FFN.prototxt

name: "mix"
layer {
  name: "data"
  type: "ImageData"
  top: "data"
  top: "label"
  include {
    phase: TRAIN
  }
  transform_param {
    mirror: true
    crop_size: 227
    mean_file: "data/ilsvrc12/imagenet_mean.binaryproto"
  }
  image_data_param {
    source: "/home/shangxuan/feature_YC/train_filename_wrongfile1_631.txt"
    batch_size: 10
    new_height: 256
    new_width: 256
    is_color: true
  }
}
layer {
  name: "data"
  type: "ImageData"
  top: "data"
  top: "label"
  include {
    phase: TEST
  }
  transform_param {
    mirror: false
    crop_size: 227
    mean_file: "data/ilsvrc12/imagenet_mean.binaryproto"
  }
  image_data_param {
    source: "/home/shangxuan/feature_YC/viper_filename_with_0.txt"
    batch_size: 1264
    new_height: 256
    new_width: 256
    is_color: true
  }
}
layer {
  name: "conv1"
  type: "Convolution"
  bottom: "data"
  top: "conv1"
  param {
    lr_mult: 1
    decay_mult: 1
  }
  param {
    lr_mult: 2
    decay_mult: 0
  }
  convolution_param {
    num_output: 96
    kernel_size: 11
    stride: 4
    weight_filler {
      type: "gaussian"
      std: 0.01
    }
    bias_filler {
      type: "constant"
      value: 0
    }
  }
}
layer {
  name: "relu1"
  type: "ReLU"
  bottom: "conv1"
  top: "conv1"
}
layer {
  name: "pool1"
  type: "Pooling"
  bottom: "conv1"
  top: "pool1"
  pooling_param {
    pool: MAX
    kernel_size: 3
    stride: 2
  }
}
layer {
  name: "norm1"
  type: "LRN"
  bottom: "pool1"
  top: "norm1"
  lrn_param {
    local_size: 5
    alpha: 0.0001
    beta: 0.75
  }
}
layer {
  name: "conv2"
  type: "Convolution"
  bottom: "norm1"
  top: "conv2"
  param {
    lr_mult: 1
    decay_mult: 1
  }
  param {
    lr_mult: 2
    decay_mult: 0
  }
  convolution_param {
    num_output: 256
    pad: 2
    kernel_size: 5
    group: 2
    weight_filler {
      type: "gaussian"
      std: 0.01
    }
    bias_filler {
      type: "constant"
      value: 1
    }
  }
}
layer {
  name: "relu2"
  type: "ReLU"
  bottom: "conv2"
  top: "conv2"
}
layer {
  name: "pool2"
  type: "Pooling"
  bottom: "conv2"
  top: "pool2"
  pooling_param {
    pool: MAX
    kernel_size: 3
    stride: 2
  }
}
layer {
  name: "norm2"
  type: "LRN"
  bottom: "pool2"
  top: "norm2"
  lrn_param {
    local_size: 5
    alpha: 0.0001
    beta: 0.75
  }
}
layer {
  name: "conv3"
  type: "Convolution"
  bottom: "norm2"
  top: "conv3"
  param {
    lr_mult: 1
    decay_mult: 1
  }
  param {
    lr_mult: 2
    decay_mult: 0
  }
  convolution_param {
    num_output: 384
    pad: 1
    kernel_size: 3
    weight_filler {
      type: "gaussian"
      std: 0.01
    }
    bias_filler {
      type: "constant"
      value: 0
    }
  }
}
layer {
  name: "relu3"
  type: "ReLU"
  bottom: "conv3"
  top: "conv3"
}
layer {
  name: "conv4"
  type: "Convolution"
  bottom: "conv3"
  top: "conv4"
  param {
    lr_mult: 1
    decay_mult: 1
  }
  param {
    lr_mult: 2
    decay_mult: 0
  }
  convolution_param {
    num_output: 384
    pad: 1
    kernel_size: 3
    group: 2
    weight_filler {
      type: "gaussian"
      std: 0.01
    }
    bias_filler {
      type: "constant"
      value: 1
    }
  }
}
layer {
  name: "relu4"
  type: "ReLU"
  bottom: "conv4"
  top: "conv4"
}
layer {
  name: "conv5"
  type: "Convolution"
  bottom: "conv4"
  top: "conv5"
  param {
    lr_mult: 1
    decay_mult: 1
  }
  param {
    lr_mult: 2
    decay_mult: 0
  }
  convolution_param {
    num_output: 256
    pad: 1
    kernel_size: 3
    group: 2
