ICIP2016_Net_CV_4.prototxt

name: "Deep_Crack_Detection_CV_4"
layers{
  name: "ICIP2016"
  type: DATA
  top:"data"
  top:"label"
  data_param{
    source: "2016_TITS_DNN_train_lmdb"
    #source: "train_fan"
    scale: 0.00390625
    batch_size: 48 
    backend: LMDB
  }
  include: { phase: TRAIN }
}
layers{
  name: "ICIP2016"
  type: DATA
  top:"data"
  top:"label"
  data_param{
    #source: "2016_TITS_DNN_validation_lmdb"
    source: "auto_test_data_set_lmdb"
    scale: 0.00390625
    batch_size: 48
    backend: LMDB
  }
  include: { phase: TEST }
}

layers {
  name: "conv1"
  type: CONVOLUTION
  bottom: "data"
  top: "conv1"
  blobs_lr: 1
  blobs_lr: 2
  convolution_param {
    num_output: 48
    kernel_size: 4
    stride: 1
    weight_filler {
      type: "xavier"
    }
    bias_filler {
      type: "constant"
    }
  }
}
layers {
  name: "relu_c_1"
  type: RELU
  bottom: "conv1"
  top: "conv1"
}
layers {
  name: "pool1"
  type: POOLING
  bottom: "conv1"
  top: "pool1"
  pooling_param {
    pool: MAX
    kernel_size: 2
    stride: 2
  }
}
layers {
  name: "conv2"
  type: CONVOLUTION
  bottom: "pool1"
  top: "conv2"
  blobs_lr: 1
  blobs_lr: 2
  convolution_param {
    num_output: 48
    kernel_size: 5
    stride: 1
    weight_filler {
      type: "xavier"
    }
    bias_filler {
      type: "constant"
    }
  }
}
layers {
  name: "relu_c_2"
  type: RELU
  bottom: "conv2"
  top: "conv2"
}
layers {
  name: "pool2"
  type: POOLING
  bottom: "conv2"
  top: "pool2"
  pooling_param {
    pool: MAX
    kernel_size: 2
    stride: 2
  }
}

layers {
  name: "conv3"
  type: CONVOLUTION
  bottom: "pool2"
  top: "conv3"
  blobs_lr: 1
  blobs_lr: 2
  convolution_param {
    num_output: 48
    kernel_size: 3
    stride: 1
    weight_filler {
      type: "xavier"
    }
    bias_filler {
      type: "constant"
    }
  }
}
layers {
  name: "relu_c_3"
  type: RELU
  bottom: "conv3"
  top: "conv3"
}
layers {
  name: "pool3"
  type: POOLING
  bottom: "conv3"
  top: "pool3"
  pooling_param {
    pool: MAX
    kernel_size: 2
    stride: 2
  }
}
layers {
  name: "conv4"
  type: CONVOLUTION
  bottom: "pool3"
  top: "conv4"
  blobs_lr: 1
  blobs_lr: 2
  convolution_param {
    num_output: 48
    kernel_size: 4
    stride: 1
    weight_filler {
      type: "xavier"
    }
    bias_filler {
      type: "constant"
    }
  }
}
layers {
  name: "relu_c_4"
  type: RELU
  bottom: "conv4"
  top: "conv4"
}

layers {
  name: "pool4"
  type: POOLING
  bottom: "conv4"
  top: "pool4"
  pooling_param {
    pool: MAX
    kernel_size: 2
    stride: 2
  }
}

layers {
  name: "ip1"
  type: INNER_PRODUCT
  bottom: "pool4"
  top: "ip1"
  blobs_lr: 1
  blobs_lr: 2
  inner_product_param {
    num_output: 200
    weight_filler {
      type: "xavier"
    }
    bias_filler {
      type: "constant"
    }
  }
}
layers {
  name: "relu1"
  type: RELU
  bottom: "ip1"
  top: "ip1"
}
layer {
  name: "drop1"
  type: "Dropout"
  bottom: "ip1"
  top: "ip1"
  dropout_param {
    dropout_ratio: 0.5
  }
}
layers {
  name: "ip2"
  type: INNER_PRODUCT
  bottom: "ip1"
  top: "ip2"
  blobs_lr: 1
  blobs_lr: 2
  inner_product_param {
    num_output: 2
    weight_filler {
      type: "xavier"
    }
    bias_filler {
      type: "constant"
    }
  }
}
layers {
  name: "accuracy"
  type: ACCURACY
  bottom: "ip2"
  bottom: "label"
  top: "accuracy"
  include: { phase: TEST }
}
layers {
  name: "loss"
  type: SOFTMAX_LOSS
  bottom: "ip2"
  bottom: "label"
  top: "loss"
}