
}
layer {
  name: "conv3_3"
  type: "Convolution"
  bottom: "conv3_2"
  top: "conv3_3"
  param {
    lr_mult: 0.5
    decay_mult: 1
  }
  convolution_param {
    num_output: 256
    pad: 1
    kernel_size: 3
    weight_filler {
      type: "msra"
    }
  }
}
layer {
  name: "conv3_3_bn"
  type: "BatchNorm"
  bottom: "conv3_3"
  top: "conv3_3"
}
layer {
  name: "conv3_3_scale"
  type: "Scale"
  bottom: "conv3_3"
  top: "conv3_3"
  param {
    lr_mult: 0.5
  }
  param {
    lr_mult: 1.0
  }
  scale_param {
    bias_term: true
  }
}
layer {
  name: "conv3_3_relu"
  type: "ReLU"
  bottom: "conv3_3"
  top: "conv3_3"
}
layer {
  name: "pool3"
  type: "Pooling"
  bottom: "conv3_3"
  top: "pool3"
  top: "pool3_mask"
  pooling_param {
    pool: MAX
    kernel_size: 2
    stride: 2
  }
}
layer {
  name: "conv4_1"
  type: "Convolution"
  bottom: "pool3"
  top: "conv4_1"
  param {
    lr_mult: 0.5
    decay_mult: 1
  }
  convolution_param {
    num_output: 512
    pad: 1
    kernel_size: 3
    weight_filler {
      type: "msra"
    }
  }
}
layer {
  name: "conv4_1_bn"
  type: "BatchNorm"
  bottom: "conv4_1"
  top: "conv4_1"
}
layer {
  name: "conv4_1_scale"
  type: "Scale"
  bottom: "conv4_1"
  top: "conv4_1"
  param {
    lr_mult: 0.5
  }
  param {
    lr_mult: 1.0
  }
  scale_param {
    bias_term: true
  }
}
layer {
  name: "conv4_1_relu"
  type: "ReLU"
  bottom: "conv4_1"
  top: "conv4_1"
}
layer {
  name: "conv4_2"
  type: "Convolution"
  bottom: "conv4_1"
  top: "conv4_2"
  param {
    lr_mult: 0.5
    decay_mult: 1
  }
  convolution_param {
    num_output: 512
    pad: 1
    kernel_size: 3
    weight_filler {
      type: "msra"
    }
  }
}
layer {
  name: "conv4_2_bn"
  type: "BatchNorm"
  bottom: "conv4_2"
  top: "conv4_2"
}
layer {
  name: "conv4_2_scale"
  type: "Scale"
  bottom: "conv4_2"
  top: "conv4_2"
  param {
    lr_mult: 0.5
  }
  param {
    lr_mult: 1.0
  }
  scale_param {
    bias_term: true
  }
}
layer {
  name: "conv4_2_relu"
  type: "ReLU"
  bottom: "conv4_2"
  top: "conv4_2"
}
layer {
  name: "conv4_3"
  type: "Convolution"
  bottom: "conv4_2"
  top: "conv4_3"
  param {
    lr_mult: 0.5
    decay_mult: 1
  }
  convolution_param {
    num_output: 512
    pad: 1
    kernel_size: 3
    weight_filler {
      type: "msra"
    }
  }
}
layer {
  name: "conv4_3_bn"
  type: "BatchNorm"
  bottom: "conv4_3"
  top: "conv4_3"
}
layer {
  name: "conv4_3_scale"
  type: "Scale"
  bottom: "conv4_3"
  top: "conv4_3"
  param {
    lr_mult: 0.5
  }
  param {
    lr_mult: 1.0
  }
  scale_param {
    bias_term: true
  }
}
layer {
  name: "conv4_3_relu"
  type: "ReLU"
  bottom: "conv4_3"
  top: "conv4_3"
}
layer {
  name: "pool4"
  type: "Pooling"
  bottom: "conv4_3"
  top: "pool4"
  top: "pool4_mask"
  pooling_param {
    pool: MAX
    kernel_size: 2
    stride: 2
  }
}
layer {
  name: "conv5_1"
  type: "Convolution"
  bottom: "pool4"
  top: "conv5_1"
  param {
    lr_mult: 0.5
    decay_mult: 1
  }
  convolution_param {
    num_output: 512
    pad: 1
    kernel_size: 3
    weight_filler {
      type: "msra"
    }
  }
}
layer {
  name: "conv5_1_bn"
  type: "BatchNorm"
  bottom: "conv5_1"
  top: "conv5_1"
}
layer {
  name: "conv5_1_scale"
  type: "Scale"
  bottom: "conv5_1"
  top: "conv5_1"
  param {
    lr_mult: 0.5
  }
  param {
    lr_mult: 1.0
  }
  scale_param {
    bias_term: true
  }
}
layer {
  name: "conv5_1_relu"
  type: "ReLU"
  bottom: "conv5_1"
  top: "conv5_1"
}
layer {
  name: "conv5_2"
  type: "Convolution"
  bottom: "conv5_1"
  top: "conv5_2"
  param {
    lr_mult: 0.5
    decay_mult: 1
  }
  convolution_param {
    num_output: 512
    pad: 1
    kernel_size: 3
    weight_filler {
      type: "msra"
    }
  }
}
layer {
  name: "conv5_2_bn"
  type: "BatchNorm"
  bottom: "conv5_2"
  top: "conv5_2"
}
layer {
  name: "conv5_2_scale"
  type: "Scale"
  bottom: "conv5_2"
  top: "conv5_2"
  param {
    lr_mult: 0.5
  }
  param {
    lr_mult: 1.0
  }
  scale_param {
    bias_term: true
  }
}
layer {
  name: "conv5_2_relu"
  type: "ReLU"
  bottom: "conv5_2"
  top: "conv5_2"
}
layer {
  name: "conv5_3"
  type: "Convolution"
  bottom: "conv5_2"
  top: "conv5_3"
  param {
    lr_mult: 0.5
    decay_mult: 1
  }
  convolution_param {
    num_output: 512
    pad: 1
    kernel_size: 3
    weight_filler {
      type: "msra"
    }
  }
}
layer {
  name: "conv5_3_bn"
  type: "BatchNorm"
  bottom: "conv5_3"
  top: "conv5_3"
}
layer {
  name: "conv5_3_scale"
  type: "Scale"
  bottom: "conv5_3"
  top: "conv5_3"
  param {
    lr_mult: 0.5
  }
  param {
    lr_mult: 1.0
  }
  scale_param {
    bias_term: true
  }
}
layer {
  name: "conv5_3_relu"
  type: "ReLU"
  bottom: "conv5_3"
  top: "conv5_3"
}
layer {
  name: "pool5"
  type: "Pooling"
  bottom: "conv5_3"
  top: "pool5"
  top: "pool5_mask"
  pooling_param {
    pool: MAX
    kernel_size: 2
    stride: 2
  }
}
layer {
  name: "upsample5"
  type: "Upsample"
  bottom: "pool5"
  bottom: "pool5_mask"
  top: "upsample5"
  upsample_param {
    scale: 2
  }
}
layer {
  name: "conv5_3_D"
  type: "Convolution"
  bottom: "upsample5"
  top: "conv5_3_D"
  param {
    lr_mult: 1
    decay_mult: 1
  }
  convolution_param {
    num_output: 512
    pad: 1
    kernel_size: 3
    weight_filler {
      type: "msra"
    }
  }
}
layer {
  name: "conv5_3_D_bn"
  type: "BatchNorm"
  bottom: "conv5_3_D"
  top: "conv5_3_D"
}
layer {
  name: "conv5_3_D_scale"
  type: "Scale"
  bottom: "conv5_3_D"
  top: "conv5_3_D"
  param {
    lr_mult: 1
  }
  param {
    lr_mult: 2
  }
  scale_param {
    bias_term: true
  }
}
layer {
  name: "conv5_3_D_relu"
  type: "ReLU"
  bottom: "conv5_3_D"
  top: "conv5_3_D"
}
layer {
  name: "conv5_2_D"
  type: "Convolution"
  bottom: "conv5_3_D"
  top: "conv5_2_D"
  param {
    lr_mult: 1
    decay_mult: 1
  }
  convolution_param {
    num_output: 512
    pad: 1
    kernel_size: 3
    weight_filler {
      type: "msra"
    }
  }
}
layer {
  name: "conv5_2_D_bn"
  type: "BatchNorm"
  bottom: "conv5_2_D"
  top: "conv5_2_D"
}
layer {
  name: "conv5_2_D_scale"
  type: "Scale"
  bottom: "conv5_2_D"
  top: "conv5_2_D"
  param {
    lr_mult: 1
  }
  param {
    lr_mult: 2
  }
  scale_param {
    bias_term: true
  }
}
layer {
  name: "conv5_2_D_relu"
  type: "ReLU"
  bottom: "conv5_2_D"
  top: "conv5_2_D"
}
layer {
  name: "conv5_1_D"
  type: "Convolution"
  bottom: "conv5_2_D"
  top: "conv5_1_D"
  param {
    lr_mult: 1
    decay_mult: 1
  }
  convolution_param {
    num_output: 512
    pad: 1
    kernel_size: 3
    weight_filler {
      type: "msra"
    }
  }
}
layer {
  name: "conv5_1_D_bn"
  type: "BatchNorm"
  bottom: "conv5_1_D"
  top: "conv5_1_D"
}
layer {
  name: "conv5_1_D_scale"
  type: "Scale"
  bottom: "conv5_1_D"
  top: "conv5_1_D"
  param {
    lr_mult: 1
  }
  param {
    lr_mult: 2
  }
  scale_param {
    bias_term: true
  }
}
layer {
  name: "conv5_1_D_relu"
  type: "ReLU"
  bottom: "conv5_1_D"
  top: "conv5_1_D"
}
layer {
  name: "upsample4"
  type: "Upsample"
  bottom: "conv5_1_D"
  bottom: "pool4_mask"
  top: "upsample4"
  upsample_param {
    scale: 2
  }
}
layer {
  name: "conv4_3_D"
  type: "Convolution"
  bottom: "upsample4"
  top: "conv4_3_D"
  param {
    lr_mult: 1
    decay_mult: 1
  }
  convolution_param {
    num_output: 512
    pad: 1
    kernel_size: 3
    weight_filler {
      type: "msra"
    }
  }
}
layer {
  name: "conv4_3_D_bn"
  type: "BatchNorm"
  bottom: "conv4_3_D"
  top: "conv4_3_D"
}
layer {
  name: "conv4_3_D_scale"
  type: "Scale"
  bottom: "conv4_3_D"
  top: "conv4_3_D"
  param {
    lr_mult: 1
  }
  param {
    lr_mult: 2
  }
  scale_param {
    bias_term: true
  }
}
layer {
  name: "conv4_3_D_relu"
  type: "ReLU"
  bottom: "conv4_3_D"
  top: "conv4_3_D"
}
layer {
  name: "conv4_2_D"
  type: "Convolution"
  bottom: "conv4_3_D"
  top: "conv4_2_D"
  param {
    lr_mult: 1
    decay_mult: 1
  }
  convolution_param {
    num_output: 512
    pad: 1
    kernel_size: 3
    weight_filler {
      type: "msra"
    }
  }
}
layer {
  name: "conv4_2_D_bn"
  type: "BatchNorm"
  bottom: "conv4_2_D"
  top: "conv4_2_D"
}
layer {
  name: "conv4_2_D_scale"
  type: "Scale"
  bottom: "conv4_2_D"
  top: "conv4_2_D"
  param {
    lr_mult: 1
  }
  param {
    lr_mult: 2
  }
  scale_param {
    bias_term: true
  }
}
layer {
  name: "conv4_2_D_relu"
  type: "ReLU"
  bottom: "conv4_2_D"
  top: "conv4_2_D"
}
layer {
  name: "conv4_1_D"
  type: "Convolution"
  bottom: "conv4_2_D"
  top: "conv4_1_D"
  param {
    lr_mult: 1
    decay_mult: 1
  }
  convolution_param {
    num_output: 256
    pad: 1
    kernel_size: 3
    weight_filler {
      type: "msra"
    }
  }
}
layer {
  name: "conv4_1_D_bn"
  type: "BatchNorm"
  bottom: "conv4_1_D"
  top: "conv4_1_D"
}
layer {
  name: "conv4_1_D_scale"
  type: "Scale"
  bottom: "conv4_1_D"
  top: "conv4_1_D"
  param {
    lr_mult: 1
  }
  param {
    lr_mult: 2
  }
  scale_param {
    bias_term: true
  }
}
layer {
  name: "conv4_1_D_relu"
  type: "ReLU"
  bottom: "conv4_1_D"
  top: "conv4_1_D"
}
layer {
  name: "upsample3"
  type: "Upsample"
  bottom: "conv4_1_D"
  bottom: "pool3_mask"
  top: "upsample3"
  upsample_param {
    scale: 2
  }
}
layer {
  name: "conv3_3_D"
  type: "Convolution"
  bottom: "upsample3"
  top: "conv3_3_D"
  param {
    lr_mult: 1
    decay_mult: 1
  }
  convolution_param {
    num_output: 256
    pad: 1
    kernel_size: 3
    weight_filler {
      type: "msra"
    }
  }
}
layer {
  name: "conv3_3_D_bn"
  type: "BatchNorm"
  bottom: "conv3_3_D"
  top: "conv3_3_D"
}
layer {
  name: "conv3_3_D_scale"
  type: "Scale"
  bottom: "conv3_3_D"
  top: "conv3_3_D"
  param {
    lr_mult: 1
  }
  param {
    lr_mult: 2
  }
  scale_param {
    bias_term: true
  }
}
layer {
  name: "conv3_3_D_relu"
  type: "ReLU"
  bottom: "conv3_3_D"
  top: "conv3_3_D"
}
layer {
  name: "conv3_2_D"
  type: "Convolution"
  bottom: "conv3_3_D"
  top: "conv3_2_D"
  param {
    lr_mult: 1
    decay_mult: 1
  }
  convolution_param {
    num_output: 256
    pad: 1
    kernel_size: 3
    weight_filler {
      type: "msra"
    }
  }
}
layer {
  name: "conv3_2_D_bn"
  type: "BatchNorm"
  bottom: "conv3_2_D"
  top: "conv3_2_D"
}
layer {
  name: "conv3_2_D_scale"
  type: "Scale"
  bottom: "conv3_2_D"
  top: "conv3_2_D"
  param {
    lr_mult: 1
  }
  param {
    lr_mult: 2
  }
  scale_param {
    bias_term: true
  }
}
layer {
  name: "conv3_2_D_relu"
  type: "ReLU"
  bottom: "conv3_2_D"
  top: "conv3_2_D"
}
layer {
  name: "conv3_1_D"
  type: "Convolution"
  bottom: "conv3_2_D"
  top: "conv3_1_D"
  param {
    lr_mult: 1
    decay_mult: 1
  }
  convolution_param {
    num_output: 128
    pad: 1
    kernel_size: 3
    weight_filler {
      type: "msra"
    }
  }
}
layer {
  name: "conv3_1_D_bn"
  type: "BatchNorm"
  bottom: "conv3_1_D"
  top: "conv3_1_D"
}
layer {
  name: "conv3_1_D_scale"
  type: "Scale"
  bottom: "conv3_1_D"
  top: "conv3_1_D"
  param {
    lr_mult: 1
  }
  param {
    lr_mult: 2
  }
  scale_param {
    bias_term: true
  }
}
layer {
  name: "conv3_1_D_relu"
  type: "ReLU"
  bottom: "conv3_1_D"
  top: "conv3_1_D"
}
layer {
  name: "upsample2"
  type: "Upsample"
  bottom: "conv3_1_D"
  bottom: "pool2_mask"
  top: "upsample2"
  upsample_param {
    scale: 2
  }
}
layer {
  name: "conv2_2_D"
  type: "Convolution"
  bottom: "upsample2"
  top: "conv2_2_D"
  param {
    lr_mult: 1
    decay_mult: 1
  }
  convolution_param {
    num_output: 128
    pad: 1
    kernel_size: 3
    weight_filler {
      type: "msra"
    }
  }
}
layer {
  name: "conv2_2_D_bn"
  type: "BatchNorm"
  bottom: "conv2_2_D"
  top: "conv2_2_D"
}
layer {
  name: "conv2_2_D_scale"
  type: "Scale"
  bottom: "conv2_2_D"
  top: "conv2_2_D"
  param {
    lr_mult: 1
  }
  param {
    lr_mult: 2
  }
  scale_param {
    bias_term: true
  }
}
layer {
  name: "conv2_2_D_relu"
  type: "ReLU"
  bottom: "conv2_2_D"
  top: "conv2_2_D"
}
layer {
  name: "conv2_1_D"
  type: "Convolution"
  bottom: "conv2_2_D"
  top: "conv2_1_D"
  param {
    lr_mult: 1
    decay_mult: 1
  }
  convolution_param {
    num_output: 64
    pad: 1
    kernel_size: 3
    weight_filler {
      type: "msra"
    }
  }
}
layer {
  name: "conv2_1_D_bn"
  type: "BatchNorm"
  bottom: "conv2_1_D"
  top: "conv2_1_D"
}
layer {
  name: "conv2_1_D_scale"
  type: "Scale"
  bottom: "conv2_1_D"
  top: "conv2_1_D"
  param {
    lr_mult: 1
  }
  param {
    lr_mult: 2
  }
  scale_param {
    bias_term: true
  }
}
layer {
  name: "conv2_1_D_relu"
  type: "ReLU"
  bottom: "conv2_1_D"
  top: "conv2_1_D"
}
layer {
  name: "upsample1"
  type: "Upsample"
  bottom: "conv2_1_D"
  bottom: "pool1_mask"
  top: "upsample1"
  upsample_param {
    scale: 2
  }
}
layer {
  name: "conv1_2_D"
  type: "Convolution"
  bottom: "upsample1"
  top: "conv1_2_D"
  param {
    lr_mult: 1
    decay_mult: 1
  }
  convolution_param {
    num_output: 64
    pad: 1
    kernel_size: 3
    weight_filler {
      type: "msra"
    }
  }
}
layer {
  name: "conv1_2_D_bn"
  type: "BatchNorm"
  bottom: "conv1_2_D"
  top: "conv1_2_D"
}
layer {
  name: "conv1_2_D_scale"
  type: "Scale"
  bottom: "conv1_2_D"
  top: "conv1_2_D"
  param {
    lr_mult: 1
  }
  param {
    lr_mult: 2
  }
  scale_param {
    bias_term: true
  }
}
layer {
  name: "conv1_2_D_relu"
  type: "ReLU"
  bottom: "conv1_2_D"
  top: "conv1_2_D"
}
layer {
  name: "conv1_1_D"
  type: "Convolution"
  bottom: "conv1_2_D"
  top: "conv1_1_D"
  param {
    lr_mult: 1
    decay_mult: 1
  }
  convolution_param {
    num_output: 4
    pad: 1
    kernel_size: 3
    weight_filler {
      type: "msra"
    }
  }
}
