segnet_train.prototxt

name: "VGG_ILSVRC_16_layer"
layer {
  name: "data"
  type: "DenseImageData"
  top: "data"
  top: "label"
  dense_image_data_param {
    source: "/SegNet/CamVid/train.txt"	# Change this to the absolute path to your data file
    batch_size: 4   			# Change this number to a batch size that will fit on your GPU
    shuffle: true
  }
}
layer {
  bottom: "data"
  top: "conv1_1"
  name: "conv1_1"
  type: "Convolution"
  param {
    lr_mult: 1
    decay_mult: 1
  }
  param {
    lr_mult: 2
    decay_mult: 0
  }
  convolution_param {
    weight_filler {
      type: "msra"
    }
    bias_filler {
      type: "constant"
    }
    num_output: 64
    pad: 1
    kernel_size: 3
  }
}
layer {
  bottom: "conv1_1"
  top: "conv1_1"
  name: "conv1_1_bn"
  type: "BN"
  param {
    lr_mult: 1
    decay_mult: 1
  }
  param {
    lr_mult: 1
    decay_mult: 0
  }
  bn_param {
	scale_filler {
	  type: "constant"
	  value: 1
	}
	shift_filler {
	  type: "constant"
	  value: 0.001
	}
 }
}
layer {
  bottom: "conv1_1"
  top: "conv1_1"
  name: "relu1_1"
  type: "ReLU"
}
layer {
  bottom: "conv1_1"
  top: "conv1_2"
  name: "conv1_2"
  type: "Convolution"
  param {
    lr_mult: 1
    decay_mult: 1
  }
  param {
    lr_mult: 2
    decay_mult: 0
  }
  convolution_param {
    weight_filler {
      type: "msra"
    }
    bias_filler {
      type: "constant"
    }
    num_output: 64
    pad: 1
    kernel_size: 3
  }
}
layer {
  bottom: "conv1_2"
  top: "conv1_2"
  name: "conv1_2_bn"
  type: "BN"
  param {
    lr_mult: 1
    decay_mult: 1
  }
  param {
    lr_mult: 1
    decay_mult: 0
  }
  bn_param {
	scale_filler {
	  type: "constant"
	  value: 1
	}
	shift_filler {
	  type: "constant"
	  value: 0.001
	}
 }
}
layer {
  bottom: "conv1_2"
  top: "conv1_2"
  name: "relu1_2"
  type: "ReLU"
}
layer {
  bottom: "conv1_2"
  top: "pool1"
  top: "pool1_mask"
  name: "pool1"
  type: "Pooling"
  pooling_param {
    pool: MAX
    kernel_size: 2
    stride: 2
  }
}
layer {
  bottom: "pool1"
  top: "conv2_1"
  name: "conv2_1"
  type: "Convolution"
  param {
    lr_mult: 1
    decay_mult: 1
  }
  param {
    lr_mult: 2
    decay_mult: 0
  }
  convolution_param {
    weight_filler {
      type: "msra"
    }
    bias_filler {
      type: "constant"
    }
    num_output: 128
    pad: 1
    kernel_size: 3
  }
}
layer {
  bottom: "conv2_1"
  top: "conv2_1"
  name: "conv2_1_bn"
  type: "BN"
  param {
    lr_mult: 1
    decay_mult: 1
  }
  param {
    lr_mult: 1
    decay_mult: 0
  }
  bn_param {
	scale_filler {
	  type: "constant"
	  value: 1
	}
	shift_filler {
	  type: "constant"
	  value: 0.001
	}
 }
}
layer {
  bottom: "conv2_1"
  top: "conv2_1"
  name: "relu2_1"
  type: "ReLU"
}
layer {
  bottom: "conv2_1"
  top: "conv2_2"
  name: "conv2_2"
  type: "Convolution"
  param {
    lr_mult: 1
    decay_mult: 1
  }
  param {
    lr_mult: 2
    decay_mult: 0
  }
  convolution_param {
    weight_filler {
      type: "msra"
    }
    bias_filler {
      type: "constant"
    }
    num_output: 128
    pad: 1
    kernel_size: 3
  }
}
layer {
