Caffe trainval definition for VGGM_PM_PM-33L base model

VGGM_PM_PM-33L_trainval.prototxt

name: "SuitAppNet-Hash64"
layer {
  name: "data"
  type: "Data"
  top: "image"
  top: "label"
  include {
    phase: TRAIN
  }
  transform_param {
    mirror: true
    crop_size: 224
    mean_file: "image_mean.binaryproto"
  }
  data_param {
    source: "caffe_train_image_lmdb"
    batch_size: 128
    backend: LMDB
  }
}
layer {
  name: "data"
  type: "Data"
  top: "image"
  top: "label"
  include {
    phase: TEST
  }
  transform_param {
    mirror: false
    crop_size: 224
    mean_file: "image_mean.binaryproto"
  }
  data_param {
    source: "caffe_val_image_lmdb"
    batch_size: 32
    backend: LMDB
  }
}

layer {
  bottom: "image"
  top: "conv1"
  name: "conv1"
  type: "Convolution"
  param {
    lr_mult: 0
    decay_mult: 0
  }
  param {
    lr_mult: 0
    decay_mult: 0
  }
  convolution_param {
    num_output: 96
    kernel_size: 7
    stride: 2
    weight_filler {
      type: "gaussian"
      std: 0.01
    }
    bias_filler {
      type: "constant"
      value: 0
    }
  }
}
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: 0
    decay_mult: 0
  }
  param {
    lr_mult: 0
    decay_mult: 0
  }
  convolution_param {
    num_output: 256
    pad: 1
    kernel_size: 5
    stride: 2
    weight_filler {
      type: "gaussian"
      std: 0.01
    }
    bias_filler {
      type: "constant"
      value: 0
    }
  }
}
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
    weight_filler {
      type: "gaussian"
      std: 0.01
    }
    bias_filler {
      type: "constant"
      value: 0
    }
  }
}
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
    weight_filler {
      type: "gaussian"
      std: 0.01
    }
    bias_filler {
      type: "constant"
      value: 0
    }
  }
}
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
    weight_filler {
      type: "gaussian"
      std: 0.01
    }
    bias_filler {
      type: "constant"
      value: 0
    }
  }
}
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
    weight_filler {
      type: "gaussian"
      std: 0.005
    }
    bias_filler {
      type: "constant"
      value: 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"
  param {
    lr_mult: 1
    decay_mult: 1
  }
  param {
    lr_mult: 2
    decay_mult: 0
  }
  inner_product_param {
    num_output: 1024
    weight_filler {
      type: "xavier"
    }
    bias_filler {
      type: "constant"
      value: 0
    }
  }
}
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: "suitapp/encoding"
  name: "suitapp/encoding"
  type: "InnerProduct"
  param {
    lr_mult: 10
    decay_mult: 1
  }
  param {
    lr_mult: 20
    decay_mult: 0
  }
  inner_product_param {
    num_output: 64
    weight_filler {
      type: "xavier"
    }
    bias_filler {
      type: "constant"
      value: 0
    }
  }
}
layer {
  bottom: "suitapp/encoding"
  top: "fc8/suitapp"
  name: "fc8/suitapp"
  type: "InnerProduct"
  param {
    lr_mult: 10
    decay_mult: 1
  }
  param {
    lr_mult: 20
    decay_mult: 0
  }
  inner_product_param {
    num_output: 33
    weight_filler {
      type: "xavier"
    }
    bias_filler {
      type: "constant"
      value: 0
    }
  }
}

layer {
  name: "accuracy/suitapp"
  type: "Accuracy"
  bottom: "fc8/suitapp"
  bottom: "label"
  top: "accuracy/suitapp"
  include {
    phase: TEST
  }
}
layer {
  name: "accuracy/top-3/suitapp"
  type: "Accuracy"
  bottom: "fc8/suitapp"
  bottom: "label"
  top: "accuracy/top-3/suitapp"
  include {
    phase: TEST
  }
  accuracy_param {
    top_k: 3
  }
}
layer {
  name: "loss/suitapp"
  type: "SoftmaxWithLoss"
  bottom: "fc8/suitapp"
  bottom: "label"
  top: "loss/suitapp"
}