net_cifar_alexnet.prototxt

name: "AlexNet"

layer {
  name: "data"
  type: "ImageData"
  top: "data"
  top: "label"
  image_data_param {
    source: "cifar_train_a.txt"
    batch_size: 50
    new_height: 227
    new_width: 227
  }
  transform_param {
    crop_size: 227
    mean_value: 104
    mean_value: 117
    mean_value: 123
  }
  include: { phase: TRAIN }
}
layer {
  name: "data_p"
  type: "ImageData"
  top: "data_p"
  top: "label_p"
  image_data_param {
    source: "cifar_train_b.txt"
    batch_size: 50
    new_height: 227
    new_width: 227
  }
  transform_param {
    crop_size: 227
    mean_value: 104
    mean_value: 117
    mean_value: 123
  }
  include: { phase: TRAIN }
}

layer {
    name: "silence_p"
    type: "Silence"
    bottom: "label_p"
}
layer {
  name: "conv1"
  type: "Convolution"
  bottom: "data"
  top: "conv1"
  param {
    name: "conv1_w"
    lr_mult: 1
    decay_mult: 1
  }
  param {
    name: "conv1_b"
    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: "norm1"
  type: "LRN"
  bottom: "conv1"
  top: "norm1"
  lrn_param {
    local_size: 5
    alpha: 0.0001
    beta: 0.75
  }
}
layer {
  name: "pool1"
  type: "Pooling"
  bottom: "norm1"
  top: "pool1"
  pooling_param {
    pool: MAX
    kernel_size: 3
    stride: 2
  }
}
layer {
  name: "conv2"
  type: "Convolution"
  bottom: "pool1"
  top: "conv2"
  param {
    name: "conv2_w"
    lr_mult: 1
    decay_mult: 1
  }
  param {
    name: "conv2_b"
    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: 0.1
    }
  }
}
layer {
  name: "relu2"
  type: "ReLU"
  bottom: "conv2"
  top: "conv2"
}
layer {
  name: "norm2"
  type: "LRN"
  bottom: "conv2"
  top: "norm2"
  lrn_param {
    local_size: 5
    alpha: 0.0001
    beta: 0.75
  }
}
layer {
  name: "pool2"
  type: "Pooling"
  bottom: "norm2"
  top: "pool2"
  pooling_param {
    pool: MAX
    kernel_size: 3
    stride: 2
  }
}
layer {
  name: "conv3"
  type: "Convolution"
  bottom: "pool2"
  top: "conv3"
  param {
    name: "conv3_w"
    lr_mult: 1
    decay_mult: 1
  }
  param {
    name: "conv3_b"
    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 {
    name: "conv4_w"
    lr_mult: 1
    decay_mult: 1
  }
  param {
    name: "conv4_b"
    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: 0.1
    }
  }
}
layer {
  name: "relu4"
  type: "ReLU"
  bottom: "conv4"
  top: "conv4"
}
layer {
  name: "conv5"
  type: "Convolution"
  bottom: "conv4"
  top: "conv5"
  param {
    name: "conv5_w"
    lr_mult: 1
    decay_mult: 1
  }
  param {
    name: "conv5_b"
    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: 0.1
    }
  }
}
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 {
    name: "fc6_w"
    lr_mult: 1
    decay_mult: 1
  }
  param {
    name: "fc6_b"
    lr_mult: 2
    decay_mult: 0
  }
  inner_product_param {
    num_output: 4096
    weight_filler {
      type: "gaussian"
      std: 0.005
    }
    bias_filler {
      type: "constant"
      value: 0.1
    }
  }
}
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: "fc7"
  type: "InnerProduct"
  bottom: "fc6"
  top: "fc7"
  param {
    name: "fc7_w"
    lr_mult: 1
    decay_mult: 1
  }
  param {
    name: "fc7_b"
    lr_mult: 2
    decay_mult: 0
  }
  inner_product_param {
    num_output: 4096
    weight_filler {
      type: "gaussian"
      std: 0.005
    }
    bias_filler {
      type: "constant"
      value: 0.1
    }
  }
}
layer {
  name: "relu7"
  type: "ReLU"
  bottom: "fc7"
  top: "fc7"
}
layer {
  name: "drop7"
  type: "Dropout"
  bottom: "fc7"
  top: "fc7"
  dropout_param {
    dropout_ratio: 0.5
  }
}

