BTCM_train.prototxt

name: "BTCM_4_layers"
input: "data"
input_dim: __
input_dim: __
input_dim: ___
input_dim: ___
}
layer {
  name: "norm"
  type: "LRN"
  bottom: "data"
  top: "norm"
  lrn_param {
    local_size: 5
    alpha: 0.0001
    beta: 0.75
  }
}
layer {
  name: "conv1"
  type: "Convolution"
  bottom: "norm"
  top: "conv1"
  param {
    lr_mult: 1
    decay_mult: 1
  }
  param {
    lr_mult: 2
    decay_mult: 0
  }
  convolution_param {
    num_output: 64
    kernel_size: 7
    pad: 3
    weight_filler {
      type: "msra"
    }
    bias_filler {
      type: "constant"
    }
  }
}
layer {
  bottom: "conv1"
  top: "conv1"
  name: "conv1_bn"
  type: "BN"
  bn_param {
  scale_filler {
    type: "constant"
    value: 1
  }
  shift_filler {
    type: "constant"
    value: 0.001
  }
 }
}
layer {
  name: "Elu1"
  type: "ELU"
  bottom: "conv1"
  top: "conv1"
}
layer {
  name: "pool1"
  type: "Pooling"
  bottom: "conv1"
  top: "pool1"
  top: "pool1_mask"
  pooling_param {
    pool: MAX
    kernel_size: 2
    stride: 2
  }
}

layer {
  name: "encdrop1"
  type: "Dropout"
  bottom: "pool1"
  top: "pool1"
  dropout_param {
    dropout_ratio: 0.5
  }
}
layer {
  name: "conv2"
  type: "Convolution"
  bottom: "pool1"
  top: "conv2"
  param {
    lr_mult: 1
    decay_mult: 1
  }
  param {
    lr_mult: 2
    decay_mult: 0
  }
  convolution_param {
    num_output: 64
    kernel_size: 7
    pad: 3
    weight_filler {
      type: "msra"
    }
    bias_filler {
      type: "constant"
    }
  }
}
layer {
  bottom: "conv2"
  top: "conv2"
  name: "conv2_bn"
  type: "BN"
  bn_param {
  scale_filler {
    type: "constant"
    value: 1
  }
  shift_filler {
    type: "constant"
    value: 0.001
  }
 }
}
layer {
  name: "Elu2"
  type: "ELU"
  bottom: "conv2"
  top: "conv2"
}
layer {
  name: "pool2"
  type: "Pooling"
  bottom: "conv2"
  top: "pool2"
  top: "pool2_mask"
  pooling_param {
    pool: MAX
    kernel_size: 2
    stride: 2
  }
}
layer {
  name: "encdrop2"
  type: "Dropout"
  bottom: "pool2"
  top: "pool2"
  dropout_param {
    dropout_ratio: 0.5
  }
}
layer {
  name: "conv3"
  type: "Convolution"
  bottom: "pool2"
  top: "conv3"
  param {
    lr_mult: 1
    decay_mult: 1
  }
  param {
    lr_mult: 2
    decay_mult: 0
  }
  convolution_param {
    num_output: 64
    kernel_size: 7
    pad: 3
    weight_filler {
      type: "msra"
    }
    bias_filler {
      type: "constant"
    }
  }
}
layer {
  bottom: "conv3"
  top: "conv3"
  name: "conv3_bn"
  type: "BN"
  bn_param {
  scale_filler {
    type: "constant"
    value: 1
  }
  shift_filler {
    type: "constant"
    value: 0.001
  }
 }
}
layer {
  name: "Elu3"
  type: "ELU"
  bottom: "conv3"
  top: "conv3"
}
layer {
  name: "pool3"
  type: "Pooling"
  bottom: "conv3"
  top: "pool3"
  top: "pool3_mask"
  pooling_param {
    pool: MAX
    kernel_size: 2
    stride: 2
  }
}
layer {
  name: "encdrop3"
  type: "Dropout"
  bottom: "pool3"
  top: "pool3"
  dropout_param {
    dropout_ratio: 0.5
  }
}
layer {
  name: "conv4"
  type: "Convolution"
  bottom: "pool3"
  top: "conv4"
  param {
    lr_mult: 1
    decay_mult: 1
  }
  param {
    lr_mult: 2
    decay_mult: 0
  }
  convolution_param {
    num_output: 64
    kernel_size: 7
    pad: 3
    weight_filler {
      type: "msra"
    }
    bias_filler {
      type: "constant"
    }
  }
}
layer {
  bottom: "conv4"
  top: "conv4"
  name: "conv4_bn"
  type: "BN"
  bn_param {
  scale_filler {
    type: "constant"
    value: 1
  }
  shift_filler {
    type: "constant"
    value: 0.001
  }
 }
}
layer {
  name: "Elu4"
  type: "ELU"
  bottom: "conv4"
  top: "conv4"
}
layer {
  name: "pool4"
  type: "Pooling"
  bottom: "conv4"
  top: "pool4"
  top: "pool4_mask"
  pooling_param {