    weight_filler {
      type: "gaussian"
      std: 0.01
    }
    bias_filler {
      type: "constant"
      value: 1
    }
  }
}
layer {
  name: "relu5"
  type: "ReLU"
  bottom: "conv5"
  top: "conv5"
}
layer {
  name: "pool5"
  type: "Pooling"
  bottom: "conv5"
  top: "pool5"
  pooling_param {
    pool: MAX
    kernel_size: 3
    stride: 2
  }
}
layer {
  name: "fc6"
  type: "InnerProduct"
  bottom: "pool5"
  top: "fc6"
  param {
    lr_mult: 1
    decay_mult: 1
  }
  param {
    lr_mult: 2
    decay_mult: 0
  }
  inner_product_param {
    num_output: 4096
    weight_filler {
      type: "gaussian"
      std: 0.005
    }
    bias_filler {
      type: "constant"
      value: 1
    }
  }
}
layer {
  name: "relu6"
  type: "ReLU"
  bottom: "fc6"
  top: "fc6"
}
layer {
  name: "drop6"
  type: "Dropout"
  bottom: "fc6"
  top: "fc6"
  dropout_param {
    dropout_ratio: 0.5
  }
}
layer {
  name: "newImageData"
  type: "ImageData"
  top: "newImageData"
  top: "label1"
  include {
    phase: TRAIN
  }
image_data_param {
    source: "/home/shangxuan/feature_YC/train_filename_wrongfile1_nondeep_631.txt"
    batch_size: 10
#    new_height:8064
#    new_width:1
    is_color: false
  }
}
layer {
  name: "newImageData"
  type: "ImageData"
  top: "newImageData"
 top: "label1"
  include {
    phase: TEST
  }
image_data_param {
    source: "/home/shangxuan/feature_YC/viper_filename_with_0_nondeep.txt"
    batch_size: 1264
#    new_height:8064
#    new_width:1
    is_color: false
  }
}
layer{
   name: "newImageData_reshaped"
   type: "Reshape"
   bottom: "newImageData"
   top: "newImageData_reshaped"
   reshape_param{
     shape {
        dim: 0
        dim: -1
}
}
}
layer{
  name: "newImageData_reshaped_fced"
  type: "InnerProduct"
  bottom: "newImageData_reshaped"
  top:  "newImageData_reshaped_fced"
param {
    lr_mult: 10
    decay_mult: 1
  }
  param {
    lr_mult: 20
    decay_mult: 1
  }
  inner_product_param {
    num_output: 4096
    weight_filler {
      type: "gaussian"
      std: 0.005
    }
  }
}
layer {
  name: "relu_r"
  type: "ReLU"
  bottom: "newImageData_reshaped_fced"
  top: "newImageData_reshaped_fced"
}
layer {
  name: "drop_r"
  type: "Dropout"
  bottom: "newImageData_reshaped_fced"
  top: "newImageData_reshaped_fced"
  dropout_param {
    dropout_ratio: 0.5
  }
}


layer{
  name: "concat"
  type: "Concat"
  bottom: "fc6"
  bottom: "newImageData_reshaped_fced"
  top: "concat"
  concat_param{
	axis: 1
  }
}
layer {
  name: "fc7_new"
  type: "InnerProduct"
  bottom: "concat"
  top: "fc7_new"
  param {
    lr_mult: 10
    decay_mult: 1
  }
  param {
    lr_mult: 20
    decay_mult: 0
  }
  inner_product_param {
    num_output: 4096
    weight_filler {
      type: "gaussian"
      std: 0.005
    }
  }
}
layer {
  name: "relu7"
  type: "ReLU"
  bottom: "fc7_new"
  top: "fc7_new"
}
layer {
  name: "drop7"
  type: "Dropout"
  bottom: "fc7_new"
  top: "fc7_new"
  dropout_param {
    dropout_ratio: 0.5
  }
}
layer {
  name: "fc8_new"
  type: "InnerProduct"
  bottom: "fc7_new"
  top: "fc8_new"
  param {
    lr_mult: 10
    decay_mult: 1
  }
  param {
    lr_mult: 20
    decay_mult: 0
  }
  inner_product_param {
    num_output: 632
    weight_filler {
      type: "gaussian"
      std: 0.01
    }
  }
}
layer {
  name: "accuracy"
  type: "Accuracy"
  bottom: "fc8_new"
  bottom: "label"
  top: "accuracy"
  include {
    phase: TEST
  }
}
layer {
  name: "loss"
  type: "SoftmaxWithLoss"
  bottom: "fc8_new"
  bottom: "label"
  top: "loss"
  loss_weight: 1
}