VGGJoint2.prototxt

name: "VGG_ILSVRC_16_layers"


layer {
  name: "data"
  type: "ImageData"
  top: "data"
  top: "label"
  include {
    phase: TRAIN
  }
  transform_param {
    crop_size: 224
    mean_value: 104
    mean_value: 117
    mean_value: 123


  }
  image_data_param {
    source: "data/ilsvrc12/train2.txt"
    batch_size: 16
    new_height: 256
    new_width: 256
  }
}


layer {
  name: "data_c"
  type: "ImageData"
  top: "data_c"
 top: "label_c"
 
  include {
    phase: TRAIN
  }
  transform_param {
    mean_value: 104
    mean_value: 117
    mean_value: 123
    crop_size: 224
  }
  image_data_param {
    source: "examples/DeepOneClass/train.txt"
    batch_size: 16
    new_height: 256
    new_width: 256
  }
}






layer {
  name: "data"
  type: "ImageData"
  top: "data"
  top: "label"
  include {
    phase: TEST
  }
  transform_param {
    crop_size: 224
    mean_value: 104
    mean_value: 117
    mean_value: 123

  }
  image_data_param {
    source: "data/ilsvrc12/train2.txt"
    batch_size: 16
    new_height: 256
    new_width: 256
  }
}


layer {
  name: "data_c"
  type: "ImageData"
  top: "data_c"
 top: "label_c"
  include {
    phase: TEST
  }
  transform_param {
    crop_size: 224
    mean_value: 104
    mean_value: 117
    mean_value: 123

  }
  image_data_param {
    source: "examples/DeepOneClass/val.txt"
    batch_size: 16
    new_height: 256
    new_width: 256
  }
}







layer {
  bottom: "data_c"
  top: "conv1_1_c"
  name: "conv1_1_c"
  type: "Convolution"
  convolution_param {
    num_output: 64
    pad: 1
    kernel_size: 3
    weight_filler {
      type: "gaussian"
      std: 0.01
    }
    bias_filler {
      type: "constant"
      value: 0
    }
  }
  param {
    name : "conv1_1w"
    lr_mult: 0
    