  bottom: "conv2_2"
  top: "conv2_2"
  name: "conv2_2_bn"
  type: "BN"
  param {
    lr_mult: 1
    decay_mult: 1
  }
  param {
    lr_mult: 1
    decay_mult: 0
  }
  bn_param {
	scale_filler {
	  type: "constant"
	  value: 1
	}
	shift_filler {
	  type: "constant"
	  value: 0.001
	}
 }
}
layer {
  bottom: "conv2_2"
  top: "conv2_2"
  name: "relu2_2"
  type: "ReLU"
}
layer {
  bottom: "conv2_2"
  top: "pool2"
  top: "pool2_mask"
  name: "pool2"
  type: "Pooling"
  pooling_param {
    pool: MAX
    kernel_size: 2
    stride: 2
  }
}
layer {
  bottom: "pool2"
  top: "conv3_1"
  name: "conv3_1"
  type: "Convolution"
  param {
    lr_mult: 1
    decay_mult: 1
  }
  param {
    lr_mult: 2
    decay_mult: 0
  }
  convolution_param {
    weight_filler {
      type: "msra"
    }
    bias_filler {
      type: "constant"
    }
    num_output: 256
    pad: 1
    kernel_size: 3
  }
}
layer {
  bottom: "conv3_1"
  top: "conv3_1"
  name: "conv3_1_bn"
  type: "BN"
  param {
    lr_mult: 1
    decay_mult: 1
  }
  param {
    lr_mult: 1
    decay_mult: 0
  }
  bn_param {
	scale_filler {
	  type: "constant"
	  value: 1
	}
	shift_filler {
	  type: "constant"
	  value: 0.001
	}
 }
}
layer {
  bottom: "conv3_1"
  top: "conv3_1"
  name: "relu3_1"
  type: "ReLU"
}
layer {
  bottom: "conv3_1"
  top: "conv3_2"
  name: "conv3_2"
  type: "Convolution"
  param {
    lr_mult: 1
    decay_mult: 1
  }
  param {
    lr_mult: 2
    decay_mult: 0
  }
  convolution_param {
    weight_filler {
      type: "msra"
    }
    bias_filler {
      type: "constant"
    }
    num_output: 256
    pad: 1
    kernel_size: 3
  }
}
layer {
  bottom: "conv3_2"
  top: "conv3_2"
  name: "conv3_2_bn"
  type: "BN"
  param {
    lr_mult: 1
    decay_mult: 1
  }
  param {
    lr_mult: 1
    decay_mult: 0
  }
  bn_param {
	scale_filler {
	  type: "constant"
	  value: 1
	}
	shift_filler {
	  type: "constant"
	  value: 0.001
	}
 }
}
layer {
  bottom: "conv3_2"
  top: "conv3_2"
  name: "relu3_2"
  type: "ReLU"
}
layer {
  bottom: "conv3_2"
  top: "conv3_3"
  name: "conv3_3"
  type: "Convolution"
  param {
    lr_mult: 1
    decay_mult: 1
  }
  param {
    lr_mult: 2
    decay_mult: 0
  }
  convolution_param {
    weight_filler {
      type: "msra"
    }
    bias_filler {
      type: "constant"
    }
    num_output: 256
    pad: 1
    kernel_size: 3
  }
}
layer {
  bottom: "conv3_3"
  top: "conv3_3"
  name: "conv3_3_bn"
  type: "BN"
  param {
    lr_mult: 1
    decay_mult: 1
  }
  param {
    lr_mult: 1
    decay_mult: 0
  }
  bn_param {
	scale_filler {
	  type: "constant"
	  value: 1
	}
	shift_filler {
	  type: "constant"
	  value: 0.001
	}
 }
}
layer {
  bottom: "conv3_3"
  top: "conv3_3"
  name: "relu3_3"
  type: "ReLU"
}
layer {
  bottom: "conv3_3"
  top: "pool3"
  top: "pool3_mask"
  name: "pool3"
  type: "Pooling"
  pooling_param {
    pool: MAX
    kernel_size: 2
    stride: 2
  }
}
layer {
  bottom: "pool3"
  top: "conv4_1"
  name: "conv4_1"
  type: "Convolution"
  param {
    lr_mult: 1
    decay_mult: 1
  }
  param {
    lr_mult: 2
    decay_mult: 0