layer {
  name: "conv1_p"
  type: "Convolution"
  bottom: "data_p"
  top: "conv1_p"
  param {
    name: "conv1_w"
    lr_mult: 1
    decay_mult: 1
  }
  param {
    name: "conv1_b"
    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_p"
  type: "ReLU"
  bottom: "conv1_p"
  top: "conv1_p"
}
layer {
  name: "norm1_p"
  type: "LRN"
  bottom: "conv1_p"
  top: "norm1_p"
  lrn_param {
    local_size: 5
    alpha: 0.0001
    beta: 0.75
  }
}
layer {
  name: "pool1_p"
  type: "Pooling"
  bottom: "norm1_p"
  top: "pool1_p"
  pooling_param {
    pool: MAX
    kernel_size: 3
    stride: 2
  }
}
layer {
  name: "conv2_p"
  type: "Convolution"
  bottom: "pool1_p"
  top: "conv2_p"
  param {
    name: "conv2_w"
    lr_mult: 1
    decay_mult: 1
  }
  param {
    name: "conv2_b"
    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: 0.1
    }
  }
}
layer {
  name: "relu2_p"
  type: "ReLU"
  bottom: "conv2_p"
  top: "conv2_p"
}
layer {
  name: "norm2_p"
  type: "LRN"
  bottom: "conv2_p"
  top: "norm2_p"
  lrn_param {
    local_size: 5
    alpha: 0.0001
    beta: 0.75
  }
}
layer {
  name: "pool2_p"
  type: "Pooling"
  bottom: "norm2_p"
  top: "pool2_p"
  pooling_param {
    pool: MAX
    kernel_size: 3
    stride: 2
  }
}
layer {
  name: "conv3_p"
  type: "Convolution"
  bottom: "pool2_p"
  top: "conv3_p"
  param {
    name: "conv3_w"
    lr_mult: 1
    decay_mult: 1
  }
  param {
    name: "conv3_b"
    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_p"
  type: "ReLU"
  bottom: "conv3_p"
  top: "conv3_p"
}
layer {
  name: "conv4_p"
  type: "Convolution"
  bottom: "conv3_p"
  top: "conv4_p"
  param {
    name: "conv4_w"
    lr_mult: 1
    decay_mult: 1
  }
  param {
    name: "conv4_b"
    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: 0.1
    }
  }
}
layer {
  name: "relu4_p"
  type: "ReLU"
  bottom: "conv4_p"
  top: "conv4_p"
}
layer {
  name: "conv5_p"
  type: "Convolution"
  bottom: "conv4_p"
  top: "conv5_p"
  param {
    name: "conv5_w"
    lr_mult: 1
    decay_mult: 1
  }
  param {
    name: "conv5_b"
    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: 0.1
    }
  }
}
layer {
  name: "relu5_p"
  type: "ReLU"
  bottom: "conv5_p"
  top: "conv5_p"
}
layer {
  name: "pool5_p"
  type: "Pooling"
  bottom: "conv5_p"
  top: "pool5_p"
  pooling_param {
    pool: MAX
    kernel_size: 3
    stride: 2
  }
}
layer {
  name: "fc6_p"
  type: "InnerProduct"
  bottom: "pool5_p"
  top: "fc6_p"
  param {
    name: "fc6_w"
    lr_mult: 1
    decay_mult: 1
  }
  param {
    name: "fc6_b"
    lr_mult: 2
    decay_mult: 0
  }
  inner_product_param {
    num_output: 4096
    weight_filler {
      type: "gaussian"
      std: 0.005
    }
    bias_filler {
      type: "constant"
      value: 0.1
    }
  }
}
layer {
  name: "relu6_p"
  type: "ReLU"
  bottom: "fc6_p"
  top: "fc6_p"
}
layer {
  name: "drop6_p"
  type: "Dropout"
  bottom: "fc6_p"
  top: "fc6_p"
  dropout_param {
    dropout_ratio: 0.5
  }
}
layer {
  name: "fc7_p"
  type: "InnerProduct"
  bottom: "fc6_p"
  top: "fc7_p"
  param {
    name: "fc7_w"
    lr_mult: 1
    decay_mult: 1
  }
  param {
    name: "fc7_b"
    lr_mult: 2
    decay_mult: 0
  }
  inner_product_param {
    num_output: 4096
    weight_filler {
      type: "gaussian"
      std: 0.005
    }
    bias_filler {
      type: "constant"
      value: 0.1
    }
  }
}
layer {
  name: "relu7_p"
  type: "ReLU"
  bottom: "fc7_p"
  top: "fc7_p"
}
layer {
  name: "drop7_p"
  type: "Dropout"
  bottom: "fc7_p"
  top: "fc7_p"
  dropout_param {
    dropout_ratio: 0.5
  }
}

layer {
  name: "fc7_concat"
  bottom: "fc7"
  bottom: "fc7_p"
  top: "fc7_concat"
  type: "Concat"
  concat_param {
    axis: 1
  }
}

layer {
  name: "fc8_cifar"
  type: "InnerProduct"
  bottom: "fc7_concat"
  top: "fc8_cifar"
  param {
    name: "fc8_cifar_w"
    lr_mult: 5
    decay_mult: 1
  }
  param { 
    name: "fc8_cifar_b"
    lr_mult: 10
    decay_mult: 0
  }
  inner_product_param {
    num_output: 64
    weight_filler {
      type: "gaussian"
      std: 0.01
    }
    bias_filler {
      type: "constant"
      value: 0
    }
  }
}
layer {
  name: "relu8_p"
  type: "ReLU"
  bottom: "fc8_cifar"
  top: "fc8_cifar"
}

layer {
  name: "fc9_cifar"
  type: "InnerProduct"
  bottom: "fc8_cifar"
  top: "fc9_cifar"
  param {
    lr_mult: 5
    decay_mult: 1
  }
  param { 
    lr_mult: 10
    decay_mult: 0
  }
  inner_product_param {
    num_output: 2
    weight_filler {
      type: "gaussian"
      std: 0.01
    }
    bias_filler {
      type: "constant"
      value: 0
    }
  }
}

layer {
  name: "loss"
  type: "SoftmaxWithLoss"
  bottom: "fc9_cifar"
  bottom: "label"
  top: "loss"
}