  }
  param {
    lr_mult: 0
    name : "conv1_1b"
  }
}

layer {
  bottom: "conv1_1_c"
  top: "conv1_1_c"
  name: "relu1_1_c"
  type: "ReLU"
}
layer {
  bottom: "conv1_1_c"
  top: "conv1_2_c"
  name: "conv1_2_c"
  type: "Convolution"
  convolution_param {
    num_output: 64
    pad: 1
    kernel_size: 3
    weight_filler {
      type: "gaussian"
      std: 0.01
    }
    bias_filler {
      type: "constant"
      value: 0
    }
  }
  param {
    name : "conv1_2w"
    lr_mult: 0
  }
  param {
    name : "conv1_2b"
    lr_mult: 0
  }
}
layer {
  bottom: "conv1_2_c"
  top: "conv1_2_c"
  name: "relu1_2_c"
  type: "ReLU"
}
layer {
  bottom: "conv1_2_c"
  top: "pool1_c"
  name: "pool1_c"
  type: "Pooling"
  pooling_param {
    pool: MAX
    kernel_size: 2
    stride: 2
  }
}
layer {
  bottom: "pool1_c"
  top: "conv2_1_c"
  name: "conv2_1_c"
  type: "Convolution"
  convolution_param {
    num_output: 128
    pad: 1
    kernel_size: 3
      weight_filler {
      type: "gaussian"
      std: 0.01
    }
    bias_filler {
      type: "constant"
      value: 0
    }
  }
  param {
  name : "conv2_1w"
    lr_mult: 0
  }
  param {
   name : "conv2_1b"
   lr_mult: 0
  }
}
layer {
  bottom: "conv2_1_c"
  top: "conv2_1_c"
  name: "relu2_1_c"
  type: "ReLU"
}
layer {
  bottom: "conv2_1_c"
  top: "conv2_2_c"
  name: "conv2_2_c"
  type: "Convolution"
  convolution_param {
    num_output: 128
    pad: 1
    kernel_size: 3
    weight_filler {
      type: "gaussian"
      std: 0.01
    }
    bias_filler {
      type: "constant"
      value: 0
    }
  }
  param {
 name : "conv2_2w"
    lr_mult: 0
  }
  param {
 name : "conv2_2b"
    lr_mult: 0
  }
}
layer {
  bottom: "conv2_2_c"
  top: "conv2_2_c"
  name: "relu2_2_c"
  type: "ReLU"
}
layer {
  bottom: "conv2_2_c"
  top: "pool2_c"
  name: "pool2_c"
  type: "Pooling"
  pooling_param {
    pool: MAX
    kernel_size: 2
    stride: 2
  }
}
layer {
  bottom: "pool2_c"
  top: "conv3_1_c"
  name: "conv3_1_c"
  type: "Convolution"
  convolution_param {
    num_output: 256
    pad: 1
    kernel_size: 3
    weight_filler {
      type: "gaussian"
      std: 0.01
    }
    bias_filler {
      type: "constant"
      value: 0
    }
  }
  param {
    lr_mult: 0
 name : "conv3_1w"
  }
  param {
 name : "conv3_1b"
    lr_mult: 0
  }
}
layer {
  bottom: "conv3_1_c"
  top: "conv3_1_c"
  name: "relu3_1_c"
  type: "ReLU"
}
layer {
  bottom: "conv3_1_c"
  top: "conv3_2_c"
  name: "conv3_2_c"
  type: "Convolution"
  convolution_param {
    num_output: 256
    pad: 1
    kernel_size: 3
    weight_filler {
      type: "gaussian"
      std: 0.01
    }
    bias_filler {
      type: "constant"
      value: 0
    }
  }
  param {
 name : "conv3_2w"
    lr_mult: 0
  }
  param {
 name : "conv3_2b"
    lr_mult: 0
  }
}
layer {
  bottom: "conv3_2_c"
  top: "conv3_2_c"
  name: "relu3_2_c"
  type: "ReLU"
}
layer {
  bottom: "conv3_2_c"
  top: "conv3_3_c"
  name: "conv3_3_c"
  type: "Convolution"
  convolution_param {
    num_output: 256
    pad: 1
    kernel_size: 3
    weight_filler {
      type: "gaussian"
      std: 0.01
    }
    bias_filler {
      type: "constant"
      value: 0
    }
  }
  param {
 name : "conv3_3w"
    lr_mult: 0
  }
  param {
 name : "conv3_3b"
    lr_mult: 0
  }
}
layer {
  bottom: "conv3_3_c"
  top: "conv3_3_c"
  name: "relu3_3_c"
  type: "ReLU"
}
layer {
  bottom: "conv3_3_c"
  top: "pool3_c"
  name: "pool3_c"
  type: "Pooling"
  pooling_param {
    pool: MAX
    kernel_size: 2
    stride: 2
  }
}
layer {
  bottom: "pool3_c"
  top: "conv4_1_c"
  name: "conv4_1_c"
  type: "Convolution"
  convolution_param {
    num_output: 512
    pad: 1
    kernel_size: 3
    weight_filler {
      type: "gaussian"
      std: 0.01
    }
    bias_filler {
      type: "constant"
      value: 0