  }
  convolution_param {
    weight_filler {
      type: "msra"
    }
    bias_filler {
      type: "constant"
    }
    num_output: 512
    pad: 1
    kernel_size: 3
  }
}
layer {
  bottom: "conv4_1"
  top: "conv4_1"
  name: "conv4_1_bn"
  type: "BN"
  param {
    lr_mult: 1
    decay_mult: 1
  }
  param {
    lr_mult: 1
    decay_mult: 0
  }
  bn_param {
	scale_filler {
	  type: "constant"
	  value: 1
	}
	shift_filler {
	  type: "constant"
	  value: 0.001
	}
 }
}
layer {
  bottom: "conv4_1"
  top: "conv4_1"
  name: "relu4_1"
  type: "ReLU"
}
layer {
  bottom: "conv4_1"
  top: "conv4_2"
  name: "conv4_2"
  type: "Convolution"
  param {
    lr_mult: 1
    decay_mult: 1
  }
  param {
    lr_mult: 2
    decay_mult: 0
  }
  convolution_param {
    weight_filler {
      type: "msra"
    }
    bias_filler {
      type: "constant"
    }
    num_output: 512
    pad: 1
    kernel_size: 3
  }
}
layer {
  bottom: "conv4_2"
  top: "conv4_2"
  name: "conv4_2_bn"
  type: "BN"
  param {
    lr_mult: 1
    decay_mult: 1
  }
  param {
    lr_mult: 1
    decay_mult: 0
  }
  bn_param {
	scale_filler {
	  type: "constant"
	  value: 1
	}
	shift_filler {
	  type: "constant"
	  value: 0.001
	}
 }
}
layer {
  bottom: "conv4_2"
  top: "conv4_2"
  name: "relu4_2"
  type: "ReLU"
}
layer {
  bottom: "conv4_2"
  top: "conv4_3"
  name: "conv4_3"
  type: "Convolution"
  param {
    lr_mult: 1
    decay_mult: 1
  }
  param {
    lr_mult: 2
    decay_mult: 0
  }
  convolution_param {
    weight_filler {
      type: "msra"
    }
    bias_filler {
      type: "constant"
    }
    num_output: 512
    pad: 1
    kernel_size: 3
  }
}
layer {
  bottom: "conv4_3"
  top: "conv4_3"
  name: "conv4_3_bn"
  type: "BN"
  param {
    lr_mult: 1
    decay_mult: 1
  }
  param {
    lr_mult: 1
    decay_mult: 0
  }
  bn_param {
	scale_filler {
	  type: "constant"
	  value: 1
	}
	shift_filler {
	  type: "constant"
	  value: 0.001
	}
 }
}
layer {
  bottom: "conv4_3"
  top: "conv4_3"
  name: "relu4_3"
  type: "ReLU"
}
layer {
  bottom: "conv4_3"
  top: "pool4"
  top: "pool4_mask"
  name: "pool4"
  type: "Pooling"
  pooling_param {
    pool: MAX
    kernel_size: 2
    stride: 2
  }
}
layer {
  bottom: "pool4"
  top: "conv5_1"
  name: "conv5_1"
  type: "Convolution"
  param {
    lr_mult: 1
    decay_mult: 1
  }
  param {
    lr_mult: 2
    decay_mult: 0
  }
  convolution_param {
    weight_filler {
      type: "msra"
    }
    bias_filler {
      type: "constant"
    }
    num_output: 512
    pad: 1
    kernel_size: 3
  }
}
layer {
  bottom: "conv5_1"
  top: "conv5_1"
  name: "conv5_1_bn"
  type: "BN"
  param {
    lr_mult: 1
    decay_mult: 1
  }
  param {
    lr_mult: 1
    decay_mult: 0
  }
  bn_param {
	scale_filler {
	  type: "constant"
	  value: 1
	}
	shift_filler {
	  type: "constant"
	  value: 0.001
	}
 }
}
layer {
  bottom: "conv5_1"
  top: "conv5_1"
  name: "relu5_1"
  type: "ReLU"
}
layer {
  bottom: "conv5_1"
  top: "conv5_2"
  name: "conv5_2"
  type: "Convolution"
  param {
    lr_mult: 1
    decay_mult: 1
  }
  param {
