lmbd_convnet.prototxt~

name: "lmbd-solver"
layers {
  name: "lmbd_input"
  type: DATA
  top: "data"
  top: "label"
  data_param {
    source: "examples/science_bowl/science_bowl_train_lmdb"
    batch_size: 256
    backend: LMDB
  }
  transform_param {
    mirror:1
    crop_size:69
    mean_file: "data/competition_data/science_bowl.binaryproto"
    scale: 0.00390625
    
    
  }
  include: { phase: TRAIN }
}
layers {
  name: "lmbd_input"
  type: DATA
  top: "data"
  top: "label"
  data_param {
    source: "examples/science_bowl/science_bowl_val_lmdb"
    batch_size: 256
    backend: LMDB
  }
  transform_param {
    crop_size:69
    mirror:0
    mean_file: "data/competition_data/science_bowl.binaryproto"
    scale: 0.00390625
    
  }
  include: { phase: TEST }
}

layers {
  name: "conv0"
  type: CONVOLUTION
  bottom: "data"
  top: "conv0"
  blobs_lr: 1
  blobs_lr: 2
  convolution_param {
    num_output: 64
    kernel_size: 8
    stride: 1
    weight_filler {
      type: "xavier"
    }
    bias_filler {
      type: "constant"
    }
  }
}
layers {
  name: "relu0"
  type: RELU
  bottom: "conv0"
  top: "conv0"
}
layers {
  name: "pool0"
  type: POOLING
  bottom: "conv0"
  top: "pool0"
  pooling_param {
    pool: MAX
    kernel_size: 4
    stride: 2
  }
}

layers {
  name: "conv1"
  type: CONVOLUTION
  bottom: "pool0"
  top: "conv1"
  blobs_lr: 1
  blobs_lr: 2
  convolution_param {
    num_output: 128
    kernel_size: 6
    stride: 1
    weight_filler {
      type: "xavier"
    }
    bias_filler {
      type: "constant"
    }
  }
}
layers {
  name: "relu1"
  type: RELU
  bottom: "conv1"
  top: "conv1"
}
layers {
  name: "pool2"
  type: POOLING
  bottom: "conv1"
  top: "pool2"
  pooling_param {
    pool: MAX
    kernel_size: 4
    stride: 2
  }
}
layers {
  name: "norm1"
  type: LRN
  bottom: "pool2"
  top: "norm1"
  lrn_param {
    local_size: 5
    alpha: 0.0001
    beta: 0.75
  }
}
layers {
  name: "conv2"
  type: CONVOLUTION
  bottom: "norm1"
  top: "conv2"
  blobs_lr: 1
  blobs_lr: 2
  convolution_param {
    num_output: 256
    kernel_size: 5
    stride: 1
    weight_filler {
      type: "xavier"
    }
    bias_filler {
      type: "constant"
    }
  }
}
layers {
  name: "relu2"
  type: RELU
  bottom: "conv2"
  top: "conv2"
}
layers {
  name: "drop1"
  type: DROPOUT
  bottom: "conv2"
  top: "conv2"
  dropout_param {
    dropout_ratio: 0.2
  }
}
layers {
  name: "conv3"
  type: CONVOLUTION
  bottom: "conv2"
  top: "conv3"
  blobs_lr: 1
  blobs_lr: 2
  convolution_param {
    num_output: 512
    kernel_size: 5
    stride: 1
    weight_filler {
      type: "xavier"
    }
    bias_filler {
      type: "constant"
    }
  }
}

layers {
  name: "relu3"
  type: RELU
  bottom: "conv3"
  top: "conv3"
}
layers {
  name: "drop2"
  type: DROPOUT
  bottom: "conv3"
  top: "conv3"
  dropout_param {
    dropout_ratio: 0.3
  }
}
layers {
  name: "pool3"
  type: POOLING
  bottom: "conv3"
  top: "pool3"
  pooling_param {
    pool: MAX
    kernel_size: 4
    stride: 2
  }
}
layers {
  name: "norm2"
  type: LRN
  bottom: "pool3"
  top: "norm2"
  lrn_param {
    local_size: 5
    alpha: 0.0001
    beta: 0.75
  }
}
layers {
  name: "ip1"
  type: INNER_PRODUCT
  bottom: "norm2"
  top: "ip1"
  blobs_lr: 1
  blobs_lr: 2
  inner_product_param {
    num_output: 4096
    weight_filler {
      type: "xavier"
    }
    bias_filler {
      type: "constant"
    }
  }
}
layers {
  name: "relu1"
  type: RELU
  bottom: "ip1"
  top: "ip1"
}
layers {
  name: "drop7"
  type: DROPOUT
  bottom: "ip1"
  top: "ip1"
  dropout_param {
    dropout_ratio: 0.5
  }
}

layers {
  name: "ip3"
  type: INNER_PRODUCT
  bottom: "ip1"
  top: "ip3"
  blobs_lr: 1
  blobs_lr: 2
  inner_product_param {
    num_output: 121
    weight_filler {
      type: "xavier"
    }
    bias_filler {
      type: "constant"
    }
  }
}
layers {
  name: "accuracy"
  type: ACCURACY
  bottom: "ip3"
  bottom: "label"
  top: "accuracy"
  include: { phase: TEST }
}
layers {
  name: "loss"
  type: SOFTMAX_LOSS
  bottom: "ip3"
  bottom: "label"
  top: "loss"
}