    }
  }
  param {
 name : "conv4_1w"
    lr_mult: 0
  }
  param {
 name : "conv4_1b"
    lr_mult: 0
  }
}
layer {
  bottom: "conv4_1_c"
  top: "conv4_1_c"
  name: "relu4_1_c"
  type: "ReLU"
}
layer {
  bottom: "conv4_1_c"
  top: "conv4_2_c"
  name: "conv4_2_c"
  type: "Convolution"
  convolution_param {
    num_output: 512
    pad: 1
    kernel_size: 3
    weight_filler {
      type: "gaussian"
      std: 0.01
    }
    bias_filler {
      type: "constant"
      value: 0
    }
  }
  param {
 name : "conv4_2w"
    lr_mult: 0
  }
  param {
 name : "conv4_2b"
    lr_mult: 0
  }
}
layer {
  bottom: "conv4_2_c"
  top: "conv4_2_c"
  name: "relu4_2_c"
  type: "ReLU"
}
layer {
  bottom: "conv4_2_c"
  top: "conv4_3_c"
  name: "conv4_3_c"
  type: "Convolution"
  convolution_param {
    num_output: 512
    pad: 1
    kernel_size: 3
    weight_filler {
      type: "gaussian"
      std: 0.01
    }
    bias_filler {
      type: "constant"
      value: 0
    }
  }
  param {
 name : "conv4_3w"
    lr_mult: 0
  }
  param {
 name : "conv4_3b"
    lr_mult: 0
  }
}
layer {
  bottom: "conv4_3_c"
  top: "conv4_3_c"
  name: "relu4_3_c"
  type: "ReLU"
}
layer {
  bottom: "conv4_3_c"
  top: "pool4_c"
  name: "pool4_c"
  type: "Pooling"
  pooling_param {
    pool: MAX
    kernel_size: 2
    stride: 2
  }
}
layer {
  bottom: "pool4_c"
  top: "conv5_1_c"
  name: "conv5_1_c"
  type: "Convolution"
  convolution_param {
    num_output: 512
    pad: 1
    kernel_size: 3
    weight_filler {
      type: "gaussian"
      std: 0.01
    }
    bias_filler {
      type: "constant"
      value: 0
    }
  }
  param {
 name : "conv5_1w"
    lr_mult: 1
  }
  param {
 name : "conv5_1b"
    lr_mult: 1
  }
}
layer {
  bottom: "conv5_1_c"
  top: "conv5_1_c"
  name: "relu5_1_c"
  type: "ReLU"
}
layer {
  bottom: "conv5_1_c"
  top: "conv5_2_c"
  name: "conv5_2_c"
  type: "Convolution"
  convolution_param {
    num_output: 512
    pad: 1
    kernel_size: 3
    weight_filler {
      type: "gaussian"
      std: 0.01
    }
    bias_filler {
      type: "constant"
      value: 0
    }
  }
  param {
 name : "conv5_2w"
    lr_mult: 1
  }
  param {
 name : "conv5_2b"
    lr_mult: 1
  }
}
layer {
  bottom: "conv5_2_c"
  top: "conv5_2_c"
  name: "relu5_2_c"
  type: "ReLU"
}
layer {
  bottom: "conv5_2_c"
  top: "conv5_3_c"
  name: "conv5_3_c"
  type: "Convolution"
  convolution_param {
    num_output: 512
    pad: 1
    kernel_size: 3
    weight_filler {
      type: "gaussian"
      std: 0.01
    }
    bias_filler {
      type: "constant"
      value: 0
    }
  }
  param {
 name : "conv5_3w"
    lr_mult: 1
  }
  param {
 name : "conv5_3b"
    lr_mult: 1
  }
}
layer {
  bottom: "conv5_3_c"
  top: "conv5_3_c"
  name: "relu5_3_c"
  type: "ReLU"
}
layer {
  bottom: "conv5_3_c"
  top: "pool5_c"
  name: "pool5_c"
  type: "Pooling"
  pooling_param {
    pool: MAX
    kernel_size: 2
    stride: 2
  }
}
layer {
  bottom: "pool5_c"
  top: "fc6_c"
  name: "fc6_c"
  type: "InnerProduct"
  inner_product_param {
    num_output: 4096
  }
  param {
    name: "fc6_w"
    lr_mult: 1
  }
  param {
    name: "fc6_b"
    lr_mult: 1
  }
}
layer {
  bottom: "fc6_c"
  top: "fc6_c"
  name: "relu6_c"
  type: "ReLU"
}
layer {
  bottom: "fc6_c"
  top: "fc6_c"
  name: "drop6_c"
  type: "Dropout"
  dropout_param {
    dropout_ratio: 0.5
  }
}
layer {
  bottom: "fc6_c"
  top: "fc7_c"
  name: "fc7_c"
  type: "InnerProduct"
  inner_product_param {
    num_output: 4096
  }
  param {
    name: "fc7_w"
    lr_mult: 1
  }
  param {
    name: "fc7_b"
    lr_mult: 1
  }
}
layer {
  bottom: "fc7_c"
  top: "fc7_c"
  name: "relu7_c"
  type: "ReLU"
}
layer {
  bottom: "fc7_c"
  top: "fc7_c"
  name: "drop7_c"
  type: "Dropout"
  dropout_param {
    dropout_ratio: 0.5
  }
}







