co6_5.prototxt

name: "VGG_CNN_M_2048"

input: "data"
input_dim: 100
input_dim: 3
input_dim: 224
input_dim: 224

input: "label"
input_dim: 100
input_dim: 1
input_dim: 1
input_dim: 1

layer {
  bottom: "data"
  top: "conv1"
  name: "conv1"
  type: "Convolution"
  param {
    lr_mult: 1
	decay_mult: 1
  }
  param {
    lr_mult: 2
	decay_mult: 0
  }
  convolution_param {
    num_output: 96
    kernel_size: 7
    stride: 2
  }
}
layer {
  bottom: "conv1"
  top: "conv1"
  name: "relu1"
  type: "ReLU"
}
layer {
  bottom: "conv1"
  top: "norm1"
  name: "norm1"
  type: "LRN"
  lrn_param {
    local_size: 5
    alpha: 0.0005
    beta: 0.75
    k: 2
  }
}
layer {
  bottom: "norm1"
  top: "pool1"
  name: "pool1"
  type: "Pooling"
  pooling_param {
    pool: MAX
    kernel_size: 3
    stride: 2
  }
}
layer {
  bottom: "pool1"
  top: "conv2"
  name: "conv2"
  type: "Convolution"
  param {
    lr_mult: 1
	decay_mult: 1
  }
  param {
    lr_mult: 2
	decay_mult: 0
  }
  convolution_param {
    num_output: 256
    pad: 1
    kernel_size: 5
    stride: 2
  }
}
layer {
  bottom: "conv2"
  top: "conv2"
  name: "relu2"
  type: "ReLU"
}
layer {
  bottom: "conv2"
  top: "norm2"
  name: "norm2"
  type: "LRN"
  lrn_param {
    local_size: 5
    alpha: 0.0005
    beta: 0.75
    k: 2
  }
}
layer {
  bottom: "norm2"
  top: "pool2"
  name: "pool2"
  type: "Pooling"
  pooling_param {
    pool: MAX
    kernel_size: 3
    stride: 2
  }
}
layer {
  bottom: "pool2"
  top: "conv3"
  name: "conv3"
  type: "Convolution"
  param {
    lr_mult: 1
	decay_mult: 1
  }
  param {
    lr_mult: 2
	decay_mult: 0
  }
  convolution_param {
    num_output: 512
    pad: 1
    kernel_size: 3
  }
}
layer {
  bottom: "conv3"
  top: "conv3"
  name: "relu3"
  type: "ReLU"
}
layer {
  bottom: "conv3"
  top: "conv4"
  name: "conv4"
  type: "Convolution"
  param {
    lr_mult: 1
	decay_mult: 1
  }
  param {
    lr_mult: 2
	decay_mult: 0
  }
  convolution_param {
    num_output: 512
    pad: 1
    kernel_size: 3
  }
}
layer {
  bottom: "conv4"
  top: "conv4"
  name: "relu4"
  type: "ReLU"
}
layer {
  bottom: "conv4"
  top: "conv5"
  name: "conv5"
  type: "Convolution"
  param {
    lr_mult: 1
	decay_mult: 1
  }
  param {
    lr_mult: 2
	decay_mult: 0
  }
  convolution_param {
    num_output: 512
    pad: 1
    kernel_size: 3
  }
}
layer {
  bottom: "conv5"
  top: "conv5"
  name: "relu5"
  type: "ReLU"
}
layer {
  bottom: "conv5"
  top: "pool5"
  name: "pool5"
  type: "Pooling"
  pooling_param {
    pool: MAX
    kernel_size: 3
    stride: 2
  }
}
layer {
  bottom: "pool5"
  top: "fc6"
  name: "fc6"
  type: "InnerProduct"
  param {
    lr_mult: 1
	decay_mult: 1
  }
  param {
    lr_mult: 2
	decay_mult: 0
  }
  inner_product_param {
    num_output: 4096
  }
}
layer {
  bottom: "fc6"
  top: "fc6"
  name: "relu6"
  type: "ReLU"
}
layer {
  bottom: "fc6"
  top: "fc6"
  name: "drop6"
  type: "Dropout"
  dropout_param {
    dropout_ratio: 0.5
  }
}
layer {
  bottom: "fc6"
  top: "fc7"
  name: "fc7"
  type: "InnerProduct"
  param {
    lr_mult: 1
	decay_mult: 1
  }
  param {
    lr_mult: 2
	decay_mult: 0
  }
  inner_product_param {
    num_output: 2048
  }
}

layer {
  bottom: "fc7"
  top: "fc7"
  name: "relu7"
  type: "ReLU"
}
layer {
  bottom: "fc7"
  top: "fc7"
  name: "drop7"
  type: "Dropout"
  dropout_param {
    dropout_ratio: 0.5
  }
}

layer {
  bottom: "fc7"
  top: "fcm8"
  name: "fcm8"
  type: "InnerProduct"
  param {
    lr_mult: 1
	decay_mult: 1
  }
  param {
    lr_mult: 2
	decay_mult: 0
  }
  inner_product_param {
    num_output: 1024
	bias_term: true
	weight_filler {
      type: "xavier"
	}
	bias_filler {
	  type: "constant"
	  value: 0
	}
  }
}

## --------

layer {
  name: "co_loss"
  type: "COLoss"
  bottom: "fcm8"
  bottom: "label"
  top: "closs"
  top: "oloss"
  loss_weight: 1
  loss_weight: 0.03
  param {
    lr_mult: 1
    decay_mult: 1
  }
  co_loss_param {
	num_output: 40
    cutoff: 0
    delta: 1
    center_filler {
      type: "gaussian"
      std: 0.01
    }
  }
}