    pool: MAX
    kernel_size: 2
    stride: 2
  }
}
layer {
  name: "encdrop4"
  type: "Dropout"
  bottom: "pool4"
  top: "pool4"
  dropout_param {
    dropout_ratio: 0.5
  }
}
layer {
  name: "upsample4"
  type: "Upsample"
  bottom: "pool4"
  bottom: "pool4_mask"
  top: "upsample4"
  upsample_param {
    scale: 2
    pad_out_h: true
  }
}
layer {
  name: "conv_decode4"
  type: "Convolution"
  bottom: "upsample4"
  top: "conv_decode4"
  param {
    lr_mult: 1
    decay_mult: 1
  }
  param {
    lr_mult: 2
    decay_mult: 0
  }
  convolution_param {
    num_output: 64
    kernel_size: 7
    pad: 3
    weight_filler {
      type: "msra"
    }
    bias_filler {
      type: "constant"
    }
  }
}
layer {
  bottom: "conv_decode4"
  top: "conv_decode4"
  name: "conv_decode4_bn"
  type: "BN"
  bn_param {
  scale_filler {
    type: "constant"
    value: 1
  }
  shift_filler {
    type: "constant"
    value: 0.001
  }
 }
}
layer {
  name: "upsample3"
  type: "Upsample"
  bottom: "conv_decode4"
  bottom: "pool3_mask"
  top: "upsample3"
  upsample_param {
    scale: 2
  }
}
layer {
  name: "conv_decode3"
  type: "Convolution"
  bottom: "upsample3"
  top: "conv_decode3"
  param {
    lr_mult: 1
    decay_mult: 1
  }
  param {
    lr_mult: 2
    decay_mult: 0
  }
  convolution_param {
    num_output: 64
    kernel_size: 7
    pad: 3
    weight_filler {
      type: "msra"
    }
    bias_filler {
      type: "constant"
    }
  }
}
layer {
  bottom: "conv_decode3"
  top: "conv_decode3"
  name: "conv_decode3_bn"
  type: "BN"
  bn_param {
  scale_filler {
    type: "constant"
    value: 1
  }
  shift_filler {
    type: "constant"
    value: 0.001
  }
 }
}
layer {
  name: "upsample2"
  type: "Upsample"
  bottom: "conv_decode3"
  bottom: "pool2_mask"
  top: "upsample2"
  upsample_param {
    scale: 2
  }
}
layer {
  name: "conv_decode2"
  type: "Convolution"
  bottom: "upsample2"
  top: "conv_decode2"
  param {
    lr_mult: 1
    decay_mult: 1
  }
  param {
    lr_mult: 2
    decay_mult: 0
  }
  convolution_param {
    num_output: 64
    kernel_size: 7
    pad: 3
    weight_filler {
      type: "msra"
    }
    bias_filler {
      type: "constant"
    }
  }
}
layer {
  bottom: "conv_decode2"
  top: "conv_decode2"
  name: "conv_decode2_bn"
  type: "BN"
  bn_param {
  scale_filler {
    type: "constant"
    value: 1
  }
  shift_filler {
    type: "constant"
    value: 0.001
  }
 }
}
layer {
  name: "upsample1"
  type: "Upsample"
  bottom: "conv_decode2"
  bottom: "pool1_mask"
  top: "upsample1"
  upsample_param {
    scale: 2
  }
}
layer {
  name: "conv_decode1"
  type: "Convolution"
  bottom: "upsample1"
  top: "conv_decode1"
  param {
    lr_mult: 1
    decay_mult: 1
  }
  param {
    lr_mult: 2
    decay_mult: 0
  }
  convolution_param {
    num_output: 64
    kernel_size: 7
    pad: 3
    weight_filler {
      type: "msra"
    }
    bias_filler {
      type: "constant"
    }
  }
}
layer {
  bottom: "conv_decode1"
  top: "conv_decode1"
  name: "conv_decode1_bn"
  type: "BN"
  bn_param {
  scale_filler {
    type: "constant"
    value: 1
  }
  shift_filler {
    type: "constant"
    value: 0.001
  }
 }
}
layer {
  name: "conv_classifier"
  type: "Convolution"
  bottom: "conv_decode1"
  top: "conv_classifier"
  param {
    lr_mult: 1
    decay_mult: 1
  }
  param {
    lr_mult: 2
    decay_mult: 0
  }
  convolution_param {
    num_output: 11
    kernel_size: 1
    weight_filler {
      type: "msra"
    }
    bias_filler {
      type: "constant"
    }
  }
}
layer {
  name: "loss"
  type: "SoftmaxWithLoss"
  bottom: "conv_classifier"
  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: "conv_classifier"
  bottom: "label"
  top: "accuracy"
  top: "per_class_accuracy"
}