layer {
  bottom: "data"
  top: "conv1_1"
  name: "conv1_1"
  type: "Convolution"
  convolution_param {
    num_output: 64
    pad: 1
    kernel_size: 3
  }
  param {

    lr_mult: 0
    name : "conv1_1w"
  }
  param {
    lr_mult: 0
    name : "conv1_1b"
  }
}

layer {
  bottom: "conv1_1"
  top: "conv1_1"
  name: "relu1_1"
  type: "ReLU"
}
layer {
  bottom: "conv1_1"
  top: "conv1_2"
  name: "conv1_2"
  type: "Convolution"
  convolution_param {
    num_output: 64
    pad: 1
    kernel_size: 3
  }
  param {
    name : "conv1_2w"
    lr_mult: 0
  }
  param {
    name : "conv1_2b"
    lr_mult: 0
  }
}
layer {
  bottom: "conv1_2"
  top: "conv1_2"
  name: "relu1_2"
  type: "ReLU"
}
layer {
  bottom: "conv1_2"
  top: "pool1"
  name: "pool1"
  type: "Pooling"
  pooling_param {
    pool: MAX
    kernel_size: 2
    stride: 2
  }
}
layer {
  bottom: "pool1"
  top: "conv2_1"
  name: "conv2_1"
  type: "Convolution"
  convolution_param {
    num_output: 128
    pad: 1
    kernel_size: 3
  }
  param {
  name : "conv2_1w"
    lr_mult: 0
  }
  param {
   name : "conv2_1b"
   lr_mult: 0
  }
}
layer {
  bottom: "conv2_1"
  top: "conv2_1"
  name: "relu2_1"
  type: "ReLU"
}
layer {
  bottom: "conv2_1"
  top: "conv2_2"
  name: "conv2_2"
  type: "Convolution"
  convolution_param {
    num_output: 128
    pad: 1
    kernel_size: 3
  }
  param {
 name : "conv2_2w"
    lr_mult: 0
  }
  param {
 name : "conv2_2b"
    lr_mult: 0
  }
}
layer {
  bottom: "conv2_2"
  top: "conv2_2"
  name: "relu2_2"
  type: "ReLU"
}
layer {
  bottom: "conv2_2"
  top: "pool2"
  name: "pool2"
  type: "Pooling"
  pooling_param {
    pool: MAX
    kernel_size: 2
    stride: 2
  }
}
layer {
  bottom: "pool2"
  top: "conv3_1"
  name: "conv3_1"
  type: "Convolution"
  convolution_param {
    num_output: 256
    pad: 1
    kernel_size: 3
  }
  param {
    lr_mult: 0
 name : "conv3_1w"
  }
  param {
 name : "conv3_1b"
    lr_mult: 0
  }
}
layer {
  bottom: "conv3_1"
  top: "conv3_1"
  name: "relu3_1"
  type: "ReLU"
}
layer {
  bottom: "conv3_1"
  top: "conv3_2"
  name: "conv3_2"
  type: "Convolution"
  convolution_param {
    num_output: 256
    pad: 1
    kernel_size: 3
  }
  param {
 name : "conv3_2w"
    lr_mult: 0
  }
  param {
 name : "conv3_2b"
    lr_mult: 0
  }
}
layer {
  bottom: "conv3_2"
  top: "conv3_2"
  name: "relu3_2"
  type: "ReLU"
}
layer {
  bottom: "conv3_2"
  top: "conv3_3"
  name: "conv3_3"
  type: "Convolution"
  convolution_param {
    num_output: 256
    pad: 1
    kernel_size: 3
  }
  param {
 name : "conv3_3w"
    lr_mult: 0
  }
  param {
 name : "conv3_3b"
    lr_mult: 0
  }
}
layer {
  bottom: "conv3_3"
  top: "conv3_3"
  name: "relu3_3"
  type: "ReLU"
}
layer {
  bottom: "conv3_3"
  top: "pool3"
  name: "pool3"
  type: "Pooling"
  pooling_param {
    pool: MAX
    kernel_size: 2
    stride: 2
  }
}
layer {
  bottom: "pool3"
  top: "conv4_1"
  name: "conv4_1"
  type: "Convolution"
  convolution_param {
    num_output: 512
    pad: 1
    kernel_size: 3
  }
  param {
 name : "conv4_1w"
    lr_mult: 0
  }
  param {
 name : "conv4_1b"
    lr_mult: 0
  }
}
layer {
  bottom: "conv4_1"
  top: "conv4_1"
  name: "relu4_1"
  type: "ReLU"
}
layer {
  bottom: "conv4_1"
  top: "conv4_2"
  name: "conv4_2"
  type: "Convolution"
  convolution_param {
    num_output: 512
    pad: 1
    kernel_size: 3
  }
  param {
 name : "conv4_2w"
    lr_mult: 0
  }
  param {
 name : "conv4_2b"
    lr_mult: 0
  }