    lr_mult: 2
    decay_mult: 0
  }
  convolution_param {
    weight_filler {
      type: "msra"
    }
    bias_filler {
      type: "constant"
    }
    num_output: 512
    pad: 1
    kernel_size: 3
  }
}
layer {
  bottom: "conv5_2"
  top: "conv5_2"
  name: "conv5_2_bn"
  type: "BN"
  param {
    lr_mult: 1
    decay_mult: 1
  }
  param {
    lr_mult: 1
    decay_mult: 0
  }
  bn_param {
	scale_filler {
	  type: "constant"
	  value: 1
	}
	shift_filler {
	  type: "constant"
	  value: 0.001
	}
 }
}
layer {
  bottom: "conv5_2"
  top: "conv5_2"
  name: "relu5_2"
  type: "ReLU"
}
layer {
  bottom: "conv5_2"
  top: "conv5_3"
  name: "conv5_3"
  type: "Convolution"
  param {
    lr_mult: 1
    decay_mult: 1
  }
  param {
    lr_mult: 2
    decay_mult: 0
  }
  convolution_param {
    weight_filler {
      type: "msra"
    }
    bias_filler {
      type: "constant"
    }
    num_output: 512
    pad: 1
    kernel_size: 3
  }
}
layer {
  bottom: "conv5_3"
  top: "conv5_3"
  name: "conv5_3_bn"
  type: "BN"
  param {
    lr_mult: 1
    decay_mult: 1
  }
  param {
    lr_mult: 1
    decay_mult: 0
  }
  bn_param {
	scale_filler {
	  type: "constant"
	  value: 1
	}
	shift_filler {
	  type: "constant"
	  value: 0.001
	}
 }
}
layer {
  bottom: "conv5_3"
  top: "conv5_3"
  name: "relu5_3"
  type: "ReLU"
}
layer {
  bottom: "conv5_3"
  top: "pool5"
  top: "pool5_mask"
  name: "pool5"
  type: "Pooling"
  pooling_param {
    pool: MAX
    kernel_size: 2
    stride: 2
  }
}
layer {
  name: "upsample5"
  type: "Upsample"
  bottom: "pool5"
  top: "pool5_D"
  bottom: "pool5_mask"
  upsample_param {
    scale: 2
    upsample_w: 30
    upsample_h: 23
  }
}
layer {
  bottom: "pool5_D"
  top: "conv5_3_D"
  name: "conv5_3_D"
  type: "Convolution"
  param {
    lr_mult: 1
    decay_mult: 1
  }
  param {
    lr_mult: 2
    decay_mult: 0
  }
  convolution_param {
    weight_filler {
      type: "msra"
    }
    bias_filler {
      type: "constant"
    }
    num_output: 512
    pad: 1
    kernel_size: 3
  }
}
layer {
  bottom: "conv5_3_D"
  top: "conv5_3_D"
  name: "conv5_3_D_bn"
  type: "BN"
  param {
    lr_mult: 1
    decay_mult: 1
  }
  param {
    lr_mult: 1
    decay_mult: 0
  }
  bn_param {
	scale_filler {
	  type: "constant"
	  value: 1
	}
	shift_filler {
	  type: "constant"
	  value: 0.001
	}
 }
}
layer {
  bottom: "conv5_3_D"
  top: "conv5_3_D"
  name: "relu5_3_D"
  type: "ReLU"
}

layer {
  bottom: "conv5_3_D"
  top: "conv5_2_D"
  name: "conv5_2_D"
  type: "Convolution"
  param {
    lr_mult: 1
    decay_mult: 1
  }
  param {
    lr_mult: 2
    decay_mult: 0
  }
  convolution_param {
    weight_filler {
      type: "msra"
    }
    bias_filler {
      type: "constant"
    }
    num_output: 512
    pad: 1
    kernel_size: 3
  }
}
layer {
  bottom: "conv5_2_D"
  top: "conv5_2_D"
  name: "conv5_2_D_bn"
  type: "BN"
  param {
    lr_mult: 1
    decay_mult: 1
  }
  param {
    lr_mult: 1
    decay_mult: 0
  }
  bn_param {
	scale_filler {
	  type: "constant"
	  value: 1
	}
	shift_filler {
	  type: "constant"
	  value: 0.001
	}
 }
}
layer {
  bottom: "conv5_2_D"
  top: "conv5_2_D"
  name: "relu5_2_D"
  type: "ReLU"
}
layer {
  bottom: "conv5_2_D"
  top: "conv5_1_D"
  name: "conv5_1_D"
  type: "Convolution"
  param {
    lr_mult: 1
    decay_mult: 1
  }
  param {
    lr_mult: 2
    decay_mult: 0
  }
  convolution_param {
    weight_filler {
      type: "msra"
    }
    bias_filler {
      type: "constant"
    }
    num_output: 512
    pad: 1
    kernel_size: 3
  }
}
layer {
  bottom: "conv5_1_D"
  top: "conv5_1_D"
  name: "conv5_1_D_bn"
  type: "BN"
  param {
    lr_mult: 1
    decay_mult: 1
  }
  param {
    lr_mult: 1
    decay_mult: 0
  }
  bn_param {
	scale_filler {
	  type: "constant"
	  value: 1
	}
	shift_filler {
	  type: "constant"
	  value: 0.001
	}
 }
}
layer {
  bottom: "conv5_1_D"
  top: "conv5_1_D"
  name: "relu5_1_D"
  type: "ReLU"
}
layer {
  name: "upsample4"
  type: "Upsample"
  bottom: "conv5_1_D"
  top: "pool4_D"
  bottom: "pool4_mask"
  upsample_param {
    scale: 2
    upsample_w: 60
    upsample_h: 45
  }
}
layer {
  bottom: "pool4_D"
  top: "conv4_3_D"
  name: "conv4_3_D"
  type: "Convolution"
  param {
    lr_mult: 1
    decay_mult: 1
  }
  param {
    lr_mult: 2
    decay_mult: 0
  }
  convolution_param {
    weight_filler {
      type: "msra"
    }
    bias_filler {
      type: "constant"
    }
    num_output: 512
    pad: 1
    kernel_size: 3
  }
}
layer {
  bottom: "conv4_3_D"
  top: "conv4_3_D"
  name: "conv4_3_D_bn"
  type: "BN"
  param {
    lr_mult: 1
    decay_mult: 1
  }
  param {
    lr_mult: 1
    decay_mult: 0
  }
  bn_param {
	scale_filler {
	  type: "constant"
	  value: 1
	}
	shift_filler {
	  type: "constant"
	  value: 0.001
	}
 }
}
layer {
  bottom: "conv4_3_D"
  top: "conv4_3_D"
  name: "relu4_3_D"
  type: "ReLU"
}
layer {
  bottom: "conv4_3_D"
  top: "conv4_2_D"
  name: "conv4_2_D"
  type: "Convolution"
  param {
    lr_mult: 1
    decay_mult: 1
  }
  param {
    lr_mult: 2
    decay_mult: 0
  }
  convolution_param {
    weight_filler {
      type: "msra"
    }
    bias_filler {
      type: "constant"
    }
    num_output: 512
    pad: 1
    kernel_size: 3
  }
}
layer {
  bottom: "conv4_2_D"
  top: "conv4_2_D"
  name: "conv4_2_D_bn"
  type: "BN"
  param {
    lr_mult: 1
    decay_mult: 1
  }
  param {
    lr_mult: 1
    decay_mult: 0
  }
  bn_param {
	scale_filler {
	  type: "constant"
	  value: 1
	}
	shift_filler {
	  type: "constant"
	  value: 0.001
	}
 }
}
layer {
  bottom: "conv4_2_D"
  top: "conv4_2_D"
  name: "relu4_2_D"
  type: "ReLU"
}
layer {
  bottom: "conv4_2_D"
  top: "conv4_1_D"
  name: "conv4_1_D"
  type: "Convolution"
  param {
    lr_mult: 1
    decay_mult: 1
  }
  param {
    lr_mult: 2
    decay_mult: 0
  }
  convolution_param {
    weight_filler {
      type: "msra"
    }
    bias_filler {
      type: "constant"
    }
    num_output: 256
    pad: 1
    kernel_size: 3
  }
}
layer {
  bottom: "conv4_1_D"
  top: "conv4_1_D"
  name: "conv4_1_D_bn"
  type: "BN"
  param {
    lr_mult: 1
    decay_mult: 1
  }
  param {
    lr_mult: 1
    decay_mult: 0
  }
  bn_param {
	scale_filler {
	  type: "constant"
	  value: 1
	}
	shift_filler {
	  type: "constant"
	  value: 0.001
	}
 }
}
layer {
  bottom: "conv4_1_D"
  top: "conv4_1_D"
  name: "relu4_1_D"
  type: "ReLU"
}
layer {
  name: "upsample3"
  type: "Upsample"
  bottom: "conv4_1_D"
  top: "pool3_D"
  bottom: "pool3_mask"
  upsample_param {
    scale: 2
  }
}
layer {
  bottom: "pool3_D"
  top: "conv3_3_D"
  name: "conv3_3_D"
  type: "Convolution"
  param {
    lr_mult: 1
    decay_mult: 1
  }
  param {
    lr_mult: 2
    decay_mult: 0
  }
  convolution_param {
    weight_filler {
      type: "msra"
    }
    bias_filler {
      type: "constant"
    }
    num_output: 256
    pad: 1
    kernel_size: 3
  }
}
layer {
  bottom: "conv3_3_D"
  top: "conv3_3_D"
  name: "conv3_3_D_bn"
  type: "BN"
  param {
    lr_mult: 1
    decay_mult: 1
  }
  param {
    lr_mult: 1
    decay_mult: 0
  }
  bn_param {
	scale_filler {
	  type: "constant"
	  value: 1
	}
	shift_filler {
	  type: "constant"
	  value: 0.001
	}
 }
}
layer {
  bottom: "conv3_3_D"
  top: "conv3_3_D"
  name: "relu3_3_D"
  type: "ReLU"
}
layer {
  bottom: "conv3_3_D"
  top: "conv3_2_D"
  name: "conv3_2_D"
  type: "Convolution"
  param {
    lr_mult: 1
    decay_mult: 1
  }
  param {
    lr_mult: 2
    decay_mult: 0
  }
  convolution_param {
    weight_filler {
      type: "msra"
    }
    bias_filler {
      type: "constant"
    }
    num_output: 256
    pad: 1
    kernel_size: 3
  }
}
layer {
  bottom: "conv3_2_D"
  top: "conv3_2_D"
  name: "conv3_2_D_bn"
  type: "BN"
  param {
    lr_mult: 1
    decay_mult: 1
  }
  param {
    lr_mult: 1
    decay_mult: 0
  }
  bn_param {
	scale_filler {
	  type: "constant"
	  value: 1
	}
	shift_filler {
	  type: "constant"
	  value: 0.001
	}
 }
}
layer {
  bottom: "conv3_2_D"
  top: "conv3_2_D"
  name: "relu3_2_D"
  type: "ReLU"
}
layer {
  bottom: "conv3_2_D"
  top: "conv3_1_D"
  name: "conv3_1_D"
  type: "Convolution"
  param {
    lr_mult: 1
    decay_mult: 1
  }
  param {
    lr_mult: 2
    decay_mult: 0
  }
  convolution_param {
    weight_filler {
      type: "msra"
    }
    bias_filler {
      type: "constant"
    }
    num_output: 128
    pad: 1
    kernel_size: 3
  }
}
layer {
  bottom: "conv3_1_D"
  top: "conv3_1_D"
  name: "conv3_1_D_bn"
  type: "BN"
  param {
    lr_mult: 1
    decay_mult: 1
  }
  param {
    lr_mult: 1
    decay_mult: 0
  }
  bn_param {
	scale_filler {
	  type: "constant"
	  value: 1
	}
	shift_filler {
	  type: "constant"
	  value: 0.001
	}
 }
}
layer {
  bottom: "conv3_1_D"
  top: "conv3_1_D"
  name: "relu3_1_D"
  type: "ReLU"
}
layer {
  name: "upsample2"
  type: "Upsample"
  bottom: "conv3_1_D"
  top: "pool2_D"
  bottom: "pool2_mask"
  upsample_param {
    scale: 2
  }
}
layer {
  bottom: "pool2_D"
  top: "conv2_2_D"
  name: "conv2_2_D"
  type: "Convolution"
  param {
    lr_mult: 1
    decay_mult: 1
  }
  param {
    lr_mult: 2
    decay_mult: 0
  }
  convolution_param {
    weight_filler {
      type: "msra"
    }
    bias_filler {
      type: "constant"
    }
    num_output: 128
    pad: 1
    kernel_size: 3
  }
}
layer {
  bottom: "conv2_2_D"
  top: "conv2_2_D"
  name: "conv2_2_D_bn"
  type: "BN"
  param {
    lr_mult: 1
    decay_mult: 1
  }
  param {
    lr_mult: 1
    decay_mult: 0
  }
  bn_param {
	scale_filler {
	  type: "constant"
	  value: 1
	}
	shift_filler {
	  type: "constant"
	  value: 0.001
	}
 }
}
layer {
  bottom: "conv2_2_D"
  top: "conv2_2_D"
  name: "relu2_2_D"
  type: "ReLU"
}
layer {
  bottom: "conv2_2_D"
  top: "conv2_1_D"
  name: "conv2_1_D"
  type: "Convolution"
  param {
    lr_mult: 1
    decay_mult: 1
  }
  param {
    lr_mult: 2
    decay_mult: 0
  }
  convolution_param {
    weight_filler {
      type: "msra"
    }
    bias_filler {
      type: "constant"
    }
    num_output: 64
    pad: 1
    kernel_size: 3
  }
}
layer {
  bottom: "conv2_1_D"
  top: "conv2_1_D"
  name: "conv2_1_D_bn"
  type: "BN"
  param {
    lr_mult: 1
    decay_mult: 1
  }
  param {
    lr_mult: 1
    decay_mult: 0
  }
  bn_param {
	scale_filler {
	  type: "constant"
	  value: 1
	}
	shift_filler {
	  type: "constant"
	  value: 0.001
	}
 }
}
layer {
  bottom: "conv2_1_D"
  top: "conv2_1_D"
  name: "relu2_1_D"
  type: "ReLU"
}
layer {
  name: "upsample1"
  type: "Upsample"
  bottom: "conv2_1_D"
  top: "pool1_D"
  bottom: "pool1_mask"
  upsample_param {
    scale: 2
  }
}
layer {
  bottom: "pool1_D"
  top: "conv1_2_D"
  name: "conv1_2_D"
  type: "Convolution"
  param {
    lr_mult: 1
    decay_mult: 1
  }
  param {
    lr_mult: 2
    decay_mult: 0
  }
  convolution_param {
    weight_filler {
      type: "msra"
    }
    bias_filler {
      type: "constant"
    }
    num_output: 64
    pad: 1
    kernel_size: 3
  }
}
layer {
  bottom: "conv1_2_D"
  top: "conv1_2_D"
  name: "conv1_2_D_bn"
  type: "BN"
  param {
    lr_mult: 1
    decay_mult: 1
  }
  param {
    lr_mult: 1
    decay_mult: 0
  }
  bn_param {
	scale_filler {
	  type: "constant"
	  value: 1
	}
	shift_filler {
	  type: "constant"
	  value: 0.001
	}
 }
}
layer {
  bottom: "conv1_2_D"
  top: "conv1_2_D"
  name: "relu1_2_D"
  type: "ReLU"
}
layer {
  bottom: "conv1_2_D"
  top: "conv1_1_D"
  name: "conv1_1_D"
  type: "Convolution"
  param {
    lr_mult: 1
    decay_mult: 1
  }
  param {
    lr_mult: 2
    decay_mult: 0
  }
  convolution_param {
    weight_filler {
      type: "msra"
    }
    bias_filler {
      type: "constant"
    }
    num_output: 11
    pad: 1
    kernel_size: 3
  }
}
layer {
  name: "loss"
  type: "SoftmaxWithLoss"
  bottom: "conv1_1_D"
  bottom: "label"
  top: "loss"
  softmax_param {engine: CAFFE}
  loss_param: {
    weight_by_label_freqs: true
    ignore_label: 11
    class_weighting: 0.2595
    class_weighting: 0.1826
    class_weighting: 4.5640
    class_weighting: 0.1417
    class_weighting: 0.9051
    class_weighting: 0.3826
    class_weighting: 9.6446
    class_weighting: 1.8418
    class_weighting: 0.6823
    class_weighting: 6.2478
    class_weighting: 7.3614
  }
}
layer {
  name: "accuracy"
  type: "Accuracy"
  bottom: "conv1_1_D"
  bottom: "label"
  top: "accuracy"
  top: "per_class_accuracy"
}