}
layer {
  bottom: "conv4_2"
  top: "conv4_2"
  name: "relu4_2"
  type: "ReLU"
}
layer {
  bottom: "conv4_2"
  top: "conv4_3"
  name: "conv4_3"
  type: "Convolution"
  convolution_param {
    num_output: 512
    pad: 1
    kernel_size: 3
  }
  param {
 name : "conv4_3w"
    lr_mult: 0
  }
  param {
 name : "conv4_3b"
    lr_mult: 0
  }
}
layer {
  bottom: "conv4_3"
  top: "conv4_3"
  name: "relu4_3"
  type: "ReLU"
}
layer {
  bottom: "conv4_3"
  top: "pool4"
  name: "pool4"
  type: "Pooling"
  pooling_param {
    pool: MAX
    kernel_size: 2
    stride: 2
  }
}
layer {
  bottom: "pool4"
  top: "conv5_1"
  name: "conv5_1"
  type: "Convolution"
  convolution_param {
    num_output: 512
    pad: 1
    kernel_size: 3
  }
  param {
 name : "conv5_1w"
    lr_mult: 1
  }
  param {
 name : "conv5_1b"
    lr_mult: 1
  }
}
layer {
  bottom: "conv5_1"
  top: "conv5_1"
  name: "relu5_1"
  type: "ReLU"
}
layer {
  bottom: "conv5_1"
  top: "conv5_2"
  name: "conv5_2"
  type: "Convolution"
  convolution_param {
    num_output: 512
    pad: 1
    kernel_size: 3
  }
  param {
 name : "conv5_2w"
    lr_mult: 1
  }
  param {
 name : "conv5_2b"
    lr_mult: 1
  }
}
layer {
  bottom: "conv5_2"
  top: "conv5_2"
  name: "relu5_2"
  type: "ReLU"
}
layer {
  bottom: "conv5_2"
  top: "conv5_3"
  name: "conv5_3"
  type: "Convolution"
  convolution_param {
    num_output: 512
    pad: 1
    kernel_size: 3
  }
  param {
 name : "conv5_3w"
    lr_mult: 1
  }
  param {
 name : "conv5_3b"
    lr_mult: 1
  }
}
layer {
  bottom: "conv5_3"
  top: "conv5_3"
  name: "relu5_3"
  type: "ReLU"
}
layer {
  bottom: "conv5_3"
  top: "pool5"
  name: "pool5"
  type: "Pooling"
  pooling_param {
    pool: MAX
    kernel_size: 2
    stride: 2
  }
}
layer {
  bottom: "pool5"
  top: "fc6"
  name: "fc6"
  type: "InnerProduct"
  inner_product_param {
    num_output: 4096
  }
  param {
    lr_mult: 1    
    name: "fc6_w"
  }
  param {
    name: "fc6_b"
    lr_mult: 1
  }
}
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"
  inner_product_param {
    num_output: 4096
  }
  param {
    lr_mult: 1
    name: "fc7_w"
  }
  param {
    lr_mult: 1
    name: "fc7_b"
  }
}
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 {
  name: "fc8"
  bottom: "fc7"
  top: "fc8"
  type: "InnerProduct"
  inner_product_param {
    num_output: 1000
  }
  param {
    lr_mult: 1
    name: "fc8_w"
  }
  param {
    lr_mult: 1
    name: "fc8_b"
  }
}
layer {
  name: "loss"
  type: "SoftmaxWithLoss"
  bottom: "fc8"
  bottom: "label"
  top: "loss"
  loss_weight: 1
}


layer {
  name: "accuracy/top1"
  type: "Accuracy"
  bottom: "fc8"
  bottom: "label"
  top: "accuracy@1"
  include: { phase: TEST }
  accuracy_param {
    top_k: 1
  }
}
layer {
  name: "accuracy/top5"
  type: "Accuracy"
  bottom: "fc8"
  bottom: "label"
  top: "accuracy@5"
  include: { phase: TEST }
  accuracy_param {
    top_k: 5
}
}


layer {
  name: "silence_layer"
  type: "Silence"
  bottom: "label_c"
  }



layer {
  name: "fc8_c"
  bottom: "fc7_c"
  top: "fc8_c"
  type: "InnerProduct"
  inner_product_param {
    num_output: 1000
  }
  param {
    lr_mult: 1
    name: "fc8_w"
  }
  param {
    lr_mult: 1
    name: "fc8_b"
  }
}





 layer {
        name: "loss"
        type: "Python"
        bottom: "fc8_c"
        top: "l2Error"
        python_param {
            module: "distance_layer"
            layer: "DistanceLayer"
        }
	loss_weight: 0